Latest [Dec 14, 2025] Real Google Associate-Data-Practitioner Exam Dumps Questions
Associate-Data-Practitioner Dumps To Pass Google Cloud Platform Exam in One Day (Updated 108 Questions)
Google Associate-Data-Practitioner Exam Syllabus Topics:
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NEW QUESTION # 38
You are responsible for managing Cloud Storage buckets for a research company. Your company has well- defined data tiering and retention rules. You need to optimize storage costs while achieving your data retention needs. What should you do?
- A. Configure the buckets to use the Archive storage class.
- B. Configure a lifecycle management policy on each bucket to downgrade the storage class and remove objects based on age.
- C. Configure the buckets to use the Autoclass feature.
- D. Configure the buckets to use the Standard storage class and enable Object Versioning.
Answer: B
Explanation:
Configuring alifecycle management policyon each Cloud Storage bucket allows you to automatically transition objects to lower-cost storage classes (such as Nearline, Coldline, or Archive) based on their age or other criteria. Additionally, the policy can automate the removal of objects once they are no longer needed, ensuring compliance with retention rules and optimizing storage costs. This approach aligns well with well- defined data tiering and retention needs, providing cost efficiency and automation.
Extract from Google Documentation: From "Object Lifecycle Management" (https://cloud.google.com
/storage/docs/lifecycle):"Use lifecycle management policies to automatically transition objects to lower-cost storage classes (e.g., Nearline, Coldline) and delete them based on age, optimizing costs according to your specific tiering and retention requirements."
NEW QUESTION # 39
Your organization consists of two hundred employees on five different teams. The leadership team is concerned that any employee can move or delete all Looker dashboards saved in the Shared folder. You need to create an easy-to-manage solution that allows the five different teams in your organization to view content in the Shared folder, but only be able to move or delete their team-specific dashboard. What should you do?
- A. 1. Create Looker groups representing each of the five different teams, and add users to their corresponding group. 2. Create five subfolders inside the Shared folder. Grant each group the View access level to their corresponding subfolder.
- B. 1. Move all team-specific content into the dashboard owner s personal folder. 2. Change the access level of the Shared folder to View for the All Users group. 3. Instruct each user to create content for their team in the user's personal folder.
- C. 1. Change the access level of the Shared folder to View for the All Users group. 2. Create five subfolders inside the Shared folder. Grant each team member the Manage Access, Edit access level to their corresponding subfolder.
- D. 1. Change the access level of the Shared folder to View for the All Users group. 2. Create Looker groups representing each of the five different teams, and add users to their corresponding group. 3.
Create five subfolders inside the Shared folder. Grant each group the Manage Access, Edit access level to their corresponding subfolder.
Answer: D
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why C is correct:Setting the Shared folder to "View" ensures everyone can see the content.
Creating Looker groups simplifies access management.
Subfolders allow granular permissions for each team.
Granting "Manage Access, Edit" allows teams to modify only their own content.
Why other options are incorrect:A: Grants View access only, so teams can't edit.
B: Moving content to personal folders defeats the purpose of sharing.
D: Grants edit access to all members of the team, not the team as a whole, which is not ideal.
NEW QUESTION # 40
You work for an online retail company. Your company collects customer purchase data in CSV files and pushes them to Cloud Storage every 10 minutes. The data needs to be transformed and loaded into BigQuery for analysis. The transformation involves cleaning the data, removing duplicates, and enriching it with product information from a separate table in BigQuery. You need to implement a low-overhead solution that initiates data processing as soon as the files are loaded into Cloud Storage. What should you do?
- A. Use Dataflow to implement a streaming pipeline using anOBJECT_FINALIZEnotification from Pub
/Sub to read the data from Cloud Storage, perform the transformations, and write the data to BigQuery. - B. Use Cloud Composer sensors to detect files loading in Cloud Storage. Create a Dataproc cluster, and use a Composer task to execute a job on the cluster to process and load the data into BigQuery.
- C. Create a Cloud Data Fusion job to process and load the data from Cloud Storage into BigQuery. Create anOBJECT_FINALIZE notification in Pub/Sub, and trigger a Cloud Run function to start the Cloud Data Fusion job as soon as new files are loaded.
- D. Schedule a direct acyclic graph (DAG) in Cloud Composer to run hourly to batch load the data from Cloud Storage to BigQuery, and process the data in BigQuery using SQL.
Answer: A
Explanation:
UsingDataflowto implement a streaming pipeline triggered by anOBJECT_FINALIZEnotification from Pub
/Sub is the best solution. This approach automatically starts the data processing as soon as new files are uploaded to Cloud Storage, ensuring low latency. Dataflow can handle the data cleaning, deduplication, and enrichment with product information from the BigQuery table in a scalable and efficient manner. This solution minimizes overhead, as Dataflow is a fully managed service, and it is well-suited for real-time or near-real-time data pipelines.
NEW QUESTION # 41
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 Dataflow pipelines.
- C. Use Cloud Composer operators.
- D. Use Dataform workflows.
Answer: D
Explanation:
Dataform workflowsare the ideal solution for migrating batch transformation pipelines to Google Cloud when you want to perform programmatic transformations using only SQL. Dataform allows you to define SQL- based workflows for data transformations and supports Git integration for version control, enabling collaboration and version tracking of your pipelines. This approach is purpose-built for SQL-driven data pipeline management and aligns perfectly with your requirements.
The solution must use SQL for transformations and integrate with Git for version control, focusing on batch pipelines. Let's evaluate:
* Option A: Cloud Data Fusion uses a visual UI with plugins, not SQL-only transformations. It lacks native Git integration (requires external tools), missing a key requirement.
* Option B: Dataform is a SQL-based workflow tool for BigQuery transformations, defining pipelines as SQLX scripts. It integrates natively with Git for version control, supporting batch ELT processes with minimal overhead.
* Option C: Cloud Composer uses Python DAGs and operators, not SQL-only transformations. Git is possible but not intrinsic to its workflow design.
NEW QUESTION # 42
You need to create a weekly aggregated sales report based on a large volume of dat a. You want to use Python to design an efficient process for generating this report. What should you do?
- A. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code 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 Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
Answer: A
Explanation:
Using Dataflow with a Python-coded Directed Acyclic Graph (DAG) is the most efficient solution for generating a weekly aggregated sales report based on a large volume of data. Dataflow is optimized for large-scale data processing and can handle aggregation efficiently. Python allows you to customize the pipeline logic, and Cloud Scheduler enables you to automate the process to run weekly. This approach ensures scalability, efficiency, and the ability to process large datasets in a cost-effective manner.
NEW QUESTION # 43
You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage. The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?
- A. EL
- B. ETLT
- C. ETL
- D. ELT
Answer: C
Explanation:
The ETL (Extract, Transform, Load) methodology is the best approach for this scenario because it allows you to extract data from the files, transform it by applying the necessary data cleansing (including removing malicious SQL injections), and then load the sanitized data into BigQuery. By transforming the data before loading it into BigQuery, you ensure that only clean and safe data is stored, which is critical for security and data quality.
NEW QUESTION # 44
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. 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.
- B. Export the raw BigQuery data to a CSV file, upload it to Cloud Storage, and use the Gemini API to summarize the data.
- C. Create a BigQuery Cloud resource connection to a remote model in Vertex Al, and use Gemini to summarize the data.
- D. 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.
Answer: C
Explanation:
Creating aBigQuery Cloud resource connectionto a remote model inVertex AIand using Gemini to summarize the data is the most efficient approach. This method allows you to seamlessly integrate BigQuery with the Gemini model via Vertex AI, avoiding the need to export data or perform manual steps. It ensures scalability for large datasets and minimizes data movement, leveraging Google Cloud's ecosystem for efficient data summarization and storage.
NEW QUESTION # 45
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 rowsand transactional data. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost.
What should you do?
- A. 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.
- B. 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.
- C. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
- D. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
Answer: D
Explanation:
Using Database Migration Service (DMS) to replicate the MySQL database to a Cloud SQL for MySQL instance is the best approach. DMS is a fully managed service designed for migrating databases to Google Cloud with minimal downtime and cost. It supports continuous data replication, ensuring data integrity during the migration process, and handles schema and data transfer efficiently. This solution is particularly suited for large tables and transactional data, as it maintains real-time synchronization between the source and target databases, minimizing downtime for the migration.
NEW QUESTION # 46
You manage a BigQuery table that is used for critical end-of-month reports. The table is updated weekly with new sales dat a. 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. Create a view of the table. On deletion, re-create the deleted table from the view 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. 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.
Answer: D
Explanation:
Scheduling the creation of a snapshot of the table weekly ensures that you have a point-in-time backup of the table. In case of accidental deletion, you can re-create the table from the snapshot. Additionally, BigQuery's time travel feature allows you to recover data from up to seven days prior to deletion. Combining snapshots with time travel provides a robust solution for preventing data loss and ensuring reporting continuity for critical tables. This approach minimizes risks while offering flexibility for recovery.
NEW QUESTION # 47
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 Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.
- B. 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.
- C. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
- D. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.
Answer: C
Explanation:
Pushing event information to a Pub/Sub topic and then creating a Dataflow job using the Dataflow job builder is the most suitable solution. The Dataflow job builder provides a visual interface to design pipelines, allowing you to define transformations and load data into BigQuery. This approach is ideal for streaming data pipelines that require near real-time transformations and analysis. It ensures scalability across multiple regions and integrates seamlessly with Pub/Sub for event ingestion and BigQuery for analysis.
NEW QUESTION # 48
You work for an online retail company. Your company collects customer purchase data in CSV files and pushes them to Cloud Storage every 10 minutes. The data needs to be transformed and loaded into BigQuery for analysis. The transformation involves cleaning the data, removing duplicates, and enriching it with product information from a separate table in BigQuery. You need to implement a low-overhead solution that initiates data processing as soon as the files are loaded into Cloud Storage. What should you do?
- A. Use Cloud Composer sensors to detect files loading in Cloud Storage. Create a Dataproc cluster, and use a Composer task to execute a job on the cluster to process and load the data into BigQuery.
- B. Use Dataflow to implement a streaming pipeline using an OBJECT_FINALIZE notification from Pub/Sub to read the data from Cloud Storage, perform the transformations, and write the data to BigQuery.
- C. Schedule a direct acyclic graph (DAG) in Cloud Composer to run hourly to batch load the data from Cloud Storage to BigQuery, and process the data in BigQuery using SQL.
- D. Create a Cloud Data Fusion job to process and load the data from Cloud Storage into BigQuery. Create an OBJECT_FINALI ZE notification in Pub/Sub, and trigger a Cloud Run function to start the Cloud Data Fusion job as soon as new files are loaded.
Answer: B
Explanation:
Using Dataflow to implement a streaming pipeline triggered by an OBJECT_FINALIZE notification from Pub/Sub is the best solution. This approach automatically starts the data processing as soon as new files are uploaded to Cloud Storage, ensuring low latency. Dataflow can handle the data cleaning, deduplication, and enrichment with product information from the BigQuery table in a scalable and efficient manner. This solution minimizes overhead, as Dataflow is a fully managed service, and it is well-suited for real-time or near-real-time data pipelines.
NEW QUESTION # 49
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. 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.
- 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. Create a custom quota for each analyst in BigQuery.
Answer: A
Explanation:
Assigning each analyst to a separate project associated with their department and creating a single reservation for each department using BigQuery editions allows for precise cost management. By assigning each project to its department's reservation, you can allocate fixed compute resources and budgets for each department, ensuring that their query costs are predictable and controlled. This approach aligns with your organization's goal of creating a fixed budget for query costs while maintaining departmental separation and accountability.
NEW QUESTION # 50
Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?
- A. Create a saved query in the BigQuery console that uses the SUM( ) function and the DATE_SUB( ) function. Re-run the saved query every month, and save the results to a BigQuery table.
- B. Create a BigQuery table that uses the SUM( ) function and the DATE_DIFF( ) function.
- C. Create a BigQuery table that uses the SUM( ) function and the _PARTITIONDATE filter.
- D. Create a materialized view in BigQuery that uses the SUM( ) function and the DATE_SUB( ) function.
Answer: D
Explanation:
Creating a materialized view in BigQuery with the SUM() function and the DATE_SUB() function is the best approach. Materialized views allow you to pre-aggregate and cache query results, making them efficient for repeated access, such as monthly reporting. By using the DATE_SUB() function, you can filter the inventory data to include only the most recent month. This approach ensures that the aggregation is up-to-date with minimal latency and provides efficient integration with Looker Studio for dashboarding.
NEW QUESTION # 51
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 ## of data to Cloud Storage securely, and only have a few days to move the data. What should you do?
