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Google Associate Data Practitioner Sample Questions:

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

2. 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) Compress the data, upload it to multiple cloud storage providers, and then transfer the data to CloudStorage.
B) Use the gcloud storage command to transfer the data over the internet.
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.

3. 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 (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;
B) CREATE OR REPLACE MODEL churn_prediction_model options (model type='logistic_reg') AS select 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;

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

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

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