ADP Dumps PDF – ADP Real Exam Questions Answers [Q34-Q48]

Posted On October 27, 2026

ADP Dumps PDF – ADP Real Exam Questions Answers

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Q34. Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one- time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.

Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.

Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.

Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.

Q35. 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?

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.

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.

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

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

Q36. 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?

Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.

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.

Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.

Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.

Q37. 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?

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.

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.

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.

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.

Q38. Your company uses Looker to generate and share reports with various stakeholders. You have a complex dashboard with several visualizations that needs to be delivered to specific stakeholders on a recurring basis, with customized filters applied for each recipient. You need an efficient and scalable solution to automate the delivery of this customized dashboard. You want to follow the Google- recommended approach. What should you do?

Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.

Use the Looker Scheduler with a user attribute filter on the dashboard, and send the dashboard with personalized filters to each stakeholder based on their attributes.

Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.

Embed the Looker dashboard in a custom web application, and use the application’s scheduling features to send the report with personalized filters.

Q39. Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?

Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.

Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.

Use the existing data pipeline tool’s BigQuery connector to reconfigure the data mapping.

Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.

Q40. Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.

Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.

Create a single Explore with all sales metrics. Build the dashboard using this Explore.

Use Looker’s custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.

Q41. 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?

Create a custom quota for each analyst in BigQuery.

Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.

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.

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.

Q42. 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?

Use Cloud Data Fusion pipelines.

Use Dataform workflows.

Use Dataflow pipelines.

Use Cloud Composer operators.

Q43. 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?

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.

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

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

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

Q44. Your organization has a BigQuery dataset that contains sensitive employee information such as salaries and performance reviews. The payroll specialist in the HR department needs to have continuous access to aggregated performance data, but they do not need continuous access to other sensitive dat a. You need to grant the payroll specialist access to the performance data without granting them access to the entire dataset using the simplest and most secure approach. What should you do?

Use authorized views to share query results with the payroll specialist.

Create row-level and column-level permissions and policies on the table that contains performance data in the dataset. Provide the payroll specialist with the appropriate permission set.

Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.

Create a SQL query with the aggregated performance data. Export the results to an Avro file in a Cloud Storage bucket. Share the bucket with the payroll specialist.

Q45. Your organization uses Dataflow pipelines to process real-time financial transactions. You discover that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.

Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.

Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.

Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.

Q46. Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

Use Cloud Scheduler to schedule the jobs to run.

Use Cloud Tasks to schedule and run the jobs asynchronously.

Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.

Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.

Q47. You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.

Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.

Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.

Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.

Q48. You work for a healthcare company. You have a daily ETL pipeline that extracts patient data from a legacy system, transforms it, and loads it into BigQuery for analysis. The pipeline currently runs manually using a shell script. You want to automate this process and add monitoring to ensure pipeline observability and troubleshooting insights. You want one centralized solution, using open-source tooling, without rewriting the ETL code. What should you do?

Create a direct acyclic graph (DAG) in Cloud Composer to orchestrate a pipeline trigger daily. Monitor the pipeline’s execution using the Apache Airflow web interface and Cloud Monitoring.

Configure Cloud Dataflow to implement the ETL pipeline, and use Cloud Scheduler to trigger the Dataflow pipeline daily. Monitor the pipelines execution using the Dataflow job monitoring interface and Cloud Monitoring.

Use Cloud Scheduler to trigger a Dataproc job to execute the pipeline daily. Monitor the job’s progress using the Dataproc job web interface and Cloud Monitoring.

Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.

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