[2026年更新]ADP試験問題集でテストエンジン練習テスト問題 [Q27-Q46]

Rate this post

[2026年更新]ADP試験問題集でテストエンジン練習テスト問題

合格できるADP試験[2026年09月26日]最新66問題

27、Your organization has several datasets in their data warehouse in BigQuery. Several analyst teams in different departments use the datasets to run queries. Your organization is concerned about the variability of their monthly BigQuery costs. You need to identify a solution that creates a fixed budget for costs associated with the queries run by each department. What should you do?

Create a custom quota for each analyst in BigQuery.

Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.

Assign each analyst to a separate project associated with their department. Create a single reservation by using BigQuery editions. Assign all projects to the reservation.

Assign each analyst to a separate project associated with their department. Create a single reservation for each department by using BigQuery editions. Create assignments for each project in the appropriate reservation.

28、Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability.
What should you do?

Create a dual-region bucket, and upload the files to this bucket.

Create a single-region bucket in each of the two regions, and use the gcloud storage command to replicate the data across the buckets in both regions.

Create a multi-region bucket, and upload the files to this bucket.

Create a single-region bucket in each of the two regions, and use Storage Transfer Service to replicate the data across the buckets in both regions.

29、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?

Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.

Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.

Use Dataflow to create a streaming pipeline that includes validation and transformation steps.

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.

30、You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline’s performance and be alerted if errors occur. What should you do?

Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.

Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.

Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.

Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.

31、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?

Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.

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.

Grant each partner read access to the BigQuery dataset by using 1AM roles.

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.

32、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?

Store the data in Cloud Storage using Nearline storage.

Store the data in Cloud Storage using Coldline storage.

Store the data in Cloud Storage using Standard storage.

Store the data in Cloud Storage using Archive storage.

33、Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?

Use a Transfer Appliance.

Use Cloud Storage FUSE.

Use Storage Transfer Service.

Use gcloud storage commands.

34、Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization’s storage costs. You want to use the most efficient approach while minimizing cost. What should you do?

Create a scheduled query that periodically runs an update statement in SQL that sets the “deleted” column to “yes” for data that is more than one year old. Create a view that filters out rows that have been marked deleted.

Create a view that filters out rows that are older than one year.

Require users to specify a partition filter using the alter table statement in SQL.

Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.

35、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?

ELT

ETL

ETLT

36、Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company’s products in your Looker Explores and dashboards.
You need to implement a solution quickly and efficiently. What should you do?

Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.

Define a new measure that calculates the profit margin by using the existing revenue and cost fields.

Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).

Apply a filter to only show products with a positive profit margin.

37、You need to transfer approximately 300 TB of data from your company’s on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?

Use Cloud Client Libraries to transfer the data over the internet.

Use the gcloud storage command to transfer the data over the internet.

Compress the data, upload it to multiple cloud storage providers, and then transfer the data to CloudStorage.

Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.

38、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?

Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.

Create BigQuery scheduled queries to define the transformations in SQL.

Use Dataform to define the transformations in SQLX.

Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.

39、You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue. What should you do?

Use Cloud Logging to identify error groups in the pipeline’s logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.

Use Cloud Logging to create a chart displaying the pipeline’s error logs. Use Metrics Explorer to validate the findings from the chart.

Use Cloud Logging to view error messages in the pipeline’s logs. Use Cloud Monitoring to analyze the pipeline’s metrics, such as CPU utilization and memory usage.

Use the Dataflow job monitoring interface to check the pipeline’s status every hour. Use Cloud Profiler to analyze the pipeline’s metrics, such as CPU utilization and memory usage.

40、You recently inherited a task for managing Dataflow streaming pipelines in your organization and noticed that proper access had not been provisioned to you. You need to request a Google-provided IAM role so you can restart the pipelines. You need to follow the principle of least privilege. What should you do?

Request the Dataflow Developer role.

Request the Dataflow Viewer role.

Request the Dataflow Worker role.

Request the Dataflow Admin role.

41、You are working on a project that requires analyzing daily social media dat a. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?

Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.

Use Cloud Run functions to transform and load the data into BigQuery.

Use Dataflow to transform the data and write the transformed data to BigQuery.

Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.

42、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?

Use authorized views to share query results with the payroll specialist.

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.

Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.

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.

43、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?

Enable Object Versioning.

Change the bucket storage class to Archive.

Set a bucket retention policy.

Set temporary object holds.

44、You work for a home insurance company. You are frequently asked to create and save risk reports with charts for specific areas using a publicly available storm event dataset. You want to be able to quickly create and re- run risk reports when new data becomes available. What should you do?

Export the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.

Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.

Reference and query the storm event dataset using SQL in BigQuery Studio. Export the results to Google Sheets, and use cell data in the worksheets to create charts.

Reference and query the storm event dataset using SQL in a Colab Enterprise notebook. Display the table results and document with Markdown, and use Matplotlib to create charts.

45、You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.

Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.

Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.

Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.

46、Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label. Which BigQuery ML query should you use?

CREATE OR REPLACE MODEL churn_prediction_model OPTIONS(model_uype=’logisric_reg’) AS SELECT * from cusromer_data;

CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=’ logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;

CREATE OR REPLACE MODEL churn_prediction_model options (model type=’logistic_reg’) AS select churned as label FROM customer_data;

CREATE OR REPLACE MODEL churn_prediction_model options(model_type=’logistic_reg*) as select ‘ except(churned) FROM customer data;

Google ADPリアルな2026年最新の知能問題集模擬試験問題集：https://www.goshiken.com/Google/ADP-mondaishu.html

Tags: ADPウェブトレーニング, ADP技術内容, ADP日本語版問題解説, ADP無料過去問, ADP試験関連情報, ADP資格トレーニング, ADP資格練習

[2026年更新]ADP試験問題集でテストエンジン練習テスト問題 [Q27-Q46]

コメントを残すコメントをキャンセル

関連認証

ADP 練習テスト

最近の投稿

アーカイブ

カテゴリー

[2026年更新]ADP試験問題集でテストエンジン練習テスト問題 [Q27-Q46]

コメントを残す コメントをキャンセル

関連認証

ADP 練習テスト

最近の投稿

アーカイブ

カテゴリー

コメントを残すコメントをキャンセル