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

Creating external tables over the Parquet files in Cloud Storage allows you to perform SQL-based analysis and joins with data already in BigQuery without needing to load the files into BigQuery. This approach is efficient for a one-time analysis as it avoids the time and cost associated with loading large volumes of data into BigQuery. External tables provide seamless integration with Cloud Storage, enabling quick and cost-effective analysis of data stored in Parquet format.

Online transactions:Storing the transactional data inBigQueryis ideal because BigQuery is a serverless data warehouse optimized for querying and analyzing structured data at scale. It supports SQL queries and is suitable for structured transactional data.

Customer feedback:Storing customer feedback inCloud Storageis appropriate as it allows you to store unstructured text files reliably and at a low cost. Cloud Storage also integrates well with data processing and ML tools for further analysis.

Social media activity:Storing real-time social media activity inBigQueryis optimal because BigQuery supports streaming inserts, enabling real-time ingestion and analysis of data. This allows immediate analysis and integration into dashboards or ML pipelines.

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.

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.

Using Analytics Hub to create a private exchange with data egress restrictions ensures controlled sharing of the dataset while minimizing the risk of unauthorized copying. This approach allows you to provide secure, managed access to the dataset without giving direct access to the raw data. The egress restriction ensures that data cannot be exported or copied outside the designated boundaries. Additionally, this solution provides a reusable framework that simplifies future data sharing with other teams or projects while maintaining strict data governance.

Extract from Google Documentation: From "Analytics Hub Overview" (https://cloud.google.com/analytics-hub/docs ):"Analytics Hub enables secure, controlled data sharing with private exchanges. Combine with organization policies like restrictDataEgress to prevent data copying, providing a reusable framework for sharing BigQuery datasets across teams."

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.

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.

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.

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.

Scheduling the creation of asnapshot of the tableweekly ensures that you have a point-in-time backup of the table. In case of accidental deletion, you can re-create the table from the snapshot. Additionally, BigQuery'stime travelfeature allows you to recover data from up to seven days prior to deletion. Combining snapshots with time travel provides a robust solution for preventing data loss and ensuring reporting continuity for critical tables. This approach minimizes risks while offering flexibility for recovery.

Comprehensive and Detailed In-Depth Explanation:

The strategy requires storage in two specific regions with automated high availability (HA). Cloud Storage location options dictate the solution:

Option A: A dual-region bucket (e.g., us-west1 and us-east1) replicates data synchronously across two user-specified regions, ensuring HA without manual intervention. It’s fully automated and meets the requirement.

Option B: Two single-region buckets with gcloud storage replication is manual, not automated, and lacks real-time HA (requires scripting and monitoring).

Option C: Multi-region buckets (e.g., us) span multiple regions within a geography but don’t let you specify exactly two regions, potentially violating the restriction.

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.

Using Database Migration Service (DMS) to replicate the MySQL database to a Cloud SQL for MySQL instance is the best approach. DMS is a fully managed service designed for migrating databases to Google Cloud with minimal downtime and cost. It supports continuous data replication, ensuring data integrity during the migration process, and handles schema and data transfer efficiently. This solution is particularly suited for large tables and transactional data, as it maintains real-time synchronization between the source and target databases, minimizing downtime for the migration.

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."

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.

Extract from Google Documentation: From "Monitoring Dataflow Jobs" (https://cloud.google.com/dataflow/docs/guides/monitoring-jobs ):"To troubleshoot a failed Dataflow job quickly, go to the Dataflow Jobs page in the Google Cloud Console, where you can view job logs and worker logs to identify errors and their root causes."

Using Dataflow with a Python-coded Directed Acyclic Graph (DAG) is the most efficient solution for generating a weekly aggregated sales report based on a large volume of data. Dataflow is optimized for large-scale data processing and can handle aggregation efficiently. Python allows you to customize the pipeline logic, and Cloud Scheduler enables you to automate the process to run weekly. This approach ensures scalability, efficiency, and the ability to process large datasets in a cost-effective manner.

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.

Using a Colab Enterprise notebook connected to BigQuery provides a managed, collaborative environment ideal for complex statistical calculations, Python packages, and visualizations. Colab Enterprise supports Python libraries for advanced analytics and offers seamless integration with BigQuery for querying large datasets. It allows teams to collaboratively develop and share analyses while taking advantage of its visualization capabilities. This approach is particularly suitable for tasks involving sophisticated computations and custom visualizations.

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.

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.

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.

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.