Google Associate Data Practitioner : ADP

  • Exam Code: ADP
  • Exam Name: Associate Data Practitioner
  • Updated: Aug 18, 2026
  • Q & A: 66 Questions and Answers

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Free Download ADP Exam PDF Torrent

Google ADP Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Pipeline Orchestration18%- Pipeline design and automation
  • 1. Dataflow pipelines
    • 2. Workflow orchestration concepts (e.g., Cloud Composer)
      Topic 2: Data Analysis and Presentation27%- Data visualization
      • 1. Looker / Looker Studio reporting
        - Querying and analyzing data
        • 1. BigQuery SQL usage
          • 2. Data aggregation and transformation
            Topic 3: Data Preparation and Ingestion30%- Data ingestion into Google Cloud services
            • 1. Cloud Storage ingestion patterns
              • 2. Streaming ingestion with Pub/Sub concepts
                Topic 4: Data Management25%- Data governance and security
                • 1. Data lifecycle management
                  • 2. IAM and access control
                    - Storage and data organization
                    • 1. BigQuery datasets and tables management
                      • 2. Cloud Storage lifecycle policies

                        Google Associate Data Practitioner Sample Questions:

                        1. 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) 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.
                        C) 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.
                        D) Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.


                        2. You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:
                        1. Audio files from phone interactions with support agents that will be accessed during trainings.
                        2. CSV files of users' personally identifiable information (PII) that will be analyzed with SQL.
                        3. A large volume of small document files that will power other applications.
                        You need to select the appropriate tool for each data type given the required use case, while following Google- recommended practices. Which should you choose?

                        A) Cloud Storage BigQuery Firestore
                        B) Cloud Storage CloudSQL for PostgreSQL Bigtable
                        C) Filestore Bigtable BigQuery
                        D) Filestore Cloud SQL for PostgreSQL Datastore


                        3. You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

                        A) Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
                        B) Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
                        C) Use Cloud Run functions to trigger the Spark job and email the report.
                        D) Use Cloud Composer to orchestrate the Spark job and email the report.


                        4. 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?

                        A) Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
                        B) Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
                        C) Apply a filter to only show products with a positive profit margin.
                        D) Define a new measure that calculates the profit margin by using the existing revenue and cost fields.


                        5. 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?

                        A) Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
                        B) Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
                        C) Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
                        D) Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.


                        Solutions:

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

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