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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. Your organization has a BigQuery dataset that contains sensitive employee information such as salaries and performance reviews. The payroll specialist in the HR department needs to have continuous access to aggregated performance data, but they do not need continuous access to other sensitive dat a. You need to grant the payroll specialist access to the performance data without granting them access to the entire dataset using the simplest and most secure approach. What should you do?

A) Create row-level and column-level permissions and policies on the table that contains performance data in the dataset. Provide the payroll specialist with the appropriate permission set.
B) Use authorized views to share query results with the payroll specialist.
C) Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.
D) Create a SQL query with the aggregated performance data. Export the results to an Avro file in a Cloud Storage bucket. Share the bucket with the payroll specialist.

2. You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?

A) Update dataset privileges by using the SQL GRANT statement.
B) Export the data to Cloud Storage, and use signed URLs to authorize access.
C) Encrypt the data by using customer-managed encryption keys (CMEK).
D) Enable access control by using IAM roles.

3. You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?

A) Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
B) Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
C) Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.
D) Use Dataflow to create a streaming pipeline that includes validation and transformation steps.

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

5. You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:
1. Audio files from phone interactions with support agents that will be accessed during trainings.
2. CSV files of users' personally identifiable information (PII) that will be analyzed with SQL.
3. A large volume of small document files that will power other applications.
You need to select the appropriate tool for each data type given the required use case, while following Google- recommended practices. Which should you choose?

A) Filestore Bigtable BigQuery
B) Cloud Storage CloudSQL for PostgreSQL Bigtable
C) Filestore Cloud SQL for PostgreSQL Datastore
D) Cloud Storage BigQuery Firestore

Solutions: