Google Cloud Associate Data Practitioner - Associate-Data-Practitioner

You are using your own data to demonstrate the capabilities of BigQuery to your organization's leadership team. You need to perform a one-time load of the files stored on your local machine into BigQuery using as little effort as possible. What should you do?

• A. Create a Dataflow job using the Apache Beam FileIO and BigQueryIO connectors with a local runner.
• B. Execute the bq load command on your local machine.
• C. Write and execute a Python script using the BigQuery Storage Write API library.
• D. Create a Dataproc cluster, copy the files to Cloud Storage, and write an Apache Spark job using the spark-bigquery-connector.

You need to create a data pipeline for a new application. Your application will stream data that needs to be enriched and cleaned. Eventually, the data will be used to train machine learning models. You need to determine the appropriate data manipulation methodology and which Google Cloud services to use in this pipeline. What should you choose?

• A. ELT; Cloud SQL -> Analytics Hub
• B. ELT; Cloud Storage -> Bigtable
• C. ETL; Cloud Data Fusion -> Cloud Storage
• D. ETL; Dataflow -> BigQuery

Your team needs to analyze large datasets stored in BigQuery to identify trends in user behavior. The analysis will involve complex statistical calculations, Python packages, and visualizations. You need to recommend a managed collaborative environment to develop and share the analysis. What should you recommend?

• A. Connect Google Sheets to BigQuery by using Connected Sheets. Share the Google Sheet with your team. Analyze the data and generate visualizations in Gooqle Sheets.
• B. Create a statistical model by using BigQuery ML. Share the query with your team. Analyze the data and generate visualizations in Looker Studio.
• C. Create a Colab Enterprise notebook and connect the notebook to BigQuery. Share the notebook with your team. Analyze the data and generate visualizations in Colab Enterprise.
• D. Create a Looker Studio dashboard and connect the dashboard to BigQuery. Share the dashboard with your team. Analyze the data and generate visualizations in Looker Studio.

Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?

• A. Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
• B. Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.
• C. Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
• D. Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.

Your organization has several datasets in BigQuery. The datasets need to be shared with your external partners so that they can run SQL queries without needing to copy the data to their own projects. You have organized each partner's data in its own BigQuery dataset. Each partner should be able to access only their data. You want to share the data while following Google-recommended practices. What should you do?

• A. Grant the partners the bigquery.user IAM role on the BigQuery project.
• B. Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.
• C. Create a Dataflow job that reads from each BigQuery dataset and pushes the data into a dedicated Pub
  /Sub topic for each partner. Grant each partner the pubsub. subscriber IAM role.
• D. Use Analytics Hub to create a listing on a private data exchange for each partner dataset. Allow each partner to subscribe to their respective listings.