Professional-Data-Engineer Exam Dumps - Google Cloud Certified Questions and Answers

The key requirements are:

Migrate Spark 3 batch job.

Minimally change the job (reads from GCS, writes to BQ – standard for Spark on GCP).

Job optimized for Spark (specific executor vCPU/memory).

Ability to choose similar executor settings.

Minimize installation and management effort.

Dataproc Serverless (Option A)is designed for these use cases.

Spark 3 Support:Dataproc Serverless supports various Spark runtimes, including Spark 3.

Minimal Changes:Spark jobs reading from GCS and writing to BigQuery (using the Spark-BigQuery connector) are standard. Minimal code changes are generally needed.

Customizable Resources:Dataproc Serverless allows you to specify resources for the driver and executors, including vCPU and memory. You can configure these to match your optimized on-premises settings (e.g., 8 vCPU, 16 GB memory per executor, though specific available configurations should be checked).

Minimal Installation and Management:This is the core benefit of "serverless." You don't need to provision, manage, or scale clusters. You submit your batch job, and Google Cloud handles the underlying infrastructure. This significantly reduces operational overhead.

Let's analyze why other options are less suitable:

B (Compute Engine VM):You would need to manually install Spark, configure it, manage dependencies, and manage the VM itself. This is high management effort.

C (Google Kubernetes Engine cluster):While you can run Spark on GKE (e.g., using Spark on Kubernetes operator), it involves managing the GKE cluster, Spark deployment configurations, Docker images, etc. This is also significant management effort, more than Dataproc Serverless.

D (Dataproc cluster):A standard Dataproc cluster provides more control than serverless but also requires more management (creating, scaling, and managing the cluster lifecycle). Dataproc Serverless is specifically designed to minimize this management for batch jobs. Given the "minimize installation and management effort" requirement, serverless is preferred over a managed cluster if it meets the job's needs.

To design a data mesh architecture using Dataplex to eliminate bottlenecks caused by a central data platform team, consider the following:

Data Mesh Architecture:

Data mesh promotes a decentralized approach where domain teams manage their own data pipelines and assets, increasing agility and reducing bottlenecks.

Dataplex Lakes and Zones:

Lakesin Dataplex are logical containers for managing data at scale, andzonesare subdivisions within lakes for organizing data based on domains, teams, or other criteria.

Domain and Team Management:

By creating a lake for each team and zones for each domain, each team can independently manage their data assets without relying on the central data platform team.

This setup aligns with the principles of data mesh, promoting ownership and reducing delays in data processing and insights.

Implementation Steps:

Create Lakes and Zones:

Create separate lakes in Dataplex for each team (analytics and data science).

Within each lake, create zones for the different domains (airlines, hotels, ride-hailing).

Attach BigQuery Datasets:

Attach the BigQuery datasets created by the respective teams as assets to their corresponding zones.

Decentralized Management:

Allow each domain to manage their own zone's data assets, providing them with the autonomy to update and maintain their pipelines without depending on the central team.

Reference Links:

Dataplex Documentation

BigQuery Documentation

Data Mesh Principles

If you know the set of all possible feature values of a column and there are only a few of them, you can use categorical_column_with_vocabulary_list. Each key in the list will get assigned an auto-incremental ID starting from 0.

What if we don't know the set of possible values in advance? Not a problem. We can use categorical_column_with_hash_bucket instead. What will happen is that each possible value in the feature column occupation will be hashed to an integer ID as we encounter them in training.

Service accounts used with Cloud Dataproc must have Dataproc/Dataproc Worker role (or have all the permissions granted by Dataproc Worker role).