Google ADP 試験概要:
| 認定ベンダー: | |
| 試験名: | Google Cloud アソシエイト・データプラクティショナー |
| 試験番号: | GCP-ADP |
| 関連資格: | Google Cloud プロフェッショナル・データエンジニア Google Cloud プロフェッショナル・データアナリスト |
| 認定の有効期間: | 2年間 |
| 試験形式: | 択一式, 複数選択式 |
| 試験時間: | 120 minutes |
| 出題数: | 50-60 |
| 対応言語: | 英語, 日本語 |
| 受験料: | $125 USD |
| 合格点: | 70% |
| 推奨トレーニング: | Google Cloud におけるデータエンジニアリング入門 Google Cloud アソシエイト・データプラクティショナー向け学習ロードマップ |
| 受験申し込み: | Google Cloud 認定資格試験の登録 |
| サンプル問題: | Google ADP サンプル問題 |
| 受験方法: | オンライン監督付き（自宅等からの遠隔受験）または認定試験会場での会場監督付き受験 |
| 前提条件: | 受験に必須の前提条件はなし。推奨される経験として、Google Cloud 上でデータ業務に6か月以上携わった実務経験があること |
| 公式シラバスのURL: | https://cloud.google.com/learn/certification/data-practitioner |
Google ADP 試験シラバストピック:
| セクション | 比重 | 目標 |
|---|---|---|
| データパイプラインの調整・実行管理 | 18% | - パイプラインの自動化とスケジューリング
|
| データの分析と提示 | 27% | - データの調査と分析
|
| データの準備と取り込み | 30% | - ストレージソリューションの選定
|
| データの管理とガバナンス | 25% | - 法令遵守とガバナンス体制
|
Google Associate Data Practitioner 認定 ADP 試験問題：
1. You manage a large amount of data in Cloud Storage, including raw data, processed data, and backups. Your organization is subject to strict compliance regulations that mandate data immutability for specific data types.
You want to use an efficient process to reduce storage costs while ensuring that your storage strategy meets retention requirements. What should you do?
A) Move objects to different storage classes based on their age and access patterns. Use Cloud Key Management Service (Cloud KMS) to encrypt specific objects with customer-managed encryption keys (CMEK) to meet immutability requirements.
B) Configure lifecycle management rules to transition objects to appropriate storage classes based on access patterns. Set up Object Versioning for all objects to meet immutability requirements.
C) Use object holds to enforce immutability for specific objects, and configure lifecycle management rules to transition objects to appropriate storage classes based on age and access patterns.
D) Create a Cloud Run function to periodically check object metadata, and move objects to the appropriate storage class based on age and access patterns. Use object holds to enforce immutability for specific objects.
2. 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?
A) Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
B) Use Cloud Run functions to transform and load the data into BigQuery.
C) Use Dataflow to transform the data and write the transformed data to BigQuery.
D) Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
3. 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 Dataform to define the transformations in SQLX.
B) Create BigQuery scheduled queries to define the transformations in SQL.
C) Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.
D) Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
4. Following a recent company acquisition, you inherited an on- premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort. What should you do?
A) Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
B) Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
C) Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
D) Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.
5. You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?
A) Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
B) Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
C) Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.
D) Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
質問と回答：
| 質問 # 1 正解： C | 質問 # 2 正解： C | 質問 # 3 正解： A | 質問 # 4 正解： D | 質問 # 5 正解： C |
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