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Google Associate Data Practitioner Sample Questions:

1. You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

A) Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
B) Use Cloud Composer to orchestrate the Spark job and email the report.
C) Use Cloud Run functions to trigger the Spark job and email the report.
D) Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.

2. You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage.
The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?

A) ETL
B) EL
C) ETLT
D) ELT

3. Your company uses Looker to generate and share reports with various stakeholders. You have a complex dashboard with several visualizations that needs to be delivered to specific stakeholders on a recurring basis, with customized filters applied for each recipient. You need an efficient and scalable solution to automate the delivery of this customized dashboard. You want to follow the Google- recommended approach. What should you do?

A) Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
B) Use the Looker Scheduler with a user attribute filter on the dashboard, and send the dashboard with personalized filters to each stakeholder based on their attributes.
C) Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.
D) Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.

4. 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 Composer operators.
B) Use Dataflow pipelines.
C) Use Dataform workflows.
D) Use Cloud Data Fusion pipelines.

5. Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

A) Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
B) Use Cloud Tasks to schedule and run the jobs asynchronously.
C) Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
D) Use Cloud Scheduler to schedule the jobs to run.

Solutions: