You have an Apache Kafka Cluster on-prem with topics containing web application logs. You need to replicate the data to Google Cloud for analysis in BigQuery and Cloud Storage. The preferred replication method is mirroring to avoid deployment of Kafka Connect plugins.What should you do?

Deploy a Kafka cluster on GCE VM Instances. Configure your on-prem cluster to mirror your topics to the cluster running in GCE. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.

Deploy a Kafka cluster on GCE VM Instances with the PubSub Kafka connector configured as a Sink connector. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.

Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Source connector. Use a Dataflow job to read fron PubSub and write to GCS.

Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Sink connector. Use a Dataflow job to read fron PubSub and write to GCS.

You've migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet files (on average 200-400 MB size each). You see some degradation in performance after the migration to Dataproc, so you'd like to optimize for it. You need to keep in mind that your organization is very cost-sensitive, so you'd like to continue using Dataproc on preemptibles (with 2 non-preemptible workers only) for this workload.What should you do?

Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.

Increase the size of your parquet files to ensure them to be 1 GB minimum.

Switch to TFRecords formats (appr. 200MB per file) instead of parquet files.

Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.

Switch from HDDs to SSDs, override the preemptible VMs configuration to increase the boot disk size.

Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl. being able to reprocess all failing data).What should you do?

Add a tryÖ catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.

Add a filtering step to skip these types of errors in the future, extract erroneous rows from logs.

Add a tryÖ catch block to your DoFn that transforms the data, extract erroneous rows from logs.

Add a tryÖ catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.

Add a tryÖ catch block to your DoFn that transforms the data, use a sideOutput to create a PCollection that can be stored to PubSub later.

You're training a model to predict housing prices based on an available dataset with real estate properties. Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longtitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.What should you do?

Create a numeric column from a feature cross of latitude and longtitude.

Provide latitude and longtitude as input vectors to your neural net.

Create a numeric column from a feature cross of latitude and longtitude.

Create a feature cross of latitude and longtitude, bucketize at the minute level and use L1 regularization during optimization.

Create a feature cross of latitude and longtitude, bucketize it at the minute level and use L2 regularization during optimization.

You are deploying MariaDB SQL databases on GCE VM Instances and need to configure monitoring and alerting. You want to collect metrics including network connections, disk IO and replication status from MariaDB with minimal development effort and use StackDriver for dashboards and alerts.What should you do?

Install the StackDriver Logging Agent and configure fluentd in_tail plugin to read MariaDB logs.

Install the OpenCensus Agent and create a custom metric collection application with a StackDriver exporter.

Place the MariaDB instances in an Instance Group with a Health Check.

Install the StackDriver Logging Agent and configure fluentd in_tail plugin to read MariaDB logs.

Install the StackDriver Agent and configure the MySQL plugin.

You work for a bank. You have a labelled dataset that contains information on already granted loan application and whether these applications have been defaulted. You have been asked to train a model to predict default rates for credit applicants.What should you do?

Train a linear regression to predict a credit default risk score.

Increase the size of the dataset by collecting additional data.

Train a linear regression to predict a credit default risk score.

Remove the bias from the data and collect applications that have been declined loans.

Match loan applicants with their social profiles to enable feature engineering.

You're using Bigtable for a real-time application, and you have a heavy load that is a mix of read and writes. You've recently identified an additional use case and need to perform hourly an analytical job to calculate certain statistics across the whole database. You need to ensure both the reliability of your production application as well as the analytical workload.What should you do?

Add a second cluster to an existing instance with a multi-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.

Export Bigtable dump to GCS and run your analytical job on top of the exported files.

Add a second cluster to an existing instance with a multi-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.

Add a second cluster to an existing instance with a single-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.

Increase the size of your existing cluster twice and execute your analytics workload on your new resized cluster.

You are designing an Apache Beam pipeline to enrich data from Cloud Pub/Sub with static reference data from BigQuery. The reference data is small enough to fit in memory on a single worker. The pipeline should write enriched results toBigQuery for analysis. Which job type and transforms should this pipeline use?

Streaming job, PubSubIO, BigQueryIO, side-inputs

Batch job, PubSubIO, side-inputs

Streaming job, PubSubIO, JdbcIO, side-outputs

Streaming job, PubSubIO, BigQueryIO, side-inputs

Streaming job, PubSubIO, BigQueryIO, side-outputs

You have a data pipeline that writes data to Cloud Bigtable using well-designed row keys. You want to monitor your pipeline to determine when to increase the size of you Cloud Bigtable cluster. Which two actions can you take to accomplish this? Choose 2 answers.

Review Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Read pressure index is above 100.

Monitor the latency of write operations. Increase the size of the Cloud Bigtable cluster when there is a sustained increase in write latency.

Review Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Read pressure index is above 100.

Review Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Write pressure index is above 100.

Monitor the latency of write operations. Increase the size of the Cloud Bigtable cluster when there is a sustained increase in write latency.

Monitor storage utilization. Increase the size of the Cloud Bigtable cluster when utilization increases above 70% of max capacity.

Monitor latency of read operations. Increase the size of the Cloud Bigtable cluster of read operations take longer than 100 ms.

You want to analyze hundreds of thousands of social media posts daily at the lowest cost and with the fewest steps.You have the following requirements:You will batch-load the posts once per day and run them through the Cloud Natural Language API.You will extract topics and sentiment from the posts.You must store the raw posts for archiving and reprocessing.You will create dashboards to be shared with people both inside and outside your organization.You need to store both the data extracted from the API to perform analysis as well as the raw social media posts for historical archiving. What should you do?

Feed to social media posts into the API directly from the source, and write the extracted data from the API into BigQuery.

Store the social media posts and the data extracted from the API in BigQuery.

Store the social media posts and the data extracted from the API in Cloud SQL.

Store the raw social media posts in Cloud Storage, and write the data extracted from the API into BigQuery.

Feed to social media posts into the API directly from the source, and write the extracted data from the API into BigQuery.

You store historic data in Cloud Storage. You need to perform analytics on the historic dat a. You want to use a solution to detect invalid data entries and perform data transformations that will not require programming or knowledge of SQL.What should you do?

Use Cloud Dataprep with recipes to detect errors and perform transformations.

Use Cloud Dataflow with Beam to detect errors and perform transformations.

Use Cloud Dataprep with recipes to detect errors and perform transformations.

Use Cloud Dataproc with a Hadoop job to detect errors and perform transformations.

Use federated tables in BigQuery with queries to detect errors and perform transformations.

Your company needs to upload their historic data to Cloud Storage. The security rules don't allow access from external IPs to their on-premises resources. After an initial upload, they will add new data from existing on-premises applications every day. What should they do?

Use Cloud Dataflow and write the data to Cloud Storage.

Execute gsutil rsync from the on-premises servers.

Use Cloud Dataflow and write the data to Cloud Storage.

Write a job template in Cloud Dataproc to perform the data transfer.

Install an FTP server on a Compute Engine VM to receive the files and move them to Cloud Storage.

You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns.By using bq query ñ -dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall dat a. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?

Recreate the table with a partitioning column and clustering column.

Create a separate table for each ID.

Use the LIMIT keyword to reduce the number of rows returned.

Recreate the table with a partitioning column and clustering column.

Use the bq query - -maximum_bytes_billed flag to restrict the number of bytes billed.

You have a requirement to insert minute-resolution data from 50,000 sensors into a BigQuery table.You expect significant growth in data volume and need the data to be available within 1 minute of ingestion for real-time analysis of aggregated trends. What should you do?

Use the INSERT statement to insert a batch of data every 60 seconds.

Use bq load to load a batch of sensor data every 60 seconds.

Use a Cloud Dataflow pipeline to stream data into the BigQuery table.

Use the INSERT statement to insert a batch of data every 60 seconds.

Use the MERGE statement to apply updates in batch every 60 seconds.

You need to copy millions of sensitive patient records from a relational database to BigQuery. The total size of the database is 10 TB. You need to design a solution that is secure and time-efficient.What should you do?

Export the records from the database as an Avro file. Upload the file to GCS using gsutil, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.

Export the records from the database as an Avro file. Copy the file onto a Transfer Appliance and send it to Google, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.

Export the records from the database into a CSV file. Create a public URL for the CSV file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the CSV file into BigQuery using the BigQuery web UI in the GCPConsole.

Export the records from the database as an Avro file. Create a public URL for the Avro file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the Avro file into BigQuery using the BigQuery web UI in theGCP Console.

You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?

Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.

Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.

Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.

Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.

You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

Set the BigQuery dataset to be regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.

Set the BigQuery dataset to be regional. In the event of an emergency, use a point-in-time snapshot to recover the data.

Set the BigQuery dataset to be regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.

Set the BigQuery dataset to be multi-regional. In the event of an emergency, use a point-in-time snapshot to recover the data.

Set the BigQuery dataset to be multi-regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.

You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.

Create a cron schedule in Cloud Dataprep.

Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.

Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.

Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.

You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other.You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

Workflow Templates on Cloud Dataproc

You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.

Increase the cluster size with more non-preemptible workers.

Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.

Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.

Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.

You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.

Create an authorized view in BigQuery to restrict access to tables with sensitive data.

Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.

Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.

Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.

You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.

Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.

Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.

Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.

Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.

You have Cloud Functions written in Node.js that pull messages from Cloud Pub/Sub and send the data to BigQuery. You observe that the message processing rate on the Pub/Sub topic is orders of magnitude higher than anticipated, but there is no error logged in Stackdriver Log Viewer. What are the two most likely causes of this problem? Choose 2 answers.

Error handling in the subscriber code is not handling run-time errors properly.

The subscriber code cannot keep up with the messages.

Publisher throughput quota is too small.

Total outstanding messages exceed the 10-MB maximum.

Error handling in the subscriber code is not handling run-time errors properly.

The subscriber code cannot keep up with the messages.

The subscriber code does not acknowledge the messages that it pulls.

You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt dat a. What should you do?

Add a ParDo transform in Cloud Dataflow to discard corrupt elements.

Add a SideInput that returns a Boolean if the element is corrupt.

