ADP無料問題集「Google Associate Data Practitioner」

質問 1

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 Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.

（C）Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.

（D）Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.

正解：D 解答を投票する

質問 2

Your organization uses Dataflow pipelines to process real-time financial transactions. You discover that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

（A）Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.

（B）Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.

（C）Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.

（D）Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.

正解：A 解答を投票する

質問 3

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）Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.

（D）Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.

正解：B 解答を投票する

質問 4

Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

（A）Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.

（B）Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.

（C）Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.

（D）Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.

正解：A 解答を投票する

質問 5

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?

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

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

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

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

正解：C 解答を投票する

質問 6

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

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

（D）Create BigQuery scheduled queries to define the transformations in SQL.

正解：A 解答を投票する

質問 7

Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

（A）Use Cloud Data Fusion pipelines.

（B）Use Cloud Composer operators.

（C）Use Dataform workflows.

（D）Use Dataflow pipelines.

正解：C 解答を投票する

ADP 無料問題集「Google Associate Data Practitioner」

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