Professional-Cloud-Architect Exam Dumps - Google Certified Professional - Cloud Architect (GCP)

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Question # 57

Refer to the Altostrat Media case study for the following solution regarding the performance analysis of their media processing pipeline.

Altostrat needs to analyze the performance of its media processing pipeline running on Java-based Cloud Run function. You need to select the most effective tool for the task. What should you do?

Query logs in Cloud Logging.

Analyze the data via Cloud Profiler.

Instrument the code to use Cloud Trace.

Inspect data from Snapshot Debugger.

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Question # 58

For this question, refer to the TerramEarth case study

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?

Build or leverage an OAuth-compatible access control system.

Build SAML 2.0 SSO compatibility into your authentication system.

Restrict data access based on the source IP address of the partner systems.

Create secondary credentials for each dealer that can be given to the trusted third party.

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Question # 59

For this question, refer to the TerramEarth case study.

TerramEarth ' s CTO wants to use the raw data from connected vehicles to help identify approximately when a vehicle in the development team to focus their failure. You want to allow analysts to centrally query the vehicle data. Which architecture should you recommend?

Option A

Option B

Option C

Option D

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Question # 60

For this question refer to the TerramEarth case study

Operational parameters such as oil pressure are adjustable on each of TerramEarth ' s vehicles to increase their efficiency, depending on their environmental conditions. Your primary goal is to increase the operating efficiency of all 20 million cellular and unconnected vehicles in the field How can you accomplish this goal?

Have your engineers inspect the data for patterns, and then create an algorithm with rules that make operational adjustments automatically.

Capture all operating data, train machine learning models that identify ideal operations, and run locally to make operational adjustments automatically.

Implement a Google Cloud Dataflow streaming job with a sliding window, and use Google Cloud Messaging (GCM) to make operational adjustments automatically.

Capture all operating data, train machine learning models that identify ideal operations, and host in Google Cloud Machine Learning (ML) Platform to make operational adjustments automatically.

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Question # 61

For this question, refer to the TerramEarth case study.

TerramEarth has equipped unconnected trucks with servers and sensors to collet 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?

Have the vehicleâ€™ computer compress the data in hourly snapshots, and store it in a Google Cloud storage (GCS) Nearline bucket.

Push the telemetry data in Real-time to a streaming dataflow job that compresses the data, and store it in Google BigQuery.

Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Cloud Bigtable.

Have the vehicle ' s computer compress the data in hourly snapshots, a Store it in a GCS Coldline bucket.

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Question # 62

For this question, refer to the TerramEarth case study.

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?

Use 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.

Use 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.

Directly 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.

Directly 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.

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Question # 63

You are migrating third-party applications from optimized on-premises virtual machines to Google Cloud. You are unsure about the optimum CPU and memory options. The application have a consistent usage patterns across multiple weeks. You want to optimize resource usage for the lowest cost. What should you do?

Create a Compute engine instance with CPU and Memory options similar to your applicationâ€™s current on-premises virtual machine. Install the cloud monitoring agent, and deploy the third party application. Run a load with normal traffic levels on third party application and follow the Rightsizing Recommendations in the Cloud Console

Create an App Engine flexible environment, and deploy the third party application using a Docker file and a custom runtime. Set CPU and memory options similar to your applicationâ€™s current on-premises virtual machine in the app.yaml file.

Create an instance template with the smallest available machine type, and use an image of the third party application taken from the current on-premises virtual machine. Create a managed instance group that uses average CPU to autoscale the number of instances in the group. Modify the average CPU utilization threshold to optimize the number of instances running.

Create multiple Compute Engine instances with varying CPU and memory options. Install the cloud monitoring agent and deploy the third-party application on each of them. Run a load test with high traffic levels on the application and use the results to determine the optimal settings.

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Question # 64

You write a Python script to connect to Google BigQuery from a Google Compute Engine virtual machine. The script is printing errors that it cannot connect to BigQuery. What should you do to fix the script?

Install the latest BigQuery API client library for Python

Run your script on a new virtual machine with the BigQuery access scope enabled

Create a new service account with BigQuery access and execute your script with that user

Install the bq component for gccloud with the command gcloud components install bq.

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