Which is not a valid reason for poor Cloud Bigtable performance?

The Cloud Bigtable cluster has too many nodes.

The workload isn't appropriate for Cloud Bigtable.

The table's schema is not designed correctly.

The Cloud Bigtable cluster has too many nodes.

There are issues with the network connection.

When you design a Google Cloud Bigtable schema it is recommended that you _________.

Avoid schema designs that require atomicity across rows

Avoid schema designs that are based on NoSQL concepts

Create schema designs that are based on a relational database design

Avoid schema designs that require atomicity across rows

Create schema designs that require atomicity across rows

Which of the following is NOT a valid use case to select HDD (hard disk drives) as the storage for Google Cloud Bigtable?

You need to integrate with Google BigQuery.

You expect to store at least 10 TB of data.

You will mostly run batch workloads with scans and writes, rather than frequently executing random reads of a small number of rows.

You need to integrate with Google BigQuery.

You will not use the data to back a user-facing or latency-sensitive application.

If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

Do not use a production instance.

Run your test for at least 10 minutes.

Before you test, run a heavy pre-test for several minutes.

Cloud Bigtable is a recommended option for storing very large amounts of ____________________________?

single-keyed data with very low latency

multi-keyed data with very high latency

multi-keyed data with very low latency

single-keyed data with very low latency

single-keyed data with very high latency

What is the HBase Shell for Cloud Bigtable?

The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables.

The HBase shell is a GUI based interface that performs administrative tasks, such as creating and deleting tables.

The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables.

The HBase shell is a hypervisor based shell that performs administrative tasks, such as creating and deleting new virtualized instances.

The HBase shell is a command-line tool that performs only user account management functions to grant access to Cloud Bigtable instances.

What is the recommended action to do in order to switch between SSD and HDD storage for your Google Cloud Bigtable instance?

export the data from the existing instance and import the data into a new instance

create a third instance and sync the data from the two storage types via batch jobs

export the data from the existing instance and import the data into a new instance

run parallel instances where one is HDD and the other is SDD

the selection is final and you must resume using the same storage type

You are training a spam classifier. You notice that you are overfitting the training dat a. Which three actions can you take to resolve this problem? (Choose three.)

Decrease the regularization parameters

Reduce the number of training examples

Increase the regularization parameters

Decrease the regularization parameters

You are implementing security best practices on your data pipeline. Currently, you are manually executing jobs as the Project Owner. You want to automate these jobs by taking nightly batch files containing non-public information fromGoogle Cloud Storage, processing them with a Spark Scala job on a Google Cloud Dataproc cluster, and depositing the results into Google BigQuery.How should you securely run this workload?

Grant the Project Owner role to a service account, and run the job with it

Restrict the Google Cloud Storage bucket so only you can see the files

Grant the Project Owner role to a service account, and run the job with it

Use a service account with the ability to read the batch files and to write to BigQuery

Use a user account with the Project Viewer role on the Cloud Dataproc cluster to read the batch files and write to BigQuery

You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1: What is the most likely cause of the delay for this query?

Users are running too many concurrent queries in the system

The [myproject:mydataset.mytable] table has too many partitions

Either the state or the city columns in the [myproject:mydataset.mytable] table have too many NULL values

Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew

Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?

Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.

Create a file on a shared file and have the application servers write all bid events to that file.Process the file with Apache Hadoop to identify which user bid first.

Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.

Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.

Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in the bid event that is processed first.

Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the cost of queries, your organization created a view called events, which queries only the last 14 days of dat a. The view is described in legacy SQL. Next month, existing applications will be connecting to BigQuery to read the events data via an ODBC connection. You need to ensure the applications can connect. Which two actions should you take? (Choose two.)

Create a new view over events using standard SQL

Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connection and shared "events"

Create a new view over events using standard SQL

Create a new partitioned table using a standard SQL query

Create a new view over events_partitioned using standard SQL

Create a service account for the ODBC connection to use for authentication

Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connection and shared "events"

You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

Use the TABLE_DATE_RANGE function

Use the WHERE_PARTITIONTIME pseudo column

Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD

Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD

Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?

They have not applied a global windowing function, which causes the job to fail when the pipeline is created

They have not assigned the timestamp, which causes the job to fail

They have not set the triggers to accommodate the data coming in late, which causes the job to fail

They have not applied a global windowing function, which causes the job to fail when the pipeline is created

They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created

You architect a system to analyze seismic dat a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.

Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.

Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.

Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.

You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges.Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

Convert all daily log tables into date-partitioned tables

Convert the sharded tables into a single partitioned table

Enable query caching so you can cache data from previous months

Create separate views to cover each month, and query from these views

Your analytics team wants to build a simple statistical model to determine which customers are most likely to work with your company again, based on a few different metrics. They want to run the model on Apache Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on a 15-node cluster, outputting the results into GoogleBigQuery. The plan is to run this workload weekly. How should you optimize the cluster for cost?

Migrate the workload to Google Cloud Dataflow

Use pre-emptible virtual machines (VMs) for the cluster

Use a higher-memory node so that the job runs faster

Use SSDs on the worker nodes so that the job can run faster

Your company receives both batch- and stream-based event dat a. You want to process the data using Google Cloud Dataflow over a predictable time period.However, you realize that in some instances data can arrive late or out of order. How should you design your Cloud Dataflow pipeline to handle data that is late or out of order?

Set sliding windows to capture all the lagged data.

Set a single global window to capture all the data.

Set sliding windows to capture all the lagged data.

Use watermarks and timestamps to capture the lagged data.

Ensure every datasource type (stream or batch) has a timestamp, and use the timestamps to define the logic for lagged data.

You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.What should you do?

Create a service account and grant dataset access to that account. Use the service account's private key to access the dataset

Create groups for your users and give those groups access to the dataset

Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request

Create a service account and grant dataset access to that account. Use the service account's private key to access the dataset

Create a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset

You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed.What should you do?

Create encryption keys locally. Upload your encryption keys to Cloud Key Management Service.Use those keys to encrypt your data in all of the Compute Engine cluster instances.

Create a dedicated service account, and use encryption at rest to reference your data stored in your Compute Engine cluster instances as part of your API service calls.

Create encryption keys in Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.

Create encryption keys locally. Upload your encryption keys to Cloud Key Management Service.Use those keys to encrypt your data in all of the Compute Engine cluster instances.

Create encryption keys in Cloud Key Management Service. Reference those keys in your API service calls when accessing the data in your Compute Engine cluster instances.

You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of dat a. What should you do?

Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.

Build and train a complex classification model with Spark MLlib to generate labels and filter the results.Deploy the models using Cloud Dataproc. Call the model from your application.

Build and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences. Deploy the models using Cloud Dataproc. Call the models from your application.

Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.

Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.

You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.

Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.

Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.

Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.

Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs. Configure the job to use non-default Compute Engine machine types when needed.

Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-todate YouTube channels log dat a. How should you set up the log data transfer into Google Cloud?

Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as a final destination.

Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.

Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as a final destination.

Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi- Regional storage bucket as a final destination.

Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional storage bucket as a final destination.

You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices. What should you do?

Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.

Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.

Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.

Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.

Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.

You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.

Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.

Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.

Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine Learning Engine. Call the model from your application and process the results as labels.

Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.

You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud.Your input data is in CSV format. You want to minimize the cost of querying aggregate values for multiple users who will query the data in Cloud Storage with multiple engines. Which storage service and schema design should you use?

Use Cloud Bigtable for storage. Install the HBase shell on a Compute Engine instance to query the Cloud Bigtable data.

Use Cloud Bigtable for storage. Link as permanent tables in BigQuery for query.

Use Cloud Storage for storage. Link as permanent tables in BigQuery for query.

Use Cloud Storage for storage. Link as temporary tables in BigQuery for query.

You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud.You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.

Use Cloud SQL for storage. Add secondary indexes to support query patterns.

Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.

Use Cloud Spanner for storage. Add secondary indexes to support query patterns.

Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.

An organization maintains a Google BigQuery dataset that contains tables with user-level dat

Create and share a new dataset and table that contains the aggregate results.

They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?

Create and share an authorized view that provides the aggregate results.

Create and share a new dataset and view that provides the aggregate results.

Create and share a new dataset and table that contains the aggregate results.

Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.

Government regulations in your industry mandate that you have to maintain an auditable record of access to certain types of dat

Encrypted on Cloud Storage with user-supplied encryption keys. A separate decryption key will be given to each authorized user.

Assuming that all expiring logs will be archived correctly, where should you store data that is subject to that mandate?

Encrypted on Cloud Storage with user-supplied encryption keys. A separate decryption key will be given to each authorized user.

In a BigQuery dataset that is viewable only by authorized personnel, with the Data Access log used to provide the auditability.

In Cloud SQL, with separate database user names to each user. The Cloud SQL Admin activity logs will be used to provide the auditability.

In a bucket on Cloud Storage that is accessible only by an AppEngine service that collects user information and logs the access before providing a link to the bucket.

Your neural network model is taking days to train. You want to increase the training speed. What can you do?

Increase the number of layers in your neural network.

Subsample your training dataset.

Increase the number of input features to your model.

Increase the number of layers in your neural network.

Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

Increase your network bandwidth from your datacenter to GCP.

Increase the CPU size on your server.

Increase the size of the Google Persistent Disk on your server.

Increase your network bandwidth from your datacenter to GCP.

Increase your network bandwidth from Compute Engine to Cloud Storage.

After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You've loaded a table containing the output of the original job and want to compare the contents with output from the migrated job to show that they are identical. The tables do not contain a primary key column that would enable you to join them together for comparison.What should you do?

Select random samples from the tables using the HASH() function and compare the samples.

Select random samples from the tables using the RAND() function and compare the samples.

Select random samples from the tables using the HASH() function and compare the samples.

Use a Dataproc cluster and the BigQuery Hadoop connector to read the data from each table and calculate a hash from non-timestamp columns of the table after sorting. Compare the hashes of each table.

Create stratified random samples using the OVER() function and compare equivalent samples from each table.

Google Professional Data Engineer Practice Test 4 - Free Online Exam Questions 121-160

Spam

Selling illegal goods, money scams etc.

Hate Speech

Serious attack on a group.

Harassment and bullying

Harassing an individual (including doxing)

Harmful activities

Threatening or glorifying violence or serious harm, including self-harm

Adult content (Consensual)

Nudity/Sexual content

Sexual exploitation and abuse (child safety)

Sexually explicit or suggestive imagery or writing involving minors

Sexual exploitation and abuse (adults and animals)

Sexually explicit or suggestive imagery or writing involving non-consenting adults or non-humans

Plagiarism

Reusing content without attribution (link and blockquotes)

Poorly written

Not in English or has very bad formatting, grammar, and spelling

Inappropriate credential

Author's credential is offensive, spam, or impersonation

Other

Ex. Illegal content, incomplete question...

Google Professional Data Engineer Practice Test 4

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Google Professional Data Engineer Practice Tests

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