Prepare with Google ADP exam torrent, pass for sure
Updated: Aug 16, 2026
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Google ADP Exam Overview:
| Certification Vendor: | Google Cloud |
| Exam Name: | Google Cloud Associate Data Practitioner |
| Exam Number: | PCA-DP |
| Exam Price: | USD 100 |
| Exam Format: | Multiple Choice, Multiple Select |
| Certificate Validity Period: | 24 months |
| Real Exam Qty: | 50 |
| Exam Duration: | 120 minutes |
| Available Languages: | English |
| Passing Score: | 70 |
| Related Certifications: | Google Cloud Professional Data Engineer Google Cloud Data Analytics Looker Data Analyst |
| Sample Questions: | Google ADP Sample Questions |
| Exam Way: | Online proctored or in-person testing center |
| Pre Condition: | No formal prerequisites, but basic knowledge of data concepts and cloud computing is recommended |
| Official Syllabus URL: | https://cloud.google.com/certification/associate-data-practitioner |
Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation and Exploration | 20-30% | - Transform and prepare data for analysis - Ingest and acquire data - Identify data quality issues - Perform exploratory data analysis (EDA) - Explore data through visualization and queries |
| Topic 2: Data-Driven Decision Making | 10-20% | - Assess data quality and completeness - Define success metrics - Translate business requirements into data solutions - Identify stakeholders and requirements |
| Topic 3: Data Processing and Analytics | 20-30% | - Aggregate and summarize data - Query and analyze datasets - Apply statistical methods for analysis - Use BigQuery and SQL for analytics - Build and maintain data pipelines |
| Topic 4: Data Visualization and Insights | 20-30% | - Create dashboards and reports - Interpret and communicate findings - Choose appropriate visualization types - Build visualizations using Looker Studio - Present data insights to stakeholders |
Google Associate Data Practitioner Sample Questions:
1. 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 the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the -- daisy-chain option.
B) Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.
C) Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the --no- clobber option.
D) Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
2. Following a recent company acquisition, you inherited an on- premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort. What should you do?
A) Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
B) Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
C) Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
D) Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.
3. Your organization plans to move their on-premises environment to Google Cloud. Your organization's network bandwidth is less than 1 Gbps. You need to move over 500 ## of data to Cloud Storage securely, and only have a few days to move the dat a. What should you do?
A) Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.
B) Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.
C) Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.
D) Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.
4. 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 Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run 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 Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
D) Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
5. 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) Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
B) Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.
C) 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.
D) Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: C |
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