Associate-Data-Practitioner Google Cloud Associate Data Practitioner ( ADP Exam ) Questions and Answers

In BigQuery ML, when creating a logistic regression model to predict customer churn, the correct query should:

Exclude the target label column (in this case, churned) from the feature columns, as it is used for training and not as a feature input.

Rename the target label column to label, as BigQuery ML requires the target column to be named label.

The chosen query satisfies these requirements:

SELECT * EXCEPT(churned), churned AS label: Excludes churned from features and renames it to label.

The OPTIONS(model_type='logistic_reg') specifies that a logistic regression model is being trained.

This setup ensures the model is correctly trained using the features in the dataset while targeting the churned column for predictions.

Audio files from phone interactions: Use Cloud Storage. Cloud Storage is ideal for storing large binary objects like audio files, offering scalability and easy accessibility for training purposes.

CSV files of users’ personally identifiable information (PII): Use BigQuery. BigQuery is a serverless data warehouse optimized for analyzing structured data, such as CSV files, using SQL. It ensures compliance with PII handling through access controls and data encryption.

A large volume of small document files: Use Firestore. Firestore is a scalable NoSQL database designed for applications requiring fast, real-time interactions and structured document storage, making it suitable for powering other applications.

To troubleshoot a failed Dataflow job as quickly as possible, you should navigate to theDataflow Jobs page in the Google Cloud console. The console provides access to detailed job logs and worker logs, which can help you identify the cause of the failure. The graphical interface also allows you to visualize pipeline stages, monitor performance metrics, and pinpoint where the error occurred, making it the most efficient way to diagnose and resolve the issue promptly.

UsingDataflowto implement a streaming pipeline triggered by anOBJECT_FINALIZEnotification from Pub/Sub is the best solution. This approach automatically starts the data processing as soon as new files are uploaded to Cloud Storage, ensuring low latency. Dataflow can handle the data cleaning, deduplication, and enrichment with product information from the BigQuery table in a scalable and efficient manner. This solution minimizes overhead, as Dataflow is a fully managed service, and it is well-suited for real-time or near-real-time data pipelines.

Copying the storm event dataset into yourBigQuery projectand usingBigQuery Studioto query and visualize the data inLooker Studiois the best approach. This solution allows you to create reusable and automated workflows for generating risk reports. BigQuery handles the querying efficiently, and Looker Studio provides powerful tools for creating and sharing dynamic charts and dashboards. This setup ensures that reports can be easily re-run with updated data, minimizing manual effort and providing a scalable, interactive solution for visualizing risk reports.

Using Cloud Data Fusion to create a batch pipeline with a Cloud Storage source and a BigQuery sink is the best solution because:

Scalability: Cloud Data Fusion is a scalable, fully managed data integration service.

Data transformation: It provides a visual interface to design pipelines, enabling quick transformation of data.

Data quality insights: Cloud Data Fusion includes built-in tools for monitoring and addressing data quality issues during the pipeline creation and execution process.

Using thebq loadcommand is the simplest and most efficient way to perform a one-time load of files from your local machine into BigQuery. This command-line tool is easy to use, requires minimal setup, and supports direct uploads from local files to BigQuery tables. It meets the requirement for minimal effort while allowing you to quickly demonstrate BigQuery's capabilities to your organization’s leadership team.

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.

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.

Creating amaterialized view in BigQuerywith theSUM()function and theDATE_SUB()function is the best approach. Materialized views allow you to pre-aggregate and cache query results, making them efficient for repeated access, such as monthly reporting. By using theDATE_SUB()function, you can filter the inventory data to include only the most recent month. This approach ensures that the aggregation is up-to-date with minimal latency and provides efficient integration with Looker Studio for dashboarding.

To build a serverless data pipeline that processes data in real-time from Pub/Sub, transforms it, and stores it for SQL-based analysis using Looker, the best solution is to useDataflowandBigQuery.Dataflowis a fully managed service for real-time data processing and transformation, whileBigQueryis a serverless data warehouse that supports SQL-based querying and integrates seamlessly with Looker for data analysis and visualization. This combination meets the requirements for real-time streaming, transformation, and efficient storage for analytical queries.

Creating arow-level access policyin BigQuery ensures that each sales representative can see only the transactions relevant to their region. Row-level access policies allow you to define fine-grained access control by filtering rows based on specific conditions, such as matching the sales representative's region. This approach enforces security while providing tailored data access, aligning with the principle of least privilege.

Evaluating data drift involves analyzing changes in the distribution of the current serving data compared to the historical data used to train the model. If significant drift is detected, it indicates that the data patterns have changed over time, which can impact the model's performance. This analysis helps determine whether retraining the model is necessary to ensure its predictions remain accurate and relevant. Data drift evaluation is a standard approach for monitoring machine learning models over time.

Setting a table-level retention policy in BigQuery to seven years is the most efficient and cost-optimized solution to meet the regulatory requirement. A table-level retention policy ensures that the data cannot be deleted or overwritten before the specified retention period expires, providing compliance with auditing requirements while keeping the data within BigQuery for easy access and analysis. This approach avoids the complexity and additional costs of exporting data to Cloud Storage.

Using aGoogle-provided Dataflow templateis the most efficient and development-friendly approach to implement near real-time analytics for Pub/Sub messages. Dataflow templates are pre-built and optimized for processing streaming data, allowing you to quickly configure and deploy a pipeline with minimal development effort. These templates can handle message ingestion from Pub/Sub, perform necessary transformations, and load the processed data into BigQuery, ensuring scalability and low latency for near real-time analytics.

Storing the data in amulti-region bucketensures high availability and durability, even in the event of a single-zone outage. Multi-region buckets replicate data across multiple locations within the selected region, providing resilience against zone-level failures and ensuring that the data remains accessible. This approach is particularly suitable for sensitive customer data that must remain available without interruptions.

Using BigQuery to batch load the data and perform cleaning and analysis with SQL is the best approach for this scenario. BigQuery provides powerful SQL capabilities to handle missing values, enforce correct data types, and remove duplicates efficiently. This method simplifies the pipeline by leveraging BigQuery’s built-in processing power for both cleaning and analysis, reducing the need for additional tools or services and minimizing complexity.