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

2. 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) Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.
B) 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.
C) Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
D) Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.

3. You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?

A) Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
B) Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
C) Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
D) Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.

4. 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) Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
B) Create a view that filters out rows that are older than one year.
C) Require users to specify a partition filter using the alter table statement in SQL.
D) 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.

5. Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

A) Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
B) Create a single Explore with all sales metrics. Build the dashboard using this Explore.
C) Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
D) Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.

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