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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

Question 1

Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

A. Store the data in Cloud Storage using Standard storage.
B. Store the data in Cloud Storage using Nearline storage.
C. Store the data in Cloud Storage using Archive storage.
D. Store the data in Cloud Storage using Coldline storage.

Question 2

Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label. Which BigQuery ML query should you use?

A. CREATE OR REPLACE MODEL churn_prediction_model options (model type='logistic_reg') AS select churned as label FROM customer_data;
B. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;
C. CREATE OR REPLACE MODEL churn_prediction_model options(model_type='logistic_reg*) as select ' except(churned) FROM customer data;
D. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS(model_uype='logisric_reg') AS SELECT * from cusromer_data;

Question 3

You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

A. Use Vertex Al Workbench to develop a custom model for purchase prediction.
B. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
C. Use Colab Enterprise to develop a custom model for purchase prediction.
D. Use BigQuery ML to create a logistic regression model for purchase prediction.

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

Question 5

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. Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
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. 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.

Solutions: