Associate-Data-Practitioner Sample Questions Answers

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 Analytics Hub to create a listing on a private data exchange for each partner dataset is the Google-recommended practice for securely sharing BigQuery data with external partners. Analytics Hub allows you to manage data sharing at scale, enabling partners to query datasets directly without needing to copy the data into their own projects. By creating separate listings for each partner dataset and allowing only the respective partner to subscribe, you ensure that partners can access only their specific data, adhering to the principle of least privilege. This approach is secure, efficient, and designed for scenarios involving external data sharing.

Creatingmaterialized viewsis the best solution to improve dashboard loading time while ensuring that the data is as up-to-date as possible. Materialized views precompute and cache the results of complex joins and aggregations, significantly reducing query execution time for dashboards. They also automatically update as the underlying data changes, ensuring near real-time access to fresh data. This approach optimizes query performance and provides an efficient and scalable solution for streaming data dashboards.

For near real-time dashboards with slow queries involving joins and aggregations, Google recommends materialized views in BigQuery to precompute and refresh results automatically.

Option A: Scheduled queries compute intermediate results periodically, but they aren’t near real-time and require manual scheduling, adding latency.

Option B: Parameterized data types don’t address query performance for joins/aggregations; they’re for query flexibility, not optimization.

Option C: Disabling caching worsens performance by forcing full recomputation each time, contrary to the goal.

UsingBigQuery's built-in functionsis the most effective and efficient way to clean the dataset directly within BigQuery. BigQuery provides powerful SQL capabilities to handle missing values, remove duplicates, and resolve formatting issues without needing to export data or create complex pipelines. This approach minimizes overhead and leverages the scalability of BigQuery for large datasets, making it an ideal solution for quickly addressing data quality issues.

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.

Comprehensive and Detailed In-Depth Explanation:

For an enterprise BI platform with data access control across teams, Google recommends Looker (Google Cloud core) over Looker Studio for its robust access management. The "first" step focuses on setting up the foundation.

Option A: Looker Studio reports are lightweight but lack granular access control beyond sharing. Creating separate reports per team is inefficient and unscalable.

Option B: One Looker Studio report with multiple pages and data sources doesn’t enforce team-level access control natively—users could access all pages/data.

Option C: Creating a Looker instance with separate dashboards per team is a step forward but skips the foundational access control setup (groups), reducing scalability.

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.

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.

Extract from Google Documentation: From "Row-Level Security in BigQuery" (https://cloud.google.com/bigquery/docs/row-level-security ):"Row-level access policies let you restrict access to specific rows in a table based on a filter condition, such as a user’s region, providing fine-grained control over data visibility without creating separate tables or views."

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.

Pushing event information to aPub/Sub topicand then creating aDataflow job using the Dataflow job builderis the most suitable solution. The Dataflow job builder provides a visual interface to design pipelines, allowing you to define transformations and load data into BigQuery. This approach is ideal for streaming data pipelines that require near real-time transformations and analysis. It ensures scalability across multiple regions and integrates seamlessly with Pub/Sub for event ingestion and BigQuery for analysis.

The best solution for creating a data pipeline with a visual interface for streaming event information from multiple Google Cloud regions into BigQuery for near real-time analysis with transformations isA. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.

Here's why:

Pub/Sub and Dataflow:

Pub/Sub is ideal for real-time message ingestion, especially from multiple regions.

Dataflow, particularly with the Dataflow job builder, provides a visual interface for creating data pipelines that can perform real-time stream processing and transformations.

The Dataflow job builder allows creating pipelines with visual tools, fulfilling the requirement of a visual interface.

Dataflow is built for real time streaming and applying transformations.

Let's break down why the other options are less suitable:

B. Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations:

This is a batch processing approach, not real-time.

Cloud Storage and scheduled jobs are not designed for near real-time analysis.

This does not meet the real time requirement of the question.

C. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery:

While Cloud Run can handle transformations, it requires more coding and is less scalable and manageable than Dataflow for complex streaming pipelines.

Cloud run does not provide a visual interface.

D. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub:

BigQuery subscriptions in Pub/Sub are for direct loading of Pub/Sub messages into BigQuery, without the ability to perform transformations.

This option does not provide any transformation functionality.

Therefore, Pub/Sub for ingestion and Dataflow with its job builder for visual pipeline creation and transformations is the most appropriate solution.

For regulatory requirements that mandate complete and manual control of data encryption, you should use customer-supplied encryption keys (CSEK). With CSEK, your company provides the encryption keys for data storage, and Google Cloud does not store or manage these keys. This approach ensures thatyour organization retains full control and responsibility over the encryption process, meeting strict regulatory compliance requirements.

Creating aBigQuery Cloud resource connectionto a remote model inVertex AIand using Gemini to summarize the data is the most efficient approach. This method allows you to seamlessly integrate BigQuery with the Gemini model via Vertex AI, avoiding the need to export data or perform manual steps. It ensures scalability for large datasets and minimizes data movement, leveraging Google Cloud's ecosystem for efficient data summarization and storage.

The storage system must balance cost, simplicity, and access patterns: high initial access, decreasing over time, with some files remaining popular. Google Cloud Storage offers tailored options for this:

Option A: Custom Object Lifecycle Management (OLM) policies (e.g., transition to Nearline after 30 days, Archive after 90 days) are effective but static. They don’t adapt to actual usage, so popular old files in Archive would incur high retrieval costs.

Option B: Autoclass automatically adjusts storage classes (Standard, Nearline, Coldline, Archive) based on object access patterns, not just age. It keeps frequently accessed files in Standard (low latency/cost for access) and moves inactive ones to cheaper classes, minimizing costs while preserving simplicity. This fits the “some files remain popular” nuance.

Option C: A Cloud Scheduler job to manually change classes daily is complex (requires scripting, monitoring), error-prone, and less cost-effective than automated solutions like Autoclass or OLM.

Comprehensive and Detailed In-Depth Explanation:

Forecasting product demand with seasonality requires a time series model, and BigQuery ML offers a scalable, serverless solution. Let’s analyze:

Option A: BigQuery ML’s time series models (e.g., ARIMA_PLUS) are designed for forecasting with seasonality and trends. The ML.FORECAST function generates predictions based on historical data, storing them in a table. This is scalable (no infrastructure) and integrates natively with BigQuery, ideal for ecommerce demand prediction.

Option B: Colab Enterprise with a custom Python model (e.g., Prophet) is flexible but requires coding, maintenance, and potentially exporting data, reducing scalability compared to BigQuery ML’s in-place processing.

Option C: Linear regression predicts continuous values but doesn’t handle seasonality or time series patterns effectively, making it unsuitable for demand forecasting.

Comprehensive and Detailed In-Depth Explanation:

The tool must be fully managed, support batch ETL, integrate with multiple Google Cloud services, and leverage Python skills.

Option A: Dataform is SQL-focused for ELT within BigQuery, not Python-centric, and lacks broad service integration for extraction.

Option B: Cloud Data Fusion is a visual ETL tool, not Python-focused, and requires more UI-based configuration than coding.

Option C: Cloud Composer (managed Apache Airflow) is fully managed, supports batch ETL via DAGs, integrates with various Google Cloud services (e.g., BigQuery, GCS) through operators, and allows custom Python code in tasks. It’s ideal for Python developers per the "Cloud Composer" documentation.

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.

Using theStorage Transfer Serviceis the best solution for migrating data from an on-premises NFS to Google Cloud. This service allows you to control bandwidth usage by configuring transfer speed limits and provides detailed reporting on the migration status. Storage Transfer Service is specifically designed for large-scale data migrations and supports scheduling, monitoring, and error handling, making it an efficient and reliable choice for your use case.

UsingCloud Composerto create Directed Acyclic Graphs (DAGs) is the best solution because it is a fully managed, scalable workflow orchestration service based on Apache Airflow. Cloud Composer allows you to define complex task dependencies and schedules while integrating seamlessly with Google Cloud services such as Cloud Storage, BigQuery, and Dataproc for Apache Spark jobs. This approach minimizes operational overhead, supports scheduling and automation, and provides an efficient and fully managed way to orchestrate your data pipelines.

Extract from Google Documentation: From "Cloud Composer Overview" (https://cloud.google.com/composer/docs ):"Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow, enabling you to schedule and automate complex data pipelines with dependencies across Google Cloud services like Cloud Storage, Dataproc, and BigQuery."

Partitioning the BigQuery table by month allows efficient querying of recent data for the first 6 months, reducing query costs. After 6 months, exporting the data toColdline storageminimizes storage costs for data that is rarely accessed but needs to be retained for compliance. Implementing a lifecycle policy in Cloud Storage automates the deletion of the data after 3 years, ensuring compliance while reducing administrative overhead. This approach balances cost efficiency and compliance requirements effectively.

UsingNearline storagein Cloud Storage is the best option for data that is accessed infrequently (such as once a month) but must be readily available within seconds when needed. Nearline offers a balance between low storage costs and quick retrieval times, making it ideal for scenarios like monthly analysis of historical data. It is specifically designed for infrequent access patterns while avoiding the higher retrieval costs and longer access times of Coldline or Archive storage.

Comprehensive and Detailed In-Depth Explanation:

The priorities are data integrity, recoverability after a regional disaster, low RPO (minimal data loss), and low latency for primary operations. Let’s analyze:

Option A: Multi-region backups store point-in-time snapshots in a separate region. With automated backups and transaction logs, RPO can be near-zero (e.g., minutes), and recovery is possible post-disaster. Primary operations remain in one zone, minimizing latency.

Option B: Regional HA (failover to another zone) with hourly cross-region backups protects against zone failures, but hourly backups yield an RPO of up to 1 hour—too high for valuable data. Manual backup management adds overhead.

Option C: Synchronous replication to another zone ensures zero RPO within a region but doesn’t protect against regional loss. Latency increases slightly due to sync writes across zones.

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.