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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. You are storing data in Cloud Storage for a machine learning project. The data is frequently accessed during the model training phase, minimally accessed after 30 days, and unlikely to be accessed after 90 days. You need to choose the appropriate storage class for the different stages of the project to minimize cost. What should you do?

A) Store the data in Standard storage during the model training phase. Transition the data to Durable Reduced Availability (DRA) storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
B) Store the data in Nearline storage during the model training phase. Transition the data to Coldline storage 30 days after model deployment, and to Archive storage 90 days after model deployment.
C) Store the data in Standard storage during the model training phase. Transition the data to Nearline storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
D) Store the data in Nearline storage during the model training phase. Transition the data to Archive storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.

2. Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability.
What should you do?

A) Create a single-region bucket in each of the two regions, and use Storage Transfer Service to replicate the data across the buckets in both regions.
B) Create a multi-region bucket, and upload the files to this bucket.
C) Create a single-region bucket in each of the two regions, and use the gcloud storage command to replicate the data across the buckets in both regions.
D) Create a dual-region bucket, and upload the files to this bucket.

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

4. You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?

A) Compare the confusion matrix.
B) Compare the two different models.
C) Evaluate the data skewness.
D) Evaluate data drift.

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

Solutions: