Google Associate Data Practitioner Übungsprüfungen
Zuletzt aktualisiert am 27.04.2025- Prüfungscode: Associate Data Practitioner
- Prüfungsname: Google Cloud Associate Data Practitioner ( ADP Exam )
- Zertifizierungsanbieter: Google
- Zuletzt aktualisiert am: 27.04.2025
You have millions of customer feedback records stored in BigQuery. You want to summarize the data by using the large language model (LLM) Gemini. You need to plan and execute this analysis using the most efficient approach.
What should you do?
- A . Query the BigQuery table from within a Python notebook, use the Gemini API to summarize the
data within the notebook, and store the summaries in BigQuery. - B . Use a BigQuery ML model to pre-process the text data, export the results to Cloud Storage, and use the Gemini API to summarize the pre- processed data.
- C . Create a BigQuery Cloud resource connection to a remote model in Vertex Al, and use Gemini to summarize the data.
- D . Export the raw BigQuery data to a CSV file, upload it to Cloud Storage, and use the Gemini API to summarize the data.
You have millions of customer feedback records stored in BigQuery. You want to summarize the data by using the large language model (LLM) Gemini. You need to plan and execute this analysis using the most efficient approach.
What should you do?
- A . Query the BigQuery table from within a Python notebook, use the Gemini API to summarize the
data within the notebook, and store the summaries in BigQuery. - B . Use a BigQuery ML model to pre-process the text data, export the results to Cloud Storage, and use the Gemini API to summarize the pre- processed data.
- C . Create a BigQuery Cloud resource connection to a remote model in Vertex Al, and use Gemini to summarize the data.
- D . Export the raw BigQuery data to a CSV file, upload it to Cloud Storage, and use the Gemini API to summarize the data.
You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report.
What should you do?
- A . Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
- B . Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
- C . Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
- D . Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report.
What should you do?
- A . Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
- B . Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
- C . Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
- D . Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report.
What should you do?
- A . Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
- B . Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
- C . Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
- D . Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report.
What should you do?
- A . Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
- B . Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
- C . Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
- D . Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
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 organization’s 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 “deleted" column to “yes” 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.
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 data. 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.
Your organization plans to move their on-premises environment to Google Cloud. Your organization’s network bandwidth is less than 1 Gbps. You need to move over 500 ТВ of data to Cloud Storage securely, and only have a few days to move the data.
What should you do?
- A . Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.
- B . Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.
- C . Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.
- D . Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.
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 two different models.
- B . Evaluate the data skewness.
- C . Evaluate data drift.
- D . Compare the confusion matrix.