- A. Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.
- B. Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.
- C. Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.
- D. 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.
Answer: D
Explanation:
UsingTransfer Appliancesis the best solution for securely and efficiently moving over 500 TB of data to Cloud Storage within a limited timeframe, especially with network bandwidth below 1 Gbps. Transfer Appliances are physical devices provided by Google Cloud to securely transfer large amounts of data. After copying the data to the appliances, they are shipped back to Google, where the data is uploaded to Cloud Storage. This approach bypasses bandwidth limitations and ensures the data is migrated quickly and securely.
NEW QUESTION # 52
You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?
- A. Use Cloud Run functions to trigger the Spark job and email the report.
- B. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
- C. Use Cloud Composer to orchestrate the Spark job and email the report.
- D. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
Answer: D
Explanation:
Using Dataproc workflow templates is a fully-managed and straightforward solution for defining and scheduling your Spark job on a Dataproc cluster. Workflow templates allow you to automate the execution of Spark jobs with predefined steps, including data processing and report generation. You can integrate email notifications by adding a step to the workflow that sends the report using tools like a Cloud Function or external email service. This approach minimizes complexity while leveraging Dataproc's managed capabilities for batch processing.
NEW QUESTION # 53
Your company has an on-premises file server with 5 TB of data that needs to be migrated to Google Cloud.
The network operations team has mandated that you can only use up to 250 Mbps of the total available bandwidth for the migration. You need to perform an online migration to Cloud Storage. What should you do?
- A. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.
- B. Use the gcloud storage cp command to copy all files from on-premises to Cloud Storage using the --no- clobber option.
- C. Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
- D. Use the gcloud storage cp command to copy all files from on-premises to Cloud Storage using the -- daisy-chain option.
Answer: C
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why A is correct:Storage Transfer Service with agent-based transfer allows for online migrations and provides the ability to set bandwidth limits.
Agents are installed on-premises and can be configured to respect network constraints.
Why other options are incorrect:B: The --daisy-chain option is not related to bandwidth control.
C: Transfer Appliance is for offline migrations and is not suitable for online transfers with bandwidth constraints.
D: The --no-clobber option prevents overwriting existing files but does not control bandwidth.
NEW QUESTION # 54
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. Compare the confusion matrix.
- D. Evaluate data drift.
Answer: D
Explanation:
Evaluating data drift involves analyzing changes in the distribution of the current serving data compared to the historical data used to train the model. If significant drift is detected, it indicates that the data patterns have changed over time, which can impact the model's performance. This analysis helps determine whether retraining the model is necessary to ensure its predictions remain accurate and relevant. Data drift evaluation is a standard approach for monitoring machine learning models over time.
NEW QUESTION # 55
Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?
- A. Create a BigQuery materialized view that performs a cross join across all of the regional sales tables.Use the row_number() window function to query the new materialized view.
- B. Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.
- C. Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.
- D. Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.
Answer: B
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why C is correct:Materialized views in BigQuery are precomputed views that periodically cache the results of a query. This ensures up-to-date results automatically.
A UNION is the correct operation to combine the data from multiple regional sales tables.
RANK() function is correct to rank the sales regions. ROW_NUMBER() would create a unique number for each row, even if sales amount is the same, this is not the desired function.
Why other options are incorrect:A and B: Standard tables do not provide automatic updates.
D: A CROSS JOIN would produce a Cartesian product, which is not appropriate for combining regional sales data.
Cross join is used when you want every combination of rows from tables, not a aggregation of data.
NEW QUESTION # 56
Following a recent company acquisition, you inherited an on-premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort.
What should you do?
- A. Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
- B. Create a new Cloud Composer environment and copy DAGs to the Cloud Composer dags/ folder.
- C. Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
- D. Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
Answer: B
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:Cloud Composer is a managed Apache Airflow service that provides a seamless migration path for existing Airflow DAGs.
Simply copying the DAGs to the Cloud Composer folder allows them to run directly on Google Cloud.
Why other options are incorrect:A: Cloud Workflows is a different orchestration tool, not compatible with Airflow DAGs.
C: GKE deployment requires setting up and managing a Kubernetes cluster, which is more complex.
D: Cloud Data Fusion is a data integration tool, not suitable for orchestrating existing pipelines.
NEW QUESTION # 57
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