Associate-Data-Practitioner問題集、Google実際の試験問題

質問 1

Your organization uses Dataflow pipelines to process real-time financial transactions. You discover that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

A. Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.

B. Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.

C. Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.

D. Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.

正解: B

解説: (PassTest メンバーにのみ表示されます)

質問 2

You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage.
The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?

A. ELT

B. ETLT

C. EL

D. ETL

正解: D

解説: (PassTest メンバーにのみ表示されます)

質問 3

You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?

A. Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.

B. Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.

C. Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.

D. Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.

正解: A

解説: (PassTest メンバーにのみ表示されます)

質問 4

Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?

A. Enable Object Versioning.

B. Set a bucket retention policy.

C. Set temporary object holds.

D. Change the bucket storage class to Archive.

正解: B

解説: (PassTest メンバーにのみ表示されます)

質問 5

You have an existing weekly Storage Transfer Service transfer job from Amazon S3 to a Nearline Cloud Storage bucket in Google Cloud. Each week, the job moves a large number of relatively small files. As the number of files to be transferred each week has grown over time, you are at risk of no longer completing the transfer in the allocated time frame. You need to decrease the total transfer time by replacing the process.
Your solution should minimize costs where possible. What should you do?

A. Create a batch Dataflow job that is scheduled weekly to migrate the data from Amazon S3 to Cloud Storage.

B. Create an agent-based transfer job that utilizes multiple transfer agents on Compute Engine instances.

C. Create parallel transfer jobs using include and exclude prefixes.

D. Create a transfer job using the Google Cloud CLI, and specify the Standard storage class with the - custom-storage-class flag.

正解: C

解説: (PassTest メンバーにのみ表示されます)

質問 6

You work for a healthcare company that has a large on-premises data system containing patient records with personally identifiable information (PII) such as names, addresses, and medical diagnoses. You need a standardized managed solution that de-identifies PII across all your data feeds prior to ingestion to Google Cloud. What should you do?

A. Use Cloud Data Fusion to transform the data. Store the cleaned data in BigQuery.

B. Load the data into BigQuery, and inspect the data by using SQL queries. Use Dataflow to transform the data and remove any errors.

C. Use Apache Beam to read the data and perform the necessary cleaning and transformation operations.Store the cleaned data in BigQuery.

D. Use Cloud Run functions to create a serverless data cleaning pipeline. Store the cleaned data in BigQuery.

正解: A

解説: (PassTest メンバーにのみ表示されます)

Google Cloud Associate Data Practitioner - Associate-Data-Practitioner 模擬練習