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Google Associate Data Practitioner Sample Questions:

1. You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

A) Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
B) Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
C) Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
D) Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.

2. You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?

A) Compare the confusion matrix.
B) Evaluate the data skewness.
C) Compare the two different models.
D) Evaluate data drift.

3. You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

A) Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.
B) Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.
C) Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.
D) Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.

4. 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) Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
C) 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.
D) Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.

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) Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
D) Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.

Solutions: