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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?

A) Use the gcloud storage command to transfer the data over the internet.
B) Compress the data, upload it to multiple cloud storage providers, and then transfer the data to CloudStorage.
C) Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.
D) Use Cloud Client Libraries to transfer the data over the internet.

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

3. Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?

A) Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.
B) Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.
C) Create a BigQuery materialized view that performs a cross join across all of the regional sales tables. Use the row_number() window function to query the new materialized view.
D) Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.

4. Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?

A) Create a saved query in the BigQuery console that uses the SUM() function and the DATE_SUB() function. Re-run the saved query every month, and save the results to a BigQuery table.
B) Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.
C) Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.
D) Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.

5. 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) 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.
B) Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
C) Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
D) Use Dataflow to create a streaming pipeline that includes validation and transformation steps.

Solutions: