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Google Associate Data Practitioner Sample Questions:

1. Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

A) Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
B) Use Cloud Tasks to schedule and run the jobs asynchronously.
C) Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
D) Use Cloud Scheduler to schedule the jobs to run.

2. You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage.
The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?

A) ETLT
B) ETL
C) EL
D) ELT

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 table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.
D) 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.

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

5. 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 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.
B) Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
C) Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
D) Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.

Solutions: