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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Analysis and Presentation | 27% | - Data exploration and analysis
|
| Data Preparation and Ingestion | 30% | - Storage solutions selection
|
| Data Pipeline Orchestration | 18% | - Transformation tools selection
|
| Data Management and Governance | 25% | - Data quality and maintenance
|
Google Associate Data Practitioner Sample Questions:
1. You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?
A) Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
B) Create a Cloud Run function that runs daily and deletes files older than seven days.
C) Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
D) Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
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 union across all of the regional sales tables. Use the row_number() window function to query the new table.
B) 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.
C) 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.
D) 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.
3. Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability.
What should you do?
A) Create a multi-region bucket, and upload the files to this bucket.
B) Create a single-region bucket in each of the two regions, and use the gcloud storage command to replicate the data across the buckets in both regions.
C) Create a dual-region bucket, and upload the files to this bucket.
D) Create a single-region bucket in each of the two regions, and use Storage Transfer Service to replicate the data across the buckets in both regions.
4. 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 Standard storage.
C) Store the data in Cloud Storage using Nearline storage.
D) Store the data in Cloud Storage using Archive storage.
5. 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 Dataflow with a custom template for the transformation logic.
B) Use Dataproc to run the transformations on a cluster.
C) Use Cloud Data Fusion to visually design and manage the pipeline.
D) Use Dataform to define the transformations in SQLX.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: C | Question # 5 Answer: B |


