NEW 2026 Certification Sample Questions ADP Dumps & Practice Exam [Q16-Q34]

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NEW 2026 Certification Sample Questions ADP Dumps & Practice Exam

ADP Deluxe Study Guide with Online Test Engine

Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis and Presentation	27%	– Querying and analyzing data 1. Data aggregation and transformation 2. BigQuery SQL usage – Data visualization 1. Looker / Looker Studio reporting
Topic 2: Data Pipeline Orchestration	18%	– Pipeline design and automation 1. Workflow orchestration concepts (e.g., Cloud Composer) 2. Dataflow pipelines
Topic 3: Data Management	25%	– Storage and data organization 1. BigQuery datasets and tables management 2. Cloud Storage lifecycle policies – Data governance and security 1. Data lifecycle management 2. IAM and access control
Topic 4: Data Preparation and Ingestion	30%	– Data ingestion into Google Cloud services 1. Cloud Storage ingestion patterns 2. Streaming ingestion with Pub/Sub concepts

NEW QUESTION 16
You need to create a new data pipeline. You want a serverless solution that meets the following requirements:
* Data is streamed from Pub/Sub and is processed in real-time.
* Data is transformed before being stored.
* Data is stored in a location that will allow it to be analyzed with SQL using Looker.
Which Google Cloud services should you recommend for the pipeline?

Dataproc Serverless Bigtable

Cloud Composer Cloud SQL for MySQL

BigQuery Analytics Hub

Dataflow BigQuery

NEW QUESTION 17
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.

NEW QUESTION 18
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.

NEW QUESTION 19
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.

NEW QUESTION 20
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.

NEW QUESTION 21
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.

NEW QUESTION 22
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.

NEW QUESTION 23
Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.

Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.

Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.

Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.

NEW QUESTION 24
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.

NEW QUESTION 25
You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

Create asales_regionuser attribute, and assign each manager’s region as the value of their user attribute.

Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.

Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.

Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.

NEW QUESTION 26
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.

NEW QUESTION 27
You are working on a project that requires analyzing daily social media dat a. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?

Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.

Use Cloud Run functions to transform and load the data into BigQuery.

Use Dataflow to transform the data and write the transformed data to BigQuery.

Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.

NEW QUESTION 28
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

NEW QUESTION 29
Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company’s products in your Looker Explores and dashboards.
You need to implement a solution quickly and efficiently. What should you do?

Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.

Define a new measure that calculates the profit margin by using the existing revenue and cost fields.

Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).

Apply a filter to only show products with a positive profit margin.

NEW QUESTION 30
Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive dat a. What should you do?

Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.

Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.

Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.

Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.

NEW QUESTION 31
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.

NEW QUESTION 32
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

NEW QUESTION 33
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.

NEW QUESTION 34
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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NEW 2026 Certification Sample Questions ADP Dumps & Practice Exam [Q16-Q34]

Google ADP Exam Syllabus Topics:

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