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Google Associate Data Practitioner Sample Questions:

1. Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud.
You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?

A) Migrate the Spark jobs to Dataproc on Compute Engine.
B) Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
C) Migrate the Spark jobs to Dataproc Serverless.
D) Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.

2. 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 Composer to orchestrate the Spark job and email the report.
B) Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to 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.

3. 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 dat a. You want to share the data while following Google-recommended practices. What should you do?

A) Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.
B) 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.
C) Grant the partners the bigquery.user IAM role on the BigQuery project.
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.

4. You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

A) Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
B) Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
C) Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
D) Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.

5. Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?

A) Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.
B) Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.
C) Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.
D) Create a saved query in the BigQuery console that uses the SUM() function and the DATE_SUB() function. Re-run the saved query every month, and save the results to a BigQuery table.

Solutions: