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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Analysis and Presentation | 27% | - Querying and analyzing data
|
| Topic 2: Data Pipeline Orchestration | 18% | - Pipeline design and automation
|
| Topic 3: Data Preparation and Ingestion | 30% | - Data ingestion into Google Cloud services
|
| Topic 4: Data Management | 25% | - Data governance and security
|
Google Associate Data Practitioner Sample Questions:
Question #1
Your organization has several datasets in BigQuery. The datasets need to be shared with your external partners so that they can run SQL queries without needing to copy the data to their own projects. You have organized each partner's data in its own BigQuery dataset. Each partner should be able to access only their dat a. You want to share the data while following Google-recommended practices. What should you do?
A. Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.
B. Create a Dataflow job that reads from each BigQuery dataset and pushes the data into a dedicated Pub /Sub topic for each partner. Grant each partner the pubsub. subscriber IAM role.
C. Use Analytics Hub to create a listing on a private data exchange for each partner dataset. Allow each partner to subscribe to their respective listings.
D. Grant the partners the bigquery.user IAM role on the BigQuery project.
Question #2
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. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
C. 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.
D. 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.
Question #3
You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?
A. Use Dataproc to run the transformations on a cluster.
B. Use Dataflow with a custom template for the transformation logic.
C. Use Cloud Data Fusion to visually design and manage the pipeline.
D. Use Dataform to define the transformations in SQLX.
Question #4
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. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.
B. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the --no- clobber option.
C. Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
D. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the -- daisy-chain option.
Question #5
Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?
A. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
B. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
C. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
D. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
Solutions:
| Question #1 Correct Answer: C | Question #2 Correct Answer: B | Question #3 Correct Answer: A | Question #4 Correct Answer: C | Question #5 Correct Answer: A |


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