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Google Associate Data Practitioner Sample Questions:

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

2. You are working on a project that requires analyzing daily social media dat a. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?

A) Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
B) Use Cloud Run functions to transform and load the data into BigQuery.
C) Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
D) Use Dataflow to transform the data and write the transformed data to BigQuery.

3. You have a BigQuery dataset containing sales dat
a. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum. What should you do?

A) Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.
B) Partition a BigQuery table by month. After 6 months, export the data to Coldline storage. Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.
C) Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.
D) Store all data in a single BigQuery table without partitioning or lifecycle policies.

4. 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 Dataproc to run the transformations on a cluster.
C) Use Dataflow with a custom template for the transformation logic.
D) Use Dataform to define the transformations in SQLX.

5. Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?

A) Change the bucket storage class to Archive.
B) Enable Object Versioning.
C) Set a bucket retention policy.
D) Set temporary object holds.

Solutions: