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Google ADP Exam Syllabus Topics:

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 Dataproc workflow templates to define and schedule the Spark job, and to email the report.
D) Use Cloud Run functions to trigger the Spark job and email the report.

2. 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.

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

4. Your organization's ecommerce website collects user activity logs using a Pub/Sub topic. Your organization's leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?

A) Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.
B) Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting
C) Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
D) Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.

5. 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) Grant the partners the bigquery.user IAM role on the BigQuery project.
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.

Solutions: