Google Google Cloud Associate Data Practitioner (ADP Exam) Associate-Data-Practitioner Exam Dumps: Updated Questions & Answers (October 2025)

Comprehensive and Detailed In-Depth Explanation:

Cost minimization requires matching storage classes to access patterns using lifecycle rules. Let’s assess:

Option A: Nearline during training (frequent access) incurs high retrieval costs and latency, unsuitable for ML workloads. Coldline after 30 days and Archive after 90 days are reasonable but misaligned initially.

Option B: Standard storage (no retrieval fees, low latency) is ideal for frequent access during training. Transitioning to Nearline (30-day minimum, low access) after 30 days and Coldline (90-day minimum, rare access) after 90 days matches the pattern and minimizes costs effectively.

Option C: Nearline during training is costly for frequent access, and Archive to Coldline is illogical (Archive is cheaper than Coldline).

Configuring alifecycle management policyon each Cloud Storage bucket allows you to automatically transition objects to lower-cost storage classes (such as Nearline, Coldline, or Archive) based on their age or other criteria. Additionally, the policy can automate the removal of objects once they are no longer needed, ensuring compliance with retention rules and optimizing storage costs. This approach aligns well with well-defined data tiering and retention needs, providing cost efficiency and automation.

Extract from Google Documentation: From "Object Lifecycle Management" (https://cloud.google.com/storage/docs/lifecycle ):"Use lifecycle management policies to automatically transition objects to lower-cost storage classes (e.g., Nearline, Coldline) and delete them based on age, optimizing costs according to your specific tiering and retention requirements."

Creating amaterialized viewwith the limited data in a new dataset and granting the data analyst theBigQuery Data Viewerrole on the dataset and theBigQuery Job Userrole in the project ensures that the analyst can query only the non-sensitive data without access to sensitive datasets. Materialized views allow you to predefine what subset of data is visible, providing a secure and efficient way to control access while maintaining compliance with data governance policies. This approach follows the principle of least privilege while meeting the requirements.

Using theBigQuery Python client libraryto query and preprocess data directly in BigQuery and then leveragingBigQueryMLto train the churn prediction model is the Google-recommended approach for this scenario. BigQueryML allows you to build machine learning models directly within BigQuery using SQL, eliminating the need to export data or manage additional infrastructure. This minimizes overhead, scales effectively for a dataset as large as 50 PB, and simplifies the end-to-end process of building and training the churn prediction model.

Since your existing data pipeline tools already support connectors to BigQuery, the most efficient approach is touse the existing data pipeline tool's BigQuery connectorto reconfigure the data mapping. This leverages your current tools, reducing migration complexity and setup time, while optimizing migration speed. By reconfiguring the data mapping within the existing pipeline, you can seamlessly direct the data into BigQuery without needing additional services or intermediary steps.

Enabling automatic backups and creating a read replica of the Cloud SQL instance is the best solution to improve read performance. Read replicas allow you to offload read traffic from the primary database instance, reducing its load and improving overall performance. This approach is cost-effective and easy to implement within Cloud SQL. It ensures that the primary instance focuses on write operations while replicas handle read queries, providing a seamless performance boost with minimal effort.

Comprehensive and Detailed In-Depth Explanation:

The solution must support SQL-based ELT, be serverless and cost-effective, and include advanced features like version control and quality checks. Let’s dive in:

Option A: Cloud Data Fusion is a visual ETL tool, not SQL-centric (uses plugins), and isn’t fully serverless (requires instance management). It lacks native source code control and parameterization.

Option B: Dataform is a serverless, SQL-based ELT platform for BigQuery. It uses SQLX scripts, integrates with Git for version control, supports environment variables (parameterization), and offers assertions for data quality—all meeting the requirements cost-effectively.

Option C: Dataproc is for Spark/MapReduce, not SQL ELT, and requires cluster management, contradicting serverless and cost goals.

Comprehensive and Detailed In-Depth Explanation:

For a 25 TB dataset, efficiency and cost require minimizing data movement and leveraging BigQuery’s scalability within Colab Enterprise.

Option A: Exporting 25 TB to Google Drive and loading via Pandas is impractical (size limits, transfer costs) and slow.

Option B: BigQuery magic commands (%%bigquery) in Colab Enterprise allow direct querying of BigQuery data, keeping processing in the cloud, reducing costs, and enabling collaboration.

Option C: Dataproc with Spark adds cluster costs and complexity, unnecessary when BigQuery can handle the workload.

UsingCloud Data Fusionis the best solution for this scenario because:

Standardized managed solution: Cloud Data Fusion provides a visual interface for building data pipelines and includes prebuilt connectors and transformations for data cleaning and de-identification.

Compliance: It ensures sensitive data such as PII is de-identified prior to ingestion into Google Cloud, adhering to regulatory requirements for healthcare data.

Ease of use: Cloud Data Fusion is designed for transforming and preparing data, making it a managed and user-friendly tool for this purpose.

It's a fully managed, cloud-native data integration service for building ETL/ELT data pipelines visually.  

It offers built-in transformations and connectors, including those suitable for data masking and de-identification.  

It provides a standardized, visual interface, making it easier to create and manage data pipelines across various data sources.  

It's designed for data integration and transformation, making it ideal for this scenario.  

It helps to achieve a standardized managed solution.

The ETL (Extract, Transform, Load) methodology is the best approach for this scenario because it allows you to extract data from the files, transform it by applying the necessary data cleansing (including removing malicious SQL injections), and then load the sanitized data into BigQuery. By transforming the data before loading it into BigQuery, you ensure that only clean and safe data is stored, which is critical for security and data quality.