ADP 試験問題を無料オンラインアクセス

Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

• A.Store the data in Cloud Storage using Nearline storage.
• B.Store the data in Cloud Storage using Standard storage.
• C.Store the data in Cloud Storage using Archive storage.
• D.Store the data in Cloud Storage using Coldline storage.

You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?

• A.Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
• B.Grant each partner read access to the BigQuery dataset by using 1AM roles.
• C.Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
• D.Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.

Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?

• A.Create a saved query in the BigQuery console that uses the SUM() function and the DATE_SUB() function. Re-run the saved query every month, and save the results to a BigQuery table.
• B.Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.
• C.Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.
• D.Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.

Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?

• A.Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
• B.Use Dataform to define the transformations in SQLX.
• C.Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.
• D.Create BigQuery scheduled queries to define the transformations in SQL.

You are designing a BigQuery data warehouse with a team of experienced SQL developers. You need to recommend a cost- effective, fully-managed, serverless solution to build ELT processes with SQL pipelines.
Your solution must include source code control, environment parameterization, and data quality checks. What should you do?

• A.Use Dataform to build, orchestrate, and monitor the pipelines.
• B.Use Dataproc to run MapReduce jobs for distributed data processing.
• C.Use Cloud Composer to orchestrate and run data workflows.
• D.Use Cloud Data Fusion to visually design and manage the pipelines.