Google ADP real exam prep : Associate Data Practitioner

  • Exam Code: ADP
  • Exam Name: Associate Data Practitioner
  • Updated: Aug 22, 2026
  • Q&As: 66 Questions and Answers

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

Google Associate Data Practitioner Sample Questions:

1. 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?

A) Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
B) Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
C) Use Cloud Run functions to transform and load the data into BigQuery.
D) Use Dataflow to transform the data and write the transformed data to BigQuery.


2. 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?

A) 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.
B) Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.
C) Create a custom quota for each analyst in BigQuery.
D) 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.


3. 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 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.
B) 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.
C) 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.
D) Use Cloud Logging to create a chart displaying the pipeline's error logs. Use Metrics Explorer to validate the findings from the chart.


4. 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?

A) Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
B) 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.
C) 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.
D) Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.


5. 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?

A) 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.
B) 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.
C) 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.
D) 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.


Solutions:

Question # 1
Answer: D
Question # 2
Answer: A
Question # 3
Answer: C
Question # 4
Answer: D
Question # 5
Answer: A

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