Google ADP日本語解説集、ADP資格認証攻略

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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Management	25%	- Storage and data organization 1. BigQuery datasets and tables management 2. Cloud Storage lifecycle policies - Data governance and security 1. Data lifecycle management 2. IAM and access control
Topic 2: Data Analysis and Presentation	27%	- Querying and analyzing data 1. Data aggregation and transformation 2. BigQuery SQL usage - Data visualization 1. Looker / Looker Studio reporting
Topic 3: Data Preparation and Ingestion	30%	- Data ingestion into Google Cloud services 1. Cloud Storage ingestion patterns 2. Streaming ingestion with Pub/Sub concepts
Topic 4: Data Pipeline Orchestration	18%	- Pipeline design and automation 1. Dataflow pipelines 2. Workflow orchestration concepts (e.g., Cloud Composer)

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Google Associate Data Practitioner 認定 ADP 試験問題 (Q17-Q22):

質問 # 17
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. Set a bucket retention policy.
B. Set temporary object holds.
C. Enable Object Versioning.
D. Change the bucket storage class to Archive.

正解：A

質問 # 18
Your company uses Looker to generate and share reports with various stakeholders. You have a complex dashboard with several visualizations that needs to be delivered to specific stakeholders on a recurring basis, with customized filters applied for each recipient. You need an efficient and scalable solution to automate the delivery of this customized dashboard. You want to follow the Google- recommended approach. What should you do?

A. Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
B. Use the Looker Scheduler with a user attribute filter on the dashboard, and send the dashboard with personalized filters to each stakeholder based on their attributes.
C. Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
D. Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.

正解：B

質問 # 19
You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?

A. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
B. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
C. Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.
D. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.

正解：D

質問 # 20
You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

A. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.
B. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.
C. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
D. Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.

正解：C

質問 # 21
Your organization has a BigQuery dataset that contains sensitive employee information such as salaries and performance reviews. The payroll specialist in the HR department needs to have continuous access to aggregated performance data, but they do not need continuous access to other sensitive dat a. You need to grant the payroll specialist access to the performance data without granting them access to the entire dataset using the simplest and most secure approach. What should you do?

A. Create a SQL query with the aggregated performance data. Export the results to an Avro file in a Cloud Storage bucket. Share the bucket with the payroll specialist.
B. Create row-level and column-level permissions and policies on the table that contains performance data in the dataset. Provide the payroll specialist with the appropriate permission set.
C. Use authorized views to share query results with the payroll specialist.
D. Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.

正解：C

質問 # 22
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