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Google Associate Data Practitioner Sample Questions:

1. Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

A) Use Cloud Data Fusion pipelines.
B) Use Dataform workflows.
C) Use Cloud Composer operators.
D) Use Dataflow pipelines.

2. You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

A) Use Cloud Data Fusion to visually design and manage the pipeline.
B) Use Dataflow with a custom template for the transformation logic.
C) Use Dataform to define the transformations in SQLX.
D) Use Dataproc to run the transformations on a cluster.

3. You need to create a new data pipeline. You want a serverless solution that meets the following requirements:
* Data is streamed from Pub/Sub and is processed in real-time.
* Data is transformed before being stored.
* Data is stored in a location that will allow it to be analyzed with SQL using Looker.
Which Google Cloud services should you recommend for the pipeline?

A) Dataproc Serverless Bigtable
B) Dataflow BigQuery
C) Cloud Composer Cloud SQL for MySQL
D) BigQuery Analytics Hub

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 BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.
B) Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.
C) Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.
D) 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.

5. You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

A) Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.
B) Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
C) Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
D) Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.

Solutions: