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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

A) Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
B) Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
C) Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
D) Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.

2. Your company has an on-premises file server with 5 TB of data that needs to be migrated to Google Cloud.
The network operations team has mandated that you can only use up to 250 Mbps of the total available bandwidth for the migration. You need to perform an online migration to Cloud Storage. What should you do?

A) Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the --no- clobber option.
B) Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the -- daisy-chain option.
C) Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
D) Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.

3. Your organization uses scheduled queries to perform transformations on data stored in BigQuery. You discover that one of your scheduled queries has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

A) Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.
B) Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.
C) Set up a log sink using the gcloud CLI to export BigQuery audit logs to BigQuery. Query those logs to identify the error associated with the failed job I
D) Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.

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

5. 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 Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
C) Use Cloud Run functions to transform and load the data into BigQuery.
D) Use Dataflow to transform the data and write the transformed data to BigQuery.

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