Latest ADP Test Preparation - 100% Pass Quiz 2026 Google ADP: First-grade Associate Data Practitioner Exam Cram Questions

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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q42-Q47):

NEW QUESTION # 42
You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?

A. Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
B. Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
C. Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
D. Create a Cloud Run function that runs daily and deletes files older than seven days.

Answer: A

NEW QUESTION # 43
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?

A. Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
B. Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
C. 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.
D. Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.

Answer: B

NEW QUESTION # 44
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?

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

Answer: A

NEW QUESTION # 45
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 confusion matrix.
B. Evaluate data drift.
C. Compare the two different models.
D. Evaluate the data skewness.

Answer: B

NEW QUESTION # 46
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?

A. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
B. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
C. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
D. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.

Answer: B

NEW QUESTION # 47
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