Associate Data Practitioner: ADP Exam

"Associate Data Practitioner", also known as ADP exam, is a Google Certification. With the complete collection of questions and answers, ExamcollectionPass has assembled to take you through 66 Q&As to your ADP Exam preparation. In the ADP exam resources, you will cover every field and category in Google Cloud Certified Certification helping to ready you for your successful Google Certification.

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  • Exam Code: ADP
  • Exam Name: Associate Data Practitioner
  • Certification Provider: Google
  • Corresponding Certification: Google Cloud Certified
  • Updated: Sep 15, 2026
  • No. of Questions: 66 Questions & Answers with Testing Engine
  • Download Limit: Unlimited
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Google ADP Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Pipeline Orchestration18%- Transformation tools selection
  • 1. Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform
- Data transformation concepts
  • 1. ETL vs ELT processes
  • 2. Data transformation requirements
- Pipeline automation and scheduling
  • 1. Schedule and monitor data jobs
  • 2. Error handling and monitoring
Topic 2: Data Preparation and Ingestion30%- Data loading methods
  • 1. Batch and streaming ingestion
  • 2. gcloud, BQ CLI, client libraries
- Data extraction and transfer tools
  • 1. Dataflow, BigQuery Data Transfer Service, Database Migration Service
  • 2. Cloud Data Fusion, Storage Transfer Service
- Data formats and classification
  • 1. Formats: CSV, JSON, Parquet, Avro, database tables
  • 2. Structured, semi-structured, unstructured data
- Storage solutions selection
  • 1. Storage location types: regional, dual-regional, multi-regional, zonal
  • 2. Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner
Topic 3: Data Analysis and Presentation27%- Data visualization and reporting
  • 1. Visualization best practices
  • 2. Looker dashboards and reports
- Business intelligence and decision support
  • 1. Answer business questions with data
  • 2. Communicate findings effectively
- Data exploration and analysis
  • 1. Identify trends, patterns, insights
  • 2. BigQuery SQL queries
  • 3. Jupyter notebooks for analysis
Topic 4: Data Management and Governance25%- Data quality and maintenance
  • 1. Data validation and cleaning
  • 2. Data lifecycle management
- Compliance and governance
  • 1. Data stewardship and cataloging
  • 2. Data privacy and regulatory requirements
- Data security and access control
  • 1. IAM roles and permissions
  • 2. Data encryption and protection

Google Associate Data Practitioner Sample Questions:

You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

  • A. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
  • B. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
  • C. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
  • D. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

  • A. Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
  • B. Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.
  • C. Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.
  • D. Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Your company is building a near real-time streaming pipeline to process JSON telemetry data from small appliances. You need to process messages arriving at a Pub/Sub topic, capitalize letters in the serial number field, and write results to BigQuery. You want to use a managed service and write a minimal amount of code for underlying transformations. What should you do?

  • A. Use a Pub/Sub push subscription, write a Cloud Run service that accepts the messages, performs the transformations, and writes the results to BigQuery.
  • B. Use the "Pub/Sub to BigQuery" Dataflow template with a UDF, and write the results to BigQuery.
  • C. Use a Pub/Sub to Cloud Storage subscription, write a Cloud Run service that is triggered when objects arrive in the bucket, performs the transformations, and writes the results to BigQuery.
  • D. Use a Pub/Sub to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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?

  • A. Dataproc Serverless Bigtable
  • B. Dataflow BigQuery
  • C. BigQuery Analytics Hub
  • D. Cloud Composer Cloud SQL for MySQL
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue. What should you do?

  • A. Use the Dataflow job monitoring interface to check the pipeline's status every hour. Use Cloud Profiler to analyze the pipeline's metrics, such as CPU utilization and memory usage.
  • B. Use Cloud Logging to create a chart displaying the pipeline's error logs. Use Metrics Explorer to validate the findings from the chart.
  • C. Use Cloud Logging to identify error groups in the pipeline's logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.
  • D. Use Cloud Logging to view error messages in the pipeline's logs. Use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization and memory usage.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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