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Google Associate Data Practitioner Sample Questions:

1. Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization's storage costs. You want to use the most efficient approach while minimizing cost. What should you do?

A) Require users to specify a partition filter using the alter table statement in SQL.
B) Create a scheduled query that periodically runs an update statement in SQL that sets the "deleted" column to "yes" for data that is more than one year old. Create a view that filters out rows that have been marked deleted.
C) Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
D) Create a view that filters out rows that are older than one year.

2. 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 BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
B) Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for 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.

3. 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 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
B) Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
C) 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.
D) 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.

4. You manage a large amount of data in Cloud Storage, including raw data, processed data, and backups. Your organization is subject to strict compliance regulations that mandate data immutability for specific data types.
You want to use an efficient process to reduce storage costs while ensuring that your storage strategy meets retention requirements. What should you do?

A) Configure lifecycle management rules to transition objects to appropriate storage classes based on access patterns. Set up Object Versioning for all objects to meet immutability requirements.
B) Use object holds to enforce immutability for specific objects, and configure lifecycle management rules to transition objects to appropriate storage classes based on age and access patterns.
C) Move objects to different storage classes based on their age and access patterns. Use Cloud Key Management Service (Cloud KMS) to encrypt specific objects with customer-managed encryption keys (CMEK) to meet immutability requirements.
D) Create a Cloud Run function to periodically check object metadata, and move objects to the appropriate storage class based on age and access patterns. Use object holds to enforce immutability for specific objects.

5. Following a recent company acquisition, you inherited an on- premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort. What should you do?

A) Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
B) Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.
C) Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
D) Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.

Solutions: