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?

• A.Write a job template in Cloud Dataproc to perform the data transfer.
• B.Execute gsutil rsyncfrom the on-premises servers.
• C.Install an FTP server on a Compute Engine VM to receive the files and move them to Cloud Storage.
• D.Use Cloud Dataflow and write the data to Cloud Storage.

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When creating a new Cloud Dataproc cluster with the projects.regions.clusters.create operation, these four values are required: project, region, name, and ____.

• A.zone
• B.node
• C.label
• D.type

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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?

• A.Issue a command to restart the database servers.
• B.Retry the query with exponential backoff, up to a cap of 15 minutes.
• C.Retry the query every second until it comes back online to minimize staleness of data.
• D.Reduce the query frequency to once every hour until the database comes back online.

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Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file.
What is the most likely cause of this problem?

• A.The CSV data loaded in BigQuery is not flagged as CSV.
• B.The CSV data loaded in BigQuery is not using BigQuery's default encoding.
• C.The CSV data has invalid rows that were skipped on import.
• D.The CSV data has not gone through an ETL phase before loading into BigQuery.

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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?

• A.Store the data in BigQuery. Access the data using the BigQuery Connector on Cloud Dataproc and Compute Engine.
• B.Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.
• C.Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.
• D.Store the data in a regional Cloud Storage bucket. Access the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.

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