Google Google Cloud Certified ADP

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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?

A) Create a Cloud Run function that runs daily and deletes files older than seven days.
B) Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
C) Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
D) Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.

2. Your organization has several datasets in their data warehouse in BigQuery. Several analyst teams in different departments use the datasets to run queries. Your organization is concerned about the variability of their monthly BigQuery costs. You need to identify a solution that creates a fixed budget for costs associated with the queries run by each department. What should you do?

A) Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.
B) Assign each analyst to a separate project associated with their department. Create a single reservation by using BigQuery editions. Assign all projects to the reservation.
C) Assign each analyst to a separate project associated with their department. Create a single reservation for each department by using BigQuery editions. Create assignments for each project in the appropriate reservation.
D) Create a custom quota for each analyst in 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) Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function 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) Store all data in a single BigQuery table without partitioning or lifecycle policies.
D) 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.

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

5. 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 Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.
B) Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.
C) Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
D) 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

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