PCA practice questions 331–335

1. Question 331 · 335 in bank

   For this question, refer to the TerramEarth case study. TerramEarth has decided to store data files in Cloud Storage. You need to configure Cloud Storage lifecycle rule to store 1 year of data and minimize file storage cost. Which two actions should you take?

   • ACreate a Cloud Storage lifecycle rule with Age: ג€30ג€, Storage Class: ג€Standardג€, and Action: ג€Set to Coldlineג€, and create a second GCS life-cycle rule with Age: ג€365ג€, Storage Class: ג€Coldlineג€, and Action: ג€Deleteג€.
   • BCreate a Cloud Storage lifecycle rule with Age: ג€30ג€, Storage Class: ג€Coldlineג€, and Action: ג€Set to Nearlineג€, and create a second GCS life-cycle rule with Age: ג€91ג€, Storage Class: ג€Coldlineג€, and Action: ג€Set to Nearlineג€.
   • CCreate a Cloud Storage lifecycle rule with Age: ג€90ג€, Storage Class: ג€Standardג€, and Action: ג€Set to Nearlineג€, and create a second GCS life-cycle rule with Age: ג€91ג€, Storage Class: ג€Nearlineג€, and Action: ג€Set to Coldlineג€.
   • DCreate a Cloud Storage lifecycle rule with Age: ג€30ג€, Storage Class: ג€Standardג€, and Action: ג€Set to Coldlineג€, and create a second GCS life-cycle rule with Age: ג€365ג€, Storage Class: ג€Nearlineג€, and Action: ג€Deleteג€.

   Show answer

   Answer: A

   Explanation

   Answer A o When Only Option A & D talks about deleting the file after 1 Year. In Option D at Age 30 the storage Class is set to Coldline and while deleting they have used the condition Storage Class: "Nearline" which is incorrect.

   Source: Adapted from community discussion — VishalB

   References

   • Google Cloud documentation

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2. Question 332 · 335 in bank

   For this question, refer to the TerramEarth case study. You need to implement a reliable, scalable GCP solution for the data warehouse for your company, TerramEarth. Considering the TerramEarth business and technical requirements, what should you do?

   • AReplace the existing data warehouse with BigQuery. Use table partitioning.
   • BReplace the existing data warehouse with a Compute Engine instance with 96 CPUs.
   • CReplace the existing data warehouse with BigQuery. Use federated data sources.
   • DReplace the existing data warehouse with a Compute Engine instance with 96 CPUs. Add an additional Compute Engine preemptible instance with 32 CPUs.

   Show answer

   Answer: A

   Explanation

   A is the correct answer because the question was asking for a reliable way of improving the data warehouse. The reliable way is to have a table partitioned and that can be well managed. https://cloud.google.com/solutions/bigquery-data-warehouse BigQuery supports partitioning tables by date. You enable partitioning during the table-creation process. BigQuery creates new date-based partitions automatically, with no need for additional maintenance. In addition, you can specify an expiration time for data in the partitions. https://cloud.google.com/solutions/bigquery-data-warehouse#partitioning_tables Federated is an option but not a reliable option. You can run queries on data that exists outside of BigQuery by using federated data sources, but this approach has performance implications. Use federated data sources only if the data must be maintained externally. You can also use query federation to perform ETL from an external source to BigQuery. This approach allows you to define ETL using familiar SQL syntax. https://cloud.google.com/solutions/bigquery-data-warehouse#external_sources

   Source: Adapted from community discussion — Ziegler

   References

   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation

   Next question

3. Question 333 · 335 in bank

   For this question, refer to the TerramEarth case study. A new architecture that writes all incoming data to BigQuery has been introduced. You notice that the data is dirty, and want to ensure data quality on an automated daily basis while managing cost. What should you do?

   • ASet up a streaming Cloud Dataflow job, receiving data by the ingestion process. Clean the data in a Cloud Dataflow pipeline.
   • BCreate a Cloud Function that reads data from BigQuery and cleans it. Trigger the Cloud Function from a Compute Engine instance.
   • CCreate a SQL statement on the data in BigQuery, and save it as a view. Run the view daily, and save the result to a new table.
   • DUse Cloud Dataprep and configure the BigQuery tables as the source. Schedule a daily job to clean the data.

   Show answer

   Answer: D

   Explanation

   A is the correct answer e.g. When gathering information from the real world, the data will often contain errors, omissions, or inconsistencies that should be corrected before you can analyze it effectively. Instead of doing it by hand, or performing a separate cleansing step, Google Cloud Dataflow allows you to define simple functions that can cleanse your data in a pipeline, which you can plug into your data ingestion pipeline for automatic cleansing. https://cloud.google.com/community/tutorials/data-science-preprocessing

   Source: Adapted from community discussion — Ziegler

   References

   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation

   Next question

4. Question 334 · 335 in bank

   For this question, refer to the TerramEarth case study. Considering the technical requirements, how should you reduce the unplanned vehicle downtime in GCP?

   • AUse BigQuery as the data warehouse. Connect all vehicles to the network and stream data into BigQuery using Cloud Pub/Sub and Cloud Dataflow. Use Google Data Studio for analysis and reporting.
   • BUse BigQuery as the data warehouse. Connect all vehicles to the network and upload gzip files to a Multi-Regional Cloud Storage bucket using gcloud. Use Google Data Studio for analysis and reporting.
   • CUse Cloud Dataproc Hive as the data warehouse. Upload gzip files to a Multi-Regional Cloud Storage bucket. Upload this data into BigQuery using gcloud. Use Google Data Studio for analysis and reporting.
   • DUse Cloud Dataproc Hive as the data warehouse. Directly stream data into partitioned Hive tables. Use Pig scripts to analyze data.

   Show answer

   Answer: A

   Explanation

   Once all the vehicle are connected to network, there is no need to use FTP; data can be ingested directly to BQ using Pub/Sub and DataFlow.

   Source: Adapted from community discussion — AdityaGupta

   References

   • Google Cloud documentation
   • Google Cloud documentation

   Next question

5. Question 335 · 335 in bank

   For this question, refer to the TerramEarth case study. You are asked to design a new architecture for the ingestion of the data of the 200,000 vehicles that are connected to a cellular network. You want to follow Google-recommended practices. Considering the technical requirements, which components should you use for the ingestion of the data?

   • AGoogle Kubernetes Engine with an SSL Ingress
   • BCloud IoT Core with public/private key pairs
   • CCompute Engine with project-wide SSH keys
   • DCompute Engine with specific SSH keys

   Show answer

   Answer: B

   Explanation

   B – Cloud IoT Core with public / private key pairs. https://cloud.google.com/iot-core/ IoT Core was developed for connecting existing devices spread around the world to GCP. Also, it supports end-to-end security using asymmetric key authentication over TLS 1.2. So, this is exact match for Q.

   Source: Adapted from community discussion — MaxNRG

   References

   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation

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PCA practice, question 331. 335 questions available.