Google ADP test insides dumps : Associate Data Practitioner

Google ADP test insides dumps
  • Exam Code: ADP
  • Exam Name: Associate Data Practitioner
  • Updated: Aug 20, 2026
  • Q & A: 66 Questions and Answers
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Google ADP Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data-Driven Decision Making10-20%- Assess data quality and completeness
- Define success metrics
- Translate business requirements into data solutions
- Identify stakeholders and requirements
Topic 2: Data Processing and Analytics20-30%- Use BigQuery and SQL for analytics
- Apply statistical methods for analysis
- Build and maintain data pipelines
- Aggregate and summarize data
- Query and analyze datasets
Topic 3: Data Visualization and Insights20-30%- Create dashboards and reports
- Interpret and communicate findings
- Build visualizations using Looker Studio
- Present data insights to stakeholders
- Choose appropriate visualization types
Topic 4: Data Preparation and Exploration20-30%- Perform exploratory data analysis (EDA)
- Ingest and acquire data
- Explore data through visualization and queries
- Transform and prepare data for analysis
- Identify data quality issues

Google Associate Data Practitioner Sample Questions:

1. 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 to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.
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 push subscription, write a Cloud Run service that accepts the messages, performs the transformations, and writes the results to BigQuery.


2. Your company has an on-premises file server with 5 TB of data that needs to be migrated to Google Cloud.
The network operations team has mandated that you can only use up to 250 Mbps of the total available bandwidth for the migration. You need to perform an online migration to Cloud Storage. What should you do?

A) Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
B) Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the --no- clobber option.
C) Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the -- daisy-chain option.
D) Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.


3. You manage a large amount of data in Cloud Storage, including raw data, processed data, and backups. Your organization is subject to strict compliance regulations that mandate data immutability for specific data types.
You want to use an efficient process to reduce storage costs while ensuring that your storage strategy meets retention requirements. What should you do?

A) Create a Cloud Run function to periodically check object metadata, and move objects to the appropriate storage class based on age and access patterns. Use object holds to enforce immutability for specific objects.
B) Use object holds to enforce immutability for specific objects, and configure lifecycle management rules to transition objects to appropriate storage classes based on age and access patterns.
C) Move objects to different storage classes based on their age and access patterns. Use Cloud Key Management Service (Cloud KMS) to encrypt specific objects with customer-managed encryption keys (CMEK) to meet immutability requirements.
D) Configure lifecycle management rules to transition objects to appropriate storage classes based on access patterns. Set up Object Versioning for all objects to meet immutability requirements.


4. 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?

A) Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
B) 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.
C) Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
D) Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.


5. Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?

A) Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
B) Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.
C) Use Dataform to define the transformations in SQLX.
D) Create BigQuery scheduled queries to define the transformations in SQL.


Solutions:

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

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