Google Updated Professional-Data-Engineer Exam Questions and Answers by hassan

Comprehensive and Detailed Explanation:

This question requires a solution that excels at large-scale, stateful stream processing with sophisticated windowing and handling of out-of-order data.

Option C is the correct answer because this architecture is perfectly suited for the requirements.

Pub/Sub is the global, scalable ingestion service for continuous event data.

Dataflow, with the Apache Beam programming model, is specifically designed for complex stream processing. It has powerful, built-in support for different windowing strategies (including session windows for sessionization) and sophisticated triggers for handling late-arriving data. Its serverless nature ensures it scales to handle millions of events.

BigQuery is the ideal sink for the processed data, enabling large-scale analytics for the recommendation engine.

Option A is incorrect as Firebase and Cloud Run are more suited for application backends and are not designed for complex, stateful data processing pipelines at this scale.

Option B is incorrect because it describes a batch processing pattern. Using Cloud Storage for ingestion and Dataproc for batch processing would introduce high latency, failing the "low latency" requirement.

Option D is incorrect because Cloud Data Fusion is primarily a batch-oriented ETL/ELT tool, and Cloud SQL is not an analytical data warehouse capable of handling this scale of data for analytics.

Reference (Google Cloud Documentation Concepts):

This is another example of the canonical Pub/Sub -> Dataflow -> BigQuery streaming analytics pattern. The Apache Beam Programming Guide (which is the foundation for Dataflow) extensively covers concepts like Windowing (specifically SessionWindows) and Triggers for handling late data. These features are critical for accurately processing real-world event streams like clickstream data and are core strengths of Dataflow.

To provide a cost-effective storage and processing solution that allows data scientists to explore data similarly to using the on-premises HDFS cluster with SQL on the Hive query engine, deploying a Dataproc cluster is the best choice. Here’s why:

Compatibility with Hive:

Dataprocis a fully managed Apache Spark and Hadoop service that provides native support for Hive, making it easy for data scientists to run SQL queries on the data as they would in an on-premises Hadoop environment.

This ensures that the transition to Google Cloud is smooth, with minimal changes required in the workflow.

Cost-Effective Storage:

Storing the ORC files inCloud Storageis cost-effective and scalable, providing a reliable and durable storage solution that integrates seamlessly with Dataproc.

Cloud Storage allows you to store large datasets at a lower cost compared to other storage options.

Hive Integration:

Dataproc supports running Hive directly, which is essential for data scientists familiar with SQL on the Hive query engine.

This setup enables the use of existing Hive queries and scripts without significant modifications.

Steps to Implement:

Copy ORC Files to Cloud Storage:

Transfer the ORC files from the on-premises HDFS cluster to Cloud Storage, ensuring they are organized in a similar directory structure.

Deploy Dataproc Cluster:

Set up a Dataproc cluster configured to run Hive. Ensure that the cluster has access to the ORC files stored in Cloud Storage.

Configure Hive:

Configure Hive on Dataproc to read from the ORC files in Cloud Storage. This can be done by setting up external tables in Hive that point to the Cloud Storage location.

Provide Access to Data Scientists:

Grant the data scientist team access to the Dataproc cluster and the necessary permissions to interact with the Hive tables.

Reference Links:

Dataproc Documentation

Hive on Dataproc

Google Cloud Storage Documentation

To improve the performance of querying a large BigQuery table with filters on countryname and username, clustering the table by these fields is the most effective approach. Here’s why option C is the best choice:

Clustering in BigQuery:

Clustering organizes data based on the values in specified columns. This can significantly improve query performance by reducing the amount of data scanned during query execution.

Clustering by countryname and username means that data is physically sorted and stored together based on these fields, allowing BigQuery to quickly locate and read only the relevant data for queries using these filters.

Filter Efficiency:

With the table clustered by countryname and username, queries that filter on these columns can benefit from efficient data retrieval, reducing the amount of data processed and speeding up query execution.

This directly addresses the performance issue of the dashboard queries that apply filters on these fields.

Steps to Implement:

Redesign the Table:

Create a new table with clustering on countryname and username:

CREATE TABLE project.dataset.new_table

CLUSTER BY countryname, username AS

SELECT * FROM project.dataset.customer_order;

Migrate Data:

Transfer the existing data from the original table to the new clustered table.

Update Queries:

Modify the dashboard queries to reference the new clustered table.

Reference Links:

BigQuery Clustering Documentation

Optimizing Query Performance