2026 Latest Google ADP Real Exam Dumps PDF [Q30-Q52]

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2026 Latest Google ADP Real Exam Dumps PDF

ADP Exam Dumps, ADP Practice Test Questions

Q30. You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team’s existing skillset, processes data at scale, and minimizes cost. What should you do?

Use Dataflow with a custom template for the transformation logic.

Use Cloud Data Fusion to visually design and manage the pipeline.

Use Dataform to define the transformations in SQLX.

Use Dataproc to run the transformations on a cluster.

Q31. Your organization’s ecommerce website collects user activity logs using a Pub/Sub topic. Your organization’s leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?

Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.

Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.

Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.

Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting

Q32. 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.

Q33. 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 dat a. What should you do?

Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.

Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.

Use Dataflow to create a streaming pipeline that includes validation and transformation steps.

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.

Q34. Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?

Enable Object Versioning.

Change the bucket storage class to Archive.

Set a bucket retention policy.

Set temporary object holds.

Q35. You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?

Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.

Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.

Grant each partner read access to the BigQuery dataset by using 1AM roles.

Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.

Q36. 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.

Q37. 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?

Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the –no- clobber option.

Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.

Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.

Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the — daisy-chain option.

Q38. Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability.
What should you do?

Create a dual-region bucket, and upload the files to this bucket.

Create a single-region bucket in each of the two regions, and use the gcloud storage command to replicate the data across the buckets in both regions.

Create a multi-region bucket, and upload the files to this bucket.

Create a single-region bucket in each of the two regions, and use Storage Transfer Service to replicate the data across the buckets in both regions.

Q39. You work for a home insurance company. You are frequently asked to create and save risk reports with charts for specific areas using a publicly available storm event dataset. You want to be able to quickly create and re- run risk reports when new data becomes available. What should you do?

Export the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.

Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.

Reference and query the storm event dataset using SQL in BigQuery Studio. Export the results to Google Sheets, and use cell data in the worksheets to create charts.

Reference and query the storm event dataset using SQL in a Colab Enterprise notebook. Display the table results and document with Markdown, and use Matplotlib to create charts.

Q40. You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?

Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.

Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.

Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.

Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.

Q41. Your organization needs to store historical customer order dat
a. 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?

Store the data in Cloud Storage using Nearline storage.

Store the data in Cloud Storage using Coldline storage.

Store the data in Cloud Storage using Standard storage.

Store the data in Cloud Storage using Archive storage.

Q42. 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.

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 decided to move their on-premises Apache Spark-based workload to Google Cloud.
You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?

Migrate the Spark jobs to Dataproc Serverless.

Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.

Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.

Migrate the Spark jobs to Dataproc on Compute Engine.

Q45. 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?

ELT

ETL

ETLT

Q46. 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.

Q47. Your organization has several datasets in BigQuery. The datasets need to be shared with your external partners so that they can run SQL queries without needing to copy the data to their own projects. You have organized each partner’s data in its own BigQuery dataset. Each partner should be able to access only their dat a. You want to share the data while following Google-recommended practices. What should you do?

Use Analytics Hub to create a listing on a private data exchange for each partner dataset. Allow each partner to subscribe to their respective listings.

Create a Dataflow job that reads from each BigQuery dataset and pushes the data into a dedicated Pub /Sub topic for each partner. Grant each partner the pubsub. subscriber IAM role.

Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.

Grant the partners the bigquery.user IAM role on the BigQuery project.

Q48. You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

Use BigQuery ML to create a logistic regression model for purchase prediction.

Use Vertex Al Workbench to develop a custom model for purchase prediction.

Use Colab Enterprise to develop a custom model for purchase prediction.

Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.

Q49. 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.

Q50. Your organization uses scheduled queries to perform transformations on data stored in BigQuery. You discover that one of your scheduled queries has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.

Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.

Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.

Set up a log sink using the gcloud CLI to export BigQuery audit logs to BigQuery. Query those logs to identify the error associated with the failed job I

Q51. Your company’s customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files’ metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?

Create an external table.

Create a temporary table.

Create a native table.

Create an object table.

Q52. 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.

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2026 Latest Google ADP Real Exam Dumps PDF [Q30-Q52]

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