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We have perfect service guides of our ADP test dumps. There is guarantee to pass the exam after preparing with our ADP VCE engine or test dumps. Answers with explanations below:
Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Pipeline Orchestration | 18% | - Pipeline design and automation
|
| Data Preparation and Ingestion | 30% | - Data ingestion into Google Cloud services
|
| Data Management | 25% | - Data governance and security
|
| Data Analysis and Presentation | 27% | - Data visualization
|
Google Associate Data Practitioner Sample Questions:
1. Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?
A) Store the data in Cloud Storage using Coldline storage.
B) Store the data in Cloud Storage using Nearline storage.
C) Store the data in Cloud Storage using Archive storage.
D) Store the data in Cloud Storage using Standard storage.
2. Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?
A) Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.
B) Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.
C) Create a BigQuery materialized view that performs a cross join across all of the regional sales tables. Use the row_number() window function to query the new materialized view.
D) Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.
3. You recently inherited a task for managing Dataflow streaming pipelines in your organization and noticed that proper access had not been provisioned to you. You need to request a Google-provided IAM role so you can restart the pipelines. You need to follow the principle of least privilege. What should you do?
A) Request the Dataflow Admin role.
B) Request the Dataflow Viewer role.
C) Request the Dataflow Worker role.
D) Request the Dataflow Developer role.
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) 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.
B) Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
C) Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
D) 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.
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 Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
B) Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
C) Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
D) Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: B |





