Google Associate-Data-Practitioner - Google Cloud Associate Data Practitioner (ADP Exam)

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.

UsingDataflowto create a streaming pipeline that includes validation and transformation steps is the most efficient and scalable approach for real-time analysis. Dataflow is optimized for high-volume data processing and allows you to apply validation and cleaning logic as the data flows through the pipeline. This ensures that only clean, validated data is loaded into BigQuery, supporting real-time analysis while handling high data volumes effectively.

Loading the data into BigQuery using aCloud Run functionand creating aBigQuery remote functionthat invokes theCloud Translation APIis a serverless and efficient approach. With this setup, you can use ascheduled queryin BigQuery to invoke the remote function and translate new product reviews on a regular basis. This solution requires minimal maintenance, as BigQuery handles storage and querying, and the Cloud Translation API provides accurate translations without the need for custom ML model development.

Using Google-managed encryption keys (GMEK) is the best choice when you want to encrypt sensitive data in Cloud Storage without the operational overhead of managing encryption keys. GMEK is the default encryption mechanism in Google Cloud, and it ensures that data is automatically encrypted at rest with no additional setup or maintenance required. It provides strong security while eliminating the need for manual key management.

Google Cloud encrypts all data at rest by default, and the simplest way to avoid key management overhead is to use Google-managed encryption keys (GMEK).

Option A: GMEK is fully managed by Google, requiring no user intervention, and meets the requirement of no operational overhead while ensuring encryption.

Option B: CMEK requires managing keys in Cloud KMS, adding operational overhead.

Option C: CSEK requires users to supply and manage keys externally, increasing complexity significantly.

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.

A single-zone outage requires high availability across zones or regions. Cloud Storage offers location-based redundancy options:

Option A: Cloud CDN caches content for web delivery but doesn’t protect against underlying storage outages—it’s for performance, not availability of the source data.

Option B: Object Versioning retains old versions of objects, protecting against overwrites or deletions, but doesn’t ensure availability during a zone failure (still tied to one location).

Option C: Multi-region buckets (e.g., us or eu) replicate data across multiple regions, ensuring accessibility even if a single zone or region fails. This provides the highest availability for sensitive data in a pipeline.

UsingDataproc workflow templatesis a fully-managed and straightforward solution for defining and scheduling your Spark job on a Dataproc cluster. Workflow templates allow you to automate the execution of Spark jobs with predefined steps, including data processing and report generation. You can integrate email notifications by adding a step to the workflow that sends the report using tools like a Cloud Function or external email service. This approach minimizes complexity while leveraging Dataproc's managed capabilities for batch processing.

Comprehensive and Detailed in Depth Explanation:

Why C is correct:Dataform is a managed data transformation service that allows you to define data pipelines using SQL and JavaScript.

It provides version control, modular code development, and data quality checks.

Why other options are incorrect:A: Cloud Composer is an orchestration tool, not a data transformation tool.

B: Scheduled queries are not suitable for complex ETL pipelines.

D: Dataproc requires setting up a Spark cluster and writing code, which is more complex than using Dataform.

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.

Implementingflow control on the subscribersallows the subscriber applications to manage message processing during activity spikes by controlling the rate at which messages are pulled and processed. This prevents overwhelming the subscribers and ensures that messages are acknowledged within the deadline. Flow control helps maintain the stability of your pipeline during high-traffic periods without dropping or delaying messages unnecessarily.

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.