PCA practice questions 321–330

1. Question 321 · 335 in bank

   The TerramEarth development team wants to create an API to meet the company's business requirements. You want the development team to focus their development effort on business value versus creating a custom framework. Which method should they use?

   • AUse Google App Engine with Google Cloud Endpoints. Focus on an API for dealers and partners
   • BUse Google App Engine with a JAX-RS Jersey Java-based framework. Focus on an API for the public
   • CUse Google App Engine with the Swagger (Open API Specification) framework. Focus on an API for the public
   • DUse Google Container Engine with a Django Python container. Focus on an API for the public
   • EUse Google Container Engine with a Tomcat container with the Swagger (Open API Specification) framework. Focus on an API for dealers and partners

   Show answer

   Answer: A

   Explanation

   Google offers Cloud Endpoint to develop, deploy and manage APIs on any google cloud backend. https://cloud.google.com/endpoints With Endpoints Frameworks, you don't have to deploy a third-party web server (such as Apache Tomcat or Gunicorn) with your application. You annotate or decorate the code and deploy your application as you normally would to the App Engine standard environment. Cloud Endpoints Frameworks for the App Engine standard environment : https://cloud.google.com/endpoints/docs/frameworks/about-cloud-endpoints-frameworks

   Source: Adapted from community discussion — Vika

   References

   • Google Cloud documentation
   • Google Cloud documentation

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2. Question 322 · 335 in bank

   Your organization is implementing a new cloud-native application on Google Cloud and needs to ensure compliance with the ISO/IEC 27001 framework. You want to leverage Google Cloud’s security reports and documentation to support your ISO/IEC 27001 audit process. What should you do?

   • AEngage an independent auditor to conduct an ISO/IEC 27001 audit of your organization's Google Cloud implementation.
   • BDownload the ISO/IEC 27001 report for Google Cloud through internet search.
   • CReview the Compliance Reports Manager for information about ISO/IEC 27001 compliance and related documentation on obtaining reports through your Google Cloud account.
   • DUtilize the Cloud Audit Logs service for accessing and requesting the ISO/IEC 27001 reports.

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   Answer: C

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3. Question 323 · 335 in bank

   Your development team has created a structured API to retrieve vehicle data. They want to allow third parties to develop tools for dealerships that use this vehicle event data. You want to support delegated authorization against this data. What should you do?

   • ABuild or leverage an OAuth-compatible access control system
   • BBuild SAML 2.0 SSO compatibility into your authentication system
   • CRestrict data access based on the source IP address of the partner systems
   • DCreate secondary credentials for each dealer that can be given to the trusted third party

   Show answer

   Answer: A

   Explanation

   SAML is an authentication system. OAuth is an authorization system. Both can be used with SSO (Single sign on). SAML is for users and OAuth is more for applications. Answer A

   Source: Adapted from community discussion — ravisar

   References

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

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4. Question 324 · 335 in bank

   TerramEarth plans to connect all 20 million vehicles in the field to the cloud. This increases the volume to 20 million 600 byte records a second for 40 TB an hour. How should you design the data ingestion?

   • AVehicles write data directly to GCS
   • BVehicles write data directly to Google Cloud Pub/Sub
   • CVehicles stream data directly to Google BigQuery
   • DVehicles continue to write data using the existing system (FTP)

   Show answer

   Answer: B

   Explanation

   To handle the volume of data that TerramEarth plans to ingest, it is recommended to use a scalable and reliable data ingestion solution such as Google Cloud Pub/Sub. With Cloud Pub/Sub, the vehicles can stream data directly to the service, which can handle the high volume of data and provide a buffer to absorb sudden spikes in traffic. The data can then be processed and stored in a data warehouse such as BigQuery for analysis. Option A (writing data directly to GCS) may not be suitable for handling high volumes of data in real-time and may result in data loss if the volume exceeds the capacity of GCS. Option C (streaming data directly to BigQuery) may not be suitable for handling high volumes of data in real-time as it may result in data loss or ingestion delays. Option D (continuing to write data using the existing system) may not be suitable as the current system may not be able to handle the increased volume of data and may result in data loss or ingestion delays.