Add a ParDo transform in Cloud Dataflow to discard corrupt elements.

Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.

Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.

You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30ñ90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries.What should you do?

Modify your pipeline to maintain the last 30ñ90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.

Re-create the tables using DDL. Partition the tables by a column containing a TIMESTAMP or DATE Type.

Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.

Modify your pipeline to maintain the last 30ñ90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.

Write an Apache Beam pipeline that creates a BigQuery table per day. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.

You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors. You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?

Have the data acquisition devices publish data to Cloud Pub/Sub.

Deploy small Kafka clusters in your data centers to buffer events.

Have the data acquisition devices publish data to Cloud Pub/Sub.

Establish a Cloud Interconnect between all remote data centers and Google.

Write a Cloud Dataflow pipeline that aggregates all data in session windows.

You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current.Which solution should you choose?

Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide thirdparty companies with access to the bucket.

Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.

Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide thirdparty companies with access to the bucket.

Create a separate dataset in BigQuery that contains the relevant data to share, and provide thirdparty companies with access to the new dataset.

Create a Cloud Dataflow job that reads the data in frequent time intervals, and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.

A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?

Implement clustering in BigQuery on the ingest date column.

Implement clustering in BigQuery on the package-tracking ID column.

Tier older data onto Cloud Storage files, and leverage extended tables.

Re-create the table using data partitioning on the package delivery date.

You are designing a data processing pipeline. The pipeline must be able to scale automatically as load increases. Messages must be processed at least once, and must be ordered within windows of 1 hour. How should you design the solution?

Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.

Use Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.

Use Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis.

Use Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.

Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.

You need to set access to BigQuery for different departments within your company. Your solution should comply with the following requirements:Each department should have access only to their data.Each department will have one or more leads who need to be able to create and update tables and provide them to their team.Each department has data analysts who need to be able to query but not modify data.How should you set access to the data in BigQuery?

Create a table for each department. Assign the department leads the role of Editor, and assign the data analysts the role of Viewer on the project the table is in.

Create a dataset for each department. Assign the department leads the role of OWNER, and assign the data analysts the role of WRITER on their dataset.

Create a dataset for each department. Assign the department leads the role of WRITER, and assign the data analysts the role of READER on their dataset.

Create a table for each department. Assign the department leads the role of Owner, and assign the data analysts the role of Editor on the project the table is in.

Create a table for each department. Assign the department leads the role of Editor, and assign the data analysts the role of Viewer on the project the table is in.

You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added.What should you do to improve the performance of your application?

Change the row key syntax in your Cloud Bigtable table to begin with the stock symbol.

Change the row key syntax in your Cloud Bigtable table to begin with a random number per second.

Change the data pipeline to use BigQuery for storing stock trades, and update your application.

Use Cloud Dataflow to write summary of each day's stock trades to an Avro file on Cloud Storage.Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.

You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing dat a. Which Stackdriver alerts should you create?

An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination

An alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination

An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination

An alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination

An alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination

You currently have a single on-premises Kafka cluster in a data center in the us-east region that is responsible for ingesting messages from IoT devices globally. Because large parts of globe have poor internet connectivity, messages sometimes batch at the edge, come in all at once, and cause a spike in load on your Kafka cluster. This is becoming difficult to manage and prohibitively expensive. What is the Google-recommended cloud native architecture for this scenario?

An IoT gateway connected to Cloud Pub/Sub, with Cloud Dataflow to read and process the messages from Cloud Pub/Sub.

Edge TPUs as sensor devices for storing and transmitting the messages.

Cloud Dataflow connected to the Kafka cluster to scale the processing of incoming messages.

An IoT gateway connected to Cloud Pub/Sub, with Cloud Dataflow to read and process the messages from Cloud Pub/Sub.

A Kafka cluster virtualized on Compute Engine in us-east with Cloud Load Balancing to connect to the devices around the world.

You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time. Which two methods can accomplish this?Choose 2 answers.

Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.

Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.

Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.

Use managed exportm, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.

Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.

Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.

Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.

You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

Denormalize the data as must as possible.

Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro file. Use BigQuery's support for external data sources to query.

Denormalize the data as must as possible.

Preserve the structure of the data as much as possible.

Use BigQuery UPDATE to further reduce the size of the dataset.

Develop a data pipeline where status updates are appended to BigQuery instead of updated.

Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro file. Use BigQuery's support for external data sources to query.

You are designing a cloud-native historical data processing system to meet the following conditions:The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.A streaming data pipeline stores new data daily.Peformance is not a factor in the solution.The solution design should maximize availability.How should you design data storage for this solution?

Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.

Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.

Store the data in BigQuery. Access the data using the BigQuery Connector or Cloud Dataproc and Compute Engine.

Store the data in a regional Cloud Storage bucket. Aceess the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.

Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.

Google Professional Data Engineer Practice Test 5 - Free Online Exam Prep & Questions

You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don't get slots to execute their query and you need to correct this. You'd like to avoid introducing new projects to your account.

What should you do?

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Google Professional Data Engineer Practice Test 5

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Google Professional Data Engineer Practice Tests

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