Free Google Associate-Data-Practitioner Exam Questions

Question 1

Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organizations storage costs. You want to use the most efficient approach while minimizing cost. What should you do?

A : Create a scheduled query that periodically runs an update statement in SQL that sets the œdeleted" column to œyes for data that is more than one year old. Create a view that filters out rows that have been marked deleted.

B : Create a view that filters out rows that are older than one year.

C : Require users to specify a partition filter using the alter table statement in SQL.

D : Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.

Answer: D

Question 2

Your organization sends IoT event data to a Pub/Sub topic. Subscriber applications read and perform transformations on the messages before storing them in the data warehouse. During particularly busy times when more data is being written to the topic, you notice that the subscriber applications are not acknowledging messages within the deadline. You need to modify your pipeline to handle these activity spikes and continue to process the messages. What should you do?

A : Retry messages until they are acknowledged.

B : Implement flow control on the subscribers

C : Forward unacknowledged messages to a dead-letter topic.

D : Seek back to the last acknowledged message.

Answer: B

Question 3

You manage an ecommerce website that has a diverse range of products. You need to forecast future product demand accurately to ensure that your company has sufficient inventory to meet customer needs and avoid stockouts. Your company's historical sales data is stored in a BigQuery table. You need to create a scalable solution that takes into account the seasonality and historical data to predict product demand. What should you do?

A : Use the historical sales data to train and create a BigQuery ML time series model. Use the ML.FORECAST function call to output the predictions into a new BigQuery table.

B : Use Colab Enterprise to create a Jupyter notebook. Use the historical sales data to train a custom prediction model in Python.

C : Use the historical sales data to train and create a BigQuery ML linear regression model. Use the ML.PREDICT function call to output the predictions into a new BigQuery table.

D : Use the historical sales data to train and create a BigQuery ML logistic regression model. Use the ML.PREDICT function call to output the predictions into a new BigQuery table.

Answer: A

Question 4

You manage an ecommerce website that has a diverse range of products. You need to forecast future product demand accurately to ensure that your company has sufficient inventory to meet customer needs and avoid stockouts. Your company's historical sales data is stored in a BigQuery table. You need to create a scalable solution that takes into account the seasonality and historical data to predict product demand. What should you do?

A : Use the historical sales data to train and create a BigQuery ML time series model. Use the ML.FORECAST function call to output the predictions into a new BigQuery table.

B : Use Colab Enterprise to create a Jupyter notebook. Use the historical sales data to train a custom prediction model in Python.

C : Use the historical sales data to train and create a BigQuery ML linear regression model. Use the ML.PREDICT function call to output the predictions into a new BigQuery table.

D : Use the historical sales data to train and create a BigQuery ML logistic regression model. Use the ML.PREDICT function call to output the predictions into a new BigQuery table.

Answer: A

Question 5

You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional dat a. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?

A : Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.

B : Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.

C : Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.

D : Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.

Answer: C