   Source: Adapted from community discussion — omermahgoub

   References

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

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5. Question 325 · 335 in bank

   You analyzed TerramEarth's business requirement to reduce downtime, and found that they can achieve a majority of time saving by reducing customer's wait time for parts. You decided to focus on reduction of the 3 weeks aggregate reporting time. Which modifications to the company's processes should you recommend?

   • AMigrate from CSV to binary format, migrate from FTP to SFTP transport, and develop machine learning analysis of metrics
   • BMigrate from FTP to streaming transport, migrate from CSV to binary format, and develop machine learning analysis of metrics
   • CIncrease fleet cellular connectivity to 80%, migrate from FTP to streaming transport, and develop machine learning analysis of metrics
   • DMigrate from FTP to SFTP transport, develop machine learning analysis of metrics, and increase dealer local inventory by a fixed factor

   Show answer

   Answer: C

   Explanation

   C is right choice because using cellular connectivity will greatly improve the freshness of data used for analysis from where it is now, collected when the machines are in for maintenance. Streaming transport instead of periodic FTP will tighten the feedback loop even more. Machine learning is ideal for predictive maintenance workloads. A is not correct because machine learning analysis is a good means toward the end of reducing downtime, but shuffling formats and transport doesn't directly help at all. B is not correct because machine learning analysis is a good means toward the end of reducing downtime, and moving to streaming can improve the freshness of the information in that analysis, but changing the format doesn't directly help at all. D is not correct because machine learning analysis is a good means toward the end of reducing downtime, but the rest of these changes don't directly help at all.

   Source: Adapted from community discussion — shandy

   References

   • Google Cloud documentation
   • Google Cloud documentation

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6. Question 326 · 335 in bank

   Which of TerramEarth's legacy enterprise processes will experience significant change as a result of increased Google Cloud Platform adoption?

   • AOpex/capex allocation, LAN changes, capacity planning
   • BCapacity planning, TCO calculations, opex/capex allocation
   • CCapacity planning, utilization measurement, data center expansion
   • DData Center expansion, TCO calculations, utilization measurement

   Show answer

   Answer: B

   Explanation

   Correct Answer B Capacity planning, TCO calculations, opex/capex allocation From the case study, it can conclude that Management (CXO) all concern rapid provision of resources (infrastructure) for growing as well as cost management, such as Cost optimization in Infrastructure, trade up front capital expenditures (Capex) for ongoing operating expenditures (Opex), and Total cost of ownership (TCO)

   Source: Adapted from community discussion — sri007

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7. Question 327 · 335 in bank

   To speed up data retrieval, more vehicles will be upgraded to cellular connections and be able to transmit data to the ETL process. The current FTP process is error-prone and restarts the data transfer from the start of the file when connections fail, which happens often. You want to improve the reliability of the solution and minimize data transfer time on the cellular connections. What should you do?

   • AUse one Google Container Engine cluster of FTP servers. Save the data to a Multi-Regional bucket. Run the ETL process using data in the bucket
   • BUse multiple Google Container Engine clusters running FTP servers located in different regions. Save the data to Multi-Regional buckets in US, EU, and Asia. Run the ETL process using the data in the bucket
   • CDirectly transfer the files to different Google Cloud Multi-Regional Storage bucket locations in US, EU, and Asia using Google APIs over HTTP(S). Run the ETL process using the data in the bucket
   • DDirectly transfer the files to a different Google Cloud Regional Storage bucket location in US, EU, and Asia using Google APIs over HTTP(S). Run the ETL process to retrieve the data from each Regional bucket

   Show answer

   Answer: D

   Explanation

   c) Multi-Region Name Multi-Region Description asia Data centers in Asia eu Data centers in the European Union1 us Data centers in the United States multi-region is a large geographic area, such as the United States, that contains two or more geographic places.

   Source: Adapted from community discussion — dabrat

   References

   • Google Cloud documentation
   • Google Cloud documentation

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8. Question 328 · 335 in bank

   TerramEarth's 20 million vehicles are scattered around the world. Based on the vehicle's location, its telemetry data is stored in a Google Cloud Storage (GCS) regional bucket (US, Europe, or Asia). The CTO has asked you to run a report on the raw telemetry data to determine why vehicles are breaking down after 100 K miles. You want to run this job on all the data. What is the most cost-effective way to run this job?

   • AMove all the data into 1 zone, then launch a Cloud Dataproc cluster to run the job
   • BMove all the data into 1 region, then launch a Google Cloud Dataproc cluster to run the job
   • CLaunch a cluster in each region to preprocess and compress the raw data, then move the data into a multi-region bucket and use a Dataproc cluster to finish the job
   • DLaunch a cluster in each region to preprocess and compress the raw data, then move the data into a region bucket and use a Cloud Dataproc cluster to finish the job

   Show answer

   Answer: D

   Explanation

   I will look at it from a different perspective; A, B says "move all data" but analysis will try to reveal breaking down after 100K miles so there is no point of transferring data of the vehicles with less than 100K milage. Therefore, transferring all data is just waste of time and money. There is one thing for sure here. If we move/copy data between continents it will cost us money therefore compressing the data before copying to another region/continent makes sense. Preprocessing also makes sense because we probably want to process smaller chunks of data first (remember 100K milage). So now type of target bucket; multi-region or standard? multi-region is good for high-availability and low latency with a little more cost however question doesn't require any of these features. Therefore I think standard storage option is good to go given lower costs are always better. So my answer would be D

   Source: Adapted from community discussion — cetanx

   References

   • Google Cloud documentation

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9. Question 329 · 335 in bank

   TerramEarth has equipped all connected trucks with servers and sensors to collect telemetry data. Next year they want to use the data to train machine learning models. They want to store this data in the cloud while reducing costs. What should they do?

   • AHave the vehicle's computer compress the data in hourly snapshots, and store it in a Google Cloud Storage (GCS) Nearline bucket
   • BPush the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Google BigQuery
   • CPush the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Cloud Bigtable
   • DHave the vehicle's computer compress the data in hourly snapshots, and store it in a GCS Coldline bucket

   Show answer

   Answer: D

   Explanation

   vote for D.. Between storing inside BigQuery vs Coldline (Since is meant for next year).. Bigquery pricing after storing data over 90 days is $0.010 per GB . Coldline storage pricing is $0.007 per GB (for storage) plus $0.02 per GB (for data retrieval). No additional charge for loading data into BigQuery.. Thus adding both storage and retrieval is still cheaper than storing under BigQuery. Ref: 1)https://cloud.google.com/storage/pricing 2)https://cloud.google.com/bigquery/pricing

   Source: Adapted from community discussion — skywalker

   References

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

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10. Question 330 · 335 in bank

    For this question, refer to the TerramEarth case study. To be compliant with European GDPR regulation, TerramEarth is required to delete data generated from its European customers after a period of 36 months when it contains personal data. In the new architecture, this data will be stored in both Cloud Storage and BigQuery. What should you do?

    • ACreate a BigQuery table for the European data, and set the table retention period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.
    • BCreate a BigQuery table for the European data, and set the table retention period to 36 months. For Cloud Storage, use gsutil to create a SetStorageClass to NONE action when with an Age condition of 36 months.
    • CCreate a BigQuery time-partitioned table for the European data, and set the partition expiration period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.
    • DCreate a BigQuery time-partitioned table for the European data, and set the partition expiration period to 36 months. For Cloud Storage, use gsutil to create a SetStorageClass to NONE action with an Age condition of 36 months.

    Show answer

    Answer: C

    Explanation

    I thought C was correct. SetStorageClass could not be set to NONE. After data expired, data should be deleted not table. any comment?

    Source: Adapted from community discussion — KouShikyou

    References

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

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PCA practice, question 321. 335 questions available.