Google Associate Data Practitionerの合格を目指しているものの、仕事や学業に追われてまとまった学習時間を確保できない方は少なくありません。PassTestのADP練習問題は66問の精選された問題で効率的に実力を磨けるよう構成されており、忙しい方でもスキマ時間を活かして試験対策を進められます。

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Google Associate Data Practitionerの疑問にお答えします

Google Associate Data Practitioner 認定 ADP 試験問題：

問題 #1

You need to create a new data pipeline. You want a serverless solution that meets the following requirements:
* Data is streamed from Pub/Sub and is processed in real-time.
* Data is transformed before being stored.
* Data is stored in a location that will allow it to be analyzed with SQL using Looker.
Which Google Cloud services should you recommend for the pipeline?

A. Cloud Composer Cloud SQL for MySQL
B. Dataflow BigQuery
C. BigQuery Analytics Hub
D. Dataproc Serverless Bigtable

問題 #2

Your company has an on-premises file server with 5 TB of data that needs to be migrated to Google Cloud.
The network operations team has mandated that you can only use up to 250 Mbps of the total available bandwidth for the migration. You need to perform an online migration to Cloud Storage. What should you do?

A. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the -- daisy-chain option.
B. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.
C. Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
D. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the --no- clobber option.

問題 #3

You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

A. Use Dataproc to run the transformations on a cluster.
B. Use Dataflow with a custom template for the transformation logic.
C. Use Dataform to define the transformations in SQLX.
D. Use Cloud Data Fusion to visually design and manage the pipeline.

問題 #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. Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.
B. Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
C. Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
D. Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.

問題 #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 Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
D. 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.

解説：