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Google Associate Data Practitioner Sample Questions:

1. You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?

A) Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
B) Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.
C) Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
D) Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.

2. Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards.
You need to implement a solution quickly and efficiently. What should you do?

A) Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
B) Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
C) Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
D) Apply a filter to only show products with a positive profit margin.

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

4. You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

A) Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
B) Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.
C) Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
D) Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.

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

Solutions: