Quiz IT certification exam

Question 1

Your company built a TensorFlow neural-network model with a large number of neurons and layers.

The model fits well for the training dat a. However, when tested against new data, it performs poorly. What method can you employ to address this?

Accepted Answer

Dropout Methods

Answer

Threading

Answer

Serialization

Answer

Dimensionality Reduction

Question 2

You are building a model to make clothing recommendations. You know a user's fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available. How should you use this data to train the model?

Accepted Answer

Train on the existing data while using the new data as your test set.

Answer

Continuously retrain the model on just the new data.

Answer

Continuously retrain the model on a combination of existing data and the new data.

Answer

Train on the new data while using the existing data as your test set.

Question 3

You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports.The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records.You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

Accepted Answer

Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.

Answer

Add capacity (memory and disk space) to the database server by the order of 200.

Answer

Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.

Answer

Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.

Question 4

You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old. What should you do?

Accepted Answer

Disable caching by editing the report settings.

Answer

Disable caching in BigQuery by editing table details.

Answer

Refresh your browser tab showing the visualizations.

Answer

Clear your browser history for the past hour then reload the tab showing the virtualizations.

Question 5

An external customer provides you with a daily dump of data from their database. The data flows into Google Cloud Storage GCS as comma-separated values (CSV) files. You want to analyze this data in Google BigQuery, but the data could have rows that are formatted incorrectly or corrupted. How should you build this pipeline?

Accepted Answer

Run a Google Cloud Dataflow batch pipeline to import the data into BigQuery, and push errors to another dead-letter table for analysis.

Answer

Use federated data sources, and check data in the SQL query.

Answer

Enable BigQuery monitoring in Google Stackdriver and create an alert.

Answer

Import the data into BigQuery using the gcloud CLI and set max_bad_records to 0.

Question 6

Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

Accepted Answer

Retry the query with exponential backoff, up to a cap of 15 minutes.

Answer

Issue a command to restart the database servers.

Answer

Retry the query every second until it comes back online to minimize staleness of data.

Answer

Reduce the query frequency to once every hour until the database comes back online.

Question 7

You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?

Accepted Answer

Linear regression

Answer

Logistic classification

Answer

Recurrent neural network

Answer

Feedforward neural network

Question 8

You are building new real-time data warehouse for your company and will use Google BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying dat a. Which query type should you use?

Accepted Answer

Use the ROW_NUMBER window function with PARTITION by unique ID along with WHERE row equals 1.

Answer

Include ORDER BY DESK on timestamp column and LIMIT to 1.

Answer

Use GROUP BY on the unique ID column and timestamp column and SUM on the values.

Answer

Use the LAG window function with PARTITION by unique ID along with WHERE LAG IS NOT NULL.

Question 9

Your company is using WHILECARD tables to query data across multiple tables with similar names.The SQL statement is currently failing with the following error:# Syntax error : Expected end of statement but got "-" at [4:11]SELECT ageFROM bigquery-public-data.noaa_gsod.gsod WHERE age != 99 AND_TABLE_SUFFIX = '1929' ORDER BY age DESC Which table name will make the SQL statement work correctly?

Accepted Answer

'bigquery-public-data.noaa_gsod.gsod*`

Answer

'bigquery-public-data.noaa_gsod.gsod'

Answer

bigquery-public-data.noaa_gsod.gsod*

Answer

'bigquery-public-data.noaa_gsod.gsod'*

Question 10

Your company is in a highly regulated industry. One of your requirements is to ensure individual users have access only to the minimum amount of information required to do their jobs. You want to enforce this requirement with GoogleBigQuery. Which three approaches can you take? (Choose three.)

Accepted Answer

Restrict access to tables by role.

Accepted Answer

Restrict BigQuery API access to approved users.

Accepted Answer

Use Google Stackdriver Audit Logging to determine policy violations.

Answer

Disable writes to certain tables.

Answer

Ensure that the data is encrypted at all times.

Answer

Segregate data across multiple tables or databases.

Question 11

You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:No interaction by the user on the site for 1 hourHas added more than $30 worth of products to the basketHas not completed a transactionYou use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?

Accepted Answer

Use a session window with a gap time duration of 60 minutes.

Answer

Use a fixed-time window with a duration of 60 minutes.

Answer

Use a sliding time window with a duration of 60 minutes.

Answer

Use a global window with a time based trigger with a delay of 60 minutes.

Question 12

Your company handles data processing for a number of different clients. Each client prefers to use their own suite of analytics tools, with some allowing direct query access via Google BigQuery. You need to secure the data so that clients cannot see each other's dat a. You want to ensure appropriate access to the data. Which three steps should you take? (Choose three.)

Accepted Answer

Load data into a different dataset for each client.

Accepted Answer

Restrict a client's dataset to approved users.

Accepted Answer

Use the appropriate identity and access management (IAM) roles for each client's users.

Answer

Load data into different partitions.

Answer

Put each client's BigQuery dataset into a different table.

Answer

Only allow a service account to access the datasets.

Question 13

You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.Which Google database service should you use?

Accepted Answer

Cloud SQL

Answer

BigQuery

Answer

Cloud Bigtable

Answer

Cloud Datastore

Question 14

You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)

Accepted Answer

There are very few occurrences of mutations relative to normal samples.

Accepted Answer

You expect future mutations to have similar features to the mutated samples in the database.

Answer

There are roughly equal occurrences of both normal and mutated samples in the database.

Answer

You expect future mutations to have different features from the mutated samples in the database.

Answer

You already have labels for which samples are mutated and which are normal in the database.

Question 15

You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight dat a. How can you adjust your application design?

Accepted Answer

Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.

Answer

Re-write the application to load accumulated data every 2 minutes.

Answer

Convert the streaming insert code to batch load for individual messages.

Answer

Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.

Question 16

Your startup has never implemented a formal security policy. Currently, everyone in the company has access to the datasets stored in Google BigQuery. Teams have freedom to use the service as they see fit, and they have not documented their use cases. You have been asked to secure the data warehouse. You need to discover what everyone is doing. What should you do first?

Accepted Answer

Use Google Stackdriver Audit Logs to review data access.

Answer

Get the identity and access management IIAM) policy of each table

Answer

Use Stackdriver Monitoring to see the usage of BigQuery query slots.

Answer

Use the Google Cloud Billing API to see what account the warehouse is being billed to.

Question 17

Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?

Accepted Answer

Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.

Answer

Create a Google Cloud Dataflow job to process the data.

Answer

Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.

Answer

Create a Hadoop cluster on Google Compute Engine that uses persistent disks.

Answer

Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.

Question 18

Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the dat a. Which three machine learning applications can you use? (Choose three.)

Accepted Answer

Unsupervised learning to determine which transactions are most likely to be fraudulent.

Accepted Answer

Clustering to divide the transactions into N categories based on feature similarity.

Accepted Answer

Supervised learning to predict the location of a transaction.

Answer

Supervised learning to determine which transactions are most likely to be fraudulent.

Answer

Reinforcement learning to predict the location of a transaction.

Answer

Unsupervised learning to predict the location of a transaction.

Question 19

Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do?

Accepted Answer

Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.

Answer

Put the data into Google Cloud Storage.

Answer

Tune the Cloud Dataproc cluster so that there is just enough disk for all data.

Answer

Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.

Question 20

You work for a car manufacturer and have set up a data pipeline using Google Cloud Pub/Sub to capture anomalous sensor events. You are using a push subscription in Cloud Pub/Sub that calls a custom HTTPS endpoint that you have created to take action of these anomalous events as they occur. Your custom HTTPS endpoint keeps getting an inordinate amount of duplicate messages. What is the most likely cause of these duplicate messages?

Accepted Answer

Your custom endpoint has an out-of-date SSL certificate.

Answer

The message body for the sensor event is too large.

Answer

The Cloud Pub/Sub topic has too many messages published to it.

Answer

Your custom endpoint is not acknowledging messages within the acknowledgement deadline.

