2026年にGoogle Associate Data Practitionerの取得を目指すなら、教材選びから更新サポートまで一貫したCertShikenのサービスが便利です。ADPの無料サンプル確認から66問の本格演習、365日間の無料更新まで、合格への道筋をワンストップで用意しています。

Google ADP 試験概要:

認定ベンダー:	Google Cloud
試験名:	Google Cloud Associate Data Practitioner
試験番号:	PCA-DP
試験形式:	複数選択, 単一選択
認定の有効期間:	24か月
受験料:	USD 100
関連資格:	Looker Data Analyst Google Cloud Professional Data Engineer Google Cloud Data Analytics
対応言語:	英語
合格点:	70
試験時間:	120 分
出題数:	50
サンプル問題:
受験方法:	オンライン監督試験または対面の試験会場
前提条件:	正式な前提条件はありませんが、データの概念とクラウドコンピューティングに関する基本的な知識が推奨されます
公式シラバスのURL:	https://cloud.google.com/certification/associate-data-practitioner

Google ADP 試験シラバストピック:

ADP試験の疑問を解決するFAQ

Google Associate Data Practitioner 認定 ADP 試験問題：

問題 #1

You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

A. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
B. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
C. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.
D. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.

問題 #2

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 a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.
C. 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.
D. Use authorized views to share query results with the payroll specialist.

問題 #3

Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?

A. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
B. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
C. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
D. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.

問題 #4

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?

A. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
B. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
C. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
D. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.

問題 #5

Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive dat a. What should you do?

A. Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
B. Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.
C. Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
D. Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.

解説：