Google Cloud Certified - Associate Data Practitioner v1.0

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Exam contains 72 questions

You are a data analyst at your organization. You have been given a BigQuery dataset that includes customer information. The dataset contains inconsistencies and errors, such as missing values, duplicates, and formatting issues. You need to effectively and quickly clean the data. What should you do?

  • A. Develop a Dataflow pipeline to read the data from BigQuery, perform data quality rules and transformations, and write the cleaned data back to BigQuery.
  • B. Use Cloud Data Fusion to create a data pipeline to read the data from BigQuery, perform data quality transformations, and write the clean data back to BigQuery.
  • C. Export the data from BigQuery to CSV files. Resolve the errors using a spreadsheet editor, and re-import the cleaned data into BigQuery.
  • D. Use BigQuery's built-in functions to perform data quality transformations.


Answer : D

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 custom quota for each analyst in BigQuery.
  • B. Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.
  • C. 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.
  • D. 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.


Answer : D

You manage a web application that stores data in a Cloud SQL database. You need to improve the read performance of the application by offloading read traffic from the primary database instance. You want to implement a solution that minimizes effort and cost. What should you do?

  • A. Use Cloud CDN to cache frequently accessed data.
  • B. Store frequently accessed data in a Memorystore instance.
  • C. Migrate the database to a larger Cloud SQL instance.
  • D. Enable automatic backups, and create a read replica of the Cloud SQL instance.


Answer : D

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.


Answer : A

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.


Answer : D

Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

  • A. Use Cloud Data Fusion pipelines.
  • B. Use Dataform workflows.
  • C. Use Dataflow pipelines.
  • D. Use Cloud Composer operators.


Answer : B

You manage a BigQuery table that is used for critical end-of-month reports. The table is updated weekly with new sales data. You want to prevent data loss and reporting issues if the table is accidentally deleted. What should you do?

  • A. Configure the time travel duration on the table to be exactly seven days. On deletion, re-create the deleted table solely from the time travel data.
  • B. Schedule the creation of a new snapshot of the table once a week. On deletion, re-create the deleted table using the snapshot and time travel data.
  • C. Create a clone of the table. On deletion, re-create the deleted table by copying the content of the clone.
  • D. Create a view of the table. On deletion, re-create the deleted table from the view and time travel data.


Answer : B

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

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.


Answer : C

You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of data. What should you do?

  • A. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
  • B. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
  • C. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
  • D. Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.


Answer : C

Your organization needs to implement near real-time analytics for thousands of events arriving each second in Pub/Sub. The incoming messages require transformations. You need to configure a pipeline that processes, transforms, and loads the data into BigQuery while minimizing development time. What should you do?

  • A. Use a Google-provided Dataflow template to process the Pub/Sub messages, perform transformations, and write the results to BigQuery.
  • B. Create a Cloud Data Fusion instance and configure Pub/Sub as a source. Use Data Fusion to process the Pub/Sub messages, perform transformations, and write the results to BigQuery.
  • C. Load the data from Pub/Sub into Cloud Storage using a Cloud Storage subscription. Create a Dataproc cluster, use PySpark to perform transformations in Cloud Storage, and write the results to BigQuery.
  • D. Use Cloud Run functions to process the Pub/Sub messages, perform transformations, and write the results to BigQuery.


Answer : A

Your organization needs to store historical customer order data. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

  • A. Store the data in Cloud Storage using Nearline storage.
  • B. Store the data in Cloud Storage using Coldline storage.
  • C. Store the data in Cloud Storage using Standard storage.
  • D. Store the data in Cloud Storage using Archive storage.


Answer : A

You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue. What should you do?

  • A. Use Cloud Logging to identify error groups in the pipeline's logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.
  • B. Use Cloud Logging to create a chart displaying the pipeline’s error logs. Use Metrics Explorer to validate the findings from the chart.
  • C. Use Cloud Logging to view error messages in the pipeline's logs. Use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization and memory usage.
  • D. Use the Dataflow job monitoring interface to check the pipeline's status every hour. Use Cloud Profiler to analyze the pipeline’s metrics, such as CPU utilization and memory usage.


Answer : C

Your organization’s business analysts require near real-time access to streaming data. However, they are reporting that their dashboard queries are loading slowly. After investigating BigQuery query performance, you discover the slow dashboard queries perform several joins and aggregations.
You need to improve the dashboard loading time and ensure that the dashboard data is as up-to-date as possible. What should you do?

  • A. Disable BigQuery query result caching.
  • B. Modify the schema to use parameterized data types.
  • C. Create a scheduled query to calculate and store intermediate results.
  • D. Create materialized views.


Answer : D

You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

  • A. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
  • B. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.
  • C. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.
  • D. Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.


Answer : A

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Exam contains 72 questions

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