Question 21

Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If there are any concerns about a transmission, the system re-transmits the dat a. How should you deduplicate the data most efficiency?

Accepted Answer

Maintain a database table to store the hash value and other metadata for each data entry.

Answer

Assign global unique identifiers (GUID) to each data entry.

Answer

Compute the hash value of each data entry, and compare it with all historical data.

Answer

Store each data entry as the primary key in a separate database and apply an index.

Question 22

Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine.The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks. She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks. What should you do?

Accepted Answer

Grant the user access to Google Cloud Shell.

Answer

Run a local version of Jupiter on the laptop.

Answer

Host a visualization tool on a VM on Google Compute Engine.

Answer

Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.

Question 23

You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time.What should you do?

Accepted Answer

Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.

Answer

Send the data to Google Cloud Datastore and then export to BigQuery.

Answer

Send the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required.

Answer

Export logs in batch to Google Cloud Storage and then spin up a Google Cloud SQL instance, import the data from Cloud Storage, and run an analysis as needed.

Question 24

You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

Accepted Answer

Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, reference the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.

Answer

Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.

Answer

Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. Reference the column TS instead of the column DT from now on.

Answer

Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. Reference the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.

Answer

Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. Reference the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.

Question 25

You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?

Accepted Answer

In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.

Answer

Make a call to the Stackdriver API to list all logs, and apply an advanced filter.

Answer

In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.

Answer

Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.

Question 26

You are working on a sensitive project involving private user dat a. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in aGoogle Cloud Dataflow pipeline for your project. How should you maintain users' privacy?

Accepted Answer

Create a service account and allow the consultant to log on with it.

Answer

Grant the consultant the Viewer role on the project.

Answer

Grant the consultant the Cloud Dataflow Developer role on the project.

Answer

Create an anonymized sample of the data for the consultant to work with in a different project.

Question 27

You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

Accepted Answer

Combine highly co-dependent features into one representative feature.

Answer

Eliminate features that are highly correlated to the output labels.

Answer

Instead of feeding in each feature individually, average their values in batches of 3.

Answer

Remove the features that have null values for more than 50% of the training records.

Question 28

Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow.Numerous data logs are being are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour.The data scientists have written the following code to read the data for a new key features in the logs.BigQueryIO.Read.named("ReadLogData").from("clouddataflow-readonly:samples.log_data")You want to improve the performance of this data read. What should you do?

Accepted Answer

Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.

Answer

Specify the TableReference object in the code.

Answer

Use .fromQuery operation to read specific fields from the table.

Answer

Use of both the Google BigQuery TableSchema and TableFieldSchema classes.

Question 29

Your company is streaming real-time sensor data from their factory floor into Bigtable and they have noticed extremely poor performance. How should the row key be redesigned to improve Bigtable performance on queries that populate real-time dashboards?

Accepted Answer

Use a row key of the form <timestamp>.

Answer

Use a row key of the form <sensorid>.

Answer

Use a row key of the form #.

Answer

Use a row key of the form >##.

Question 30

Your company's customer and order databases are often under heavy load. This makes performing analytics against them difficult without harming operations. The databases are in a MySQL cluster, with nightly backups taken using mysqldump. You want to perform analytics with minimal impact on operations. What should you do?

Accepted Answer

Connect an on-premises Apache Hadoop cluster to MySQL and perform ETL.

Answer

Add a node to the MySQL cluster and build an OLAP cube there.

Answer

Use an ETL tool to load the data from MySQL into Google BigQuery.

Answer

Mount the backups to Google Cloud SQL, and then process the data using Google Cloud Dataproc.

Question 31

You have Google Cloud Dataflow streaming pipeline running with a Google Cloud Pub/Sub subscription as the source. You need to make an update to the code that will make the new Cloud Dataflow pipeline incompatible with the current version. You do not want to lose any data when making this update. What should you do?

Accepted Answer

Create a new pipeline that has a new Cloud Pub/Sub subscription and cancel the old pipeline.

Answer

Update the current pipeline and use the drain flag.

Answer

Update the current pipeline and provide the transform mapping JSON object.

Answer

Create a new pipeline that has the same Cloud Pub/Sub subscription and cancel the old pipeline.

Question 32

Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of dat a. They want to improve this performance while minimizing cost. What should they do?

Accepted Answer

Redefine the schema by evenly distributing reads and writes across the row space of the table.

Answer

The performance issue should be resolved over time as the site of the BigDate cluster is increased.

Answer

Redesign the schema to use a single row key to identify values that need to be updated frequently in the cluster.

Answer

Redesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers.

Question 33

Your software uses a simple JSON format for all messages. These messages are published to Google Cloud Pub/Sub, then processed with Google Cloud Dataflow to create a real-time dashboard for the CFO. During testing, you notice that some messages are missing in the dashboard. You check the logs, and all messages are being published to Cloud Pub/Sub successfully. What should you do next?

Accepted Answer

Run a fixed dataset through the Cloud Dataflow pipeline and analyze the output.

Answer

Check the dashboard application to see if it is not displaying correctly.

Answer

Use Google Stackdriver Monitoring on Cloud Pub/Sub to find the missing messages.

Answer

Switch Cloud Dataflow to pull messages from Cloud Pub/Sub instead of Cloud Pub/Sub pushing messages to Cloud Dataflow.

Question 34

Company OverviewFlowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.Company BackgroundThe company started as a regional trucking company, and then expanded into other logistics market.Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.Solution ConceptFlowlogistic wants to implement two concepts using the cloud:Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.Existing Technical EnvironmentFlowlogistic architecture resides in a single data center:Databases8 physical servers in 2 clustersSQL Server ñ user data, inventory, static data3 physical serversCassandra ñ metadata, tracking messages10 Kafka servers ñ tracking message aggregation and batch insertApplication servers ñ customer front end, middleware for order/customs 60 virtual machines across 20 physical servers Tomcat ñ Java services Nginx ñ static content Batch servers Storage appliances iSCSI for virtual machine (VM) hosts Fibre Channel storage area network (FC SAN) ñ SQL server storage Network-attached storage (NAS) image storage, logs, backups Apache Hadoop /Spark servers Core Data Lake Data analysis workloads 20 miscellaneous serversJenkins, monitoring, bastion hosts, Business Requirements Build a reliable and reproducible environment with scaled panty of production.Aggregate data in a centralized Data Lake for analysisUse historical data to perform predictive analytics on future shipments Accurately track every shipment worldwide using proprietary technology Improve business agility and speed of innovation through rapid provisioning of new resources Analyze and optimize architecture for performance in the cloud Migrate fully to the cloud if all other requirements are met Technical Requirements Handle both streaming and batch data Migrate existing Hadoop workloads Ensure architecture is scalable and elastic to meet the changing demands of the company.Use managed services whenever possibleEncrypt data flight and at restConnect a VPN between the production data center and cloud environment SEO Statement We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.We need to organize our information so we can more easily understand where our customers are and what they are shipping.CTO StatementIT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.CFO StatementPart of our competitive advantage is that we penalize ourselves for late shipments and deliveries.Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?

Accepted Answer

Store the common data in BigQuery and expose authorized views.

Answer

Store the common data in BigQuery as partitioned tables.

Answer

Store the common data encoded as Avro in Google Cloud Storage.

Answer

Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.

Question 35

Company OverviewFlowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.Company BackgroundThe company started as a regional trucking company, and then expanded into other logistics market.Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.Solution ConceptFlowlogistic wants to implement two concepts using the cloud:Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.Existing Technical EnvironmentFlowlogistic architecture resides in a single data center:Databases8 physical servers in 2 clustersSQL Server ñ user data, inventory, static data3 physical serversCassandra ñ metadata, tracking messages10 Kafka servers ñ tracking message aggregation and batch insertApplication servers ñ customer front end, middleware for order/customs 60 virtual machines across 20 physical servers Tomcat ñ Java services Nginx ñ static content Batch servers Storage appliances iSCSI for virtual machine (VM) hosts Fibre Channel storage area network (FC SAN) ñ SQL server storage Network-attached storage (NAS) image storage, logs, backups Apache Hadoop /Spark servers Core Data Lake Data analysis workloads 20 miscellaneous serversJenkins, monitoring, bastion hosts, Business Requirements Build a reliable and reproducible environment with scaled panty of production.Aggregate data in a centralized Data Lake for analysisUse historical data to perform predictive analytics on future shipments Accurately track every shipment worldwide using proprietary technology Improve business agility and speed of innovation through rapid provisioning of new resources Analyze and optimize architecture for performance in the cloud Migrate fully to the cloud if all other requirements are met Technical Requirements Handle both streaming and batch data Migrate existing Hadoop workloads Ensure architecture is scalable and elastic to meet the changing demands of the company.Use managed services whenever possibleEncrypt data flight and at restConnect a VPN between the production data center and cloud environment SEO Statement We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.We need to organize our information so we can more easily understand where our customers are and what they are shipping.CTO StatementIT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.CFO StatementPart of our competitive advantage is that we penalize ourselves for late shipments and deliveries.Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

Accepted Answer

Cloud Pub/Sub, Cloud SQL, and Cloud Storage

Answer

Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage

Answer

Cloud Pub/Sub, Cloud Dataflow, and Local SSD

Answer

Cloud Load Balancing, Cloud Dataflow, and Cloud Storage

Question 36

Company OverviewFlowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.Company BackgroundThe company started as a regional trucking company, and then expanded into other logistics market.Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.Solution ConceptFlowlogistic wants to implement two concepts using the cloud:Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.Existing Technical EnvironmentFlowlogistic architecture resides in a single data center:Databases8 physical servers in 2 clustersSQL Server ñ user data, inventory, static data3 physical serversCassandra ñ metadata, tracking messages10 Kafka servers ñ tracking message aggregation and batch insertApplication servers ñ customer front end, middleware for order/customs 60 virtual machines across 20 physical servers Tomcat ñ Java services Nginx ñ static content Batch servers Storage appliances iSCSI for virtual machine (VM) hosts Fibre Channel storage area network (FC SAN) ñ SQL server storage Network-attached storage (NAS) image storage, logs, backups Apache Hadoop /Spark servers Core Data Lake Data analysis workloads 20 miscellaneous serversJenkins, monitoring, bastion hosts, Business Requirements Build a reliable and reproducible environment with scaled panty of production.Aggregate data in a centralized Data Lake for analysisUse historical data to perform predictive analytics on future shipments Accurately track every shipment worldwide using proprietary technology Improve business agility and speed of innovation through rapid provisioning of new resources Analyze and optimize architecture for performance in the cloud Migrate fully to the cloud if all other requirements are met Technical Requirements Handle both streaming and batch data Migrate existing Hadoop workloads Ensure architecture is scalable and elastic to meet the changing demands of the company.Use managed services whenever possibleEncrypt data flight and at restConnect a VPN between the production data center and cloud environment SEO Statement We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.We need to organize our information so we can more easily understand where our customers are and what they are shipping.CTO StatementIT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.CFO StatementPart of our competitive advantage is that we penalize ourselves for late shipments and deliveries.Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

Accepted Answer

Create a view on the table to present to the virtualization tool.

Answer

Export the data into a Google Sheet for virtualization.

Answer

Create an additional table with only the necessary columns.

Answer

Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.

Question 37

Company OverviewFlowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.Company BackgroundThe company started as a regional trucking company, and then expanded into other logistics market.Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.Solution ConceptFlowlogistic wants to implement two concepts using the cloud:Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.Existing Technical EnvironmentFlowlogistic architecture resides in a single data center:Databases8 physical servers in 2 clustersSQL Server ñ user data, inventory, static data3 physical serversCassandra ñ metadata, tracking messages10 Kafka servers ñ tracking message aggregation and batch insertApplication servers ñ customer front end, middleware for order/customs 60 virtual machines across 20 physical servers Tomcat ñ Java services Nginx ñ static content Batch servers Storage appliances iSCSI for virtual machine (VM) hosts Fibre Channel storage area network (FC SAN) ñ SQL server storage Network-attached storage (NAS) image storage, logs, backups Apache Hadoop /Spark servers Core Data Lake Data analysis workloads 20 miscellaneous serversJenkins, monitoring, bastion hosts, Business Requirements Build a reliable and reproducible environment with scaled panty of production.Aggregate data in a centralized Data Lake for analysisUse historical data to perform predictive analytics on future shipments Accurately track every shipment worldwide using proprietary technology Improve business agility and speed of innovation through rapid provisioning of new resources Analyze and optimize architecture for performance in the cloud Migrate fully to the cloud if all other requirements are met Technical Requirements Handle both streaming and batch data Migrate existing Hadoop workloads Ensure architecture is scalable and elastic to meet the changing demands of the company.Use managed services whenever possibleEncrypt data flight and at restConnect a VPN between the production data center and cloud environment SEO Statement We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.We need to organize our information so we can more easily understand where our customers are and what they are shipping.CTO StatementIT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.CFO StatementPart of our competitive advantage is that we penalize ourselves for late shipments and deliveries.Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.Which approach should you take?

Accepted Answer

Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.

Answer

Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.

Answer

Use the NOW () function in BigQuery to record the event's time.

Answer

Use the automatically generated timestamp from Cloud Pub/Sub to order the data.

Question 38

Company OverviewMJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.Company BackgroundFounded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.Solution ConceptMJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.MJTelco will also use three separate operating environments ñ development/test, staging, and production ñ to meet the needs of running experiments, deploying new features, and serving production customers.Business RequirementsScale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.Provide reliable and timely access to data for analysis from distributed research workers Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.Technical RequirementsEnsure secure and efficient transport and storage of telemetry dataRapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.CEO StatementOur business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.CTO StatementOur public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.CFO StatementThe project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure.Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.MJTelco's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?

Accepted Answer

The zone

Answer

The number of workers

Answer

The disk size per worker

Answer

The maximum number of workers

Question 39

Company OverviewMJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.Company BackgroundFounded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.Solution ConceptMJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.MJTelco will also use three separate operating environments ñ development/test, staging, and production ñ to meet the needs of running experiments, deploying new features, and serving production customers.Business RequirementsScale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.Provide reliable and timely access to data for analysis from distributed research workers Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.Technical RequirementsEnsure secure and efficient transport and storage of telemetry dataRapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.CEO StatementOur business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.CTO StatementOur public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.CFO StatementThe project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure.Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.You need to compose visualizations for operations teams with the following requirements:Which approach meets the requirements?

Accepted Answer

Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.

Answer

Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.

Answer

Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.

Answer

Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.

Question 40

Company OverviewMJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.Company BackgroundFounded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.Solution ConceptMJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.MJTelco will also use three separate operating environments ñ development/test, staging, and production ñ to meet the needs of running experiments, deploying new features, and serving production customers.Business RequirementsScale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.Provide reliable and timely access to data for analysis from distributed research workers Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.Technical RequirementsEnsure secure and efficient transport and storage of telemetry dataRapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.CEO StatementOur business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.CTO StatementOur public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.CFO StatementThe project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure.Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.You create a new report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. It is company policy to ensure employees can view only the data associated with their region, so you create and populate a table for each region. You need to enforce the regional access policy to the data.Which two actions should you take? (Choose two.)

Accepted Answer

Ensure each table is included in a dataset for a region.

Accepted Answer

Adjust the settings for each view to allow a related region-based security group view access.

Answer

Ensure all the tables are included in global dataset.

Answer

Adjust the settings for each table to allow a related region-based security group view access.

Answer

Adjust the settings for each dataset to allow a related region-based security group view access.

Google Professional Data Engineer Practice Test 1

Google Professional Data Engineer Practice Tests

Google Professional Data Engineer Practice Test 1

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Google Professional Data Engineer Practice Tests

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