本番の時間配分に不安がある場合は、ADPの模擬試験形式で解答ペースを確認する練習が役立ちます。Google Associate Data Practitionerに対応したFast2testの66問の問題集なら、知識の定着と時間管理をあわせて鍛えられます。

ADP試験対策に選べる3つの学習形式

• PDF版：印刷して持ち運べる形式で、専門家が作成した内容をすぐにダウンロードして確認できます。学習場所を選ばず、365日間の無料更新と無料PDFサンプルに対応しています。
• Desktop Test Engine：インストールして使用するソフトウェアで、実際の試験環境を再現した2つの練習モードを利用できます。オフライン学習に対応し、Windowsで動作します。
• Online Test Engine：ブラウザーからすぐにアクセスでき、学習履歴と成績を確認できます。Windows、Mac、Android、iOSで利用できます。

Fast2testの安心な購入・利用サポート

• McAfeeのセキュリティサービスにより、お客様の情報を安全に取り扱います。
• ご購入後365日間は無料で更新版をご利用いただけます。更新期間の終了後も、50%割引で継続更新が可能です。
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Google Associate Data Practitionerの試験情報

Google ADP 試験概要:

ADP試験の出題範囲

Google ADP 試験シラバストピック:

Google ADPのよくある質問と回答

Google Associate Data Practitioner 認定 ADP 試験問題：

問題 #1

You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

A. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
B. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
C. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
D. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.

問題 #2

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. 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.
B. Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
C. 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.
D. Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.

問題 #3

You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

A. Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
B. Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.
C. Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.
D. Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.

問題 #4

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?

A. Create a dual-region bucket, and upload the files to this bucket.
B. 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.
C. 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.
D. Create a multi-region bucket, and upload the files to this bucket.

問題 #5

You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

A. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
B. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
C. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
D. Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.

解説：