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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Building and operationalizing data processing systems	24%	- Data processing and transformation 1. ETL/ELT pipeline design 2. Using Dataproc, Dataflow, and BigQuery SQL - Data ingestion and integration 1. Streaming ingestion (Pub/Sub, Dataflow) 2. Batch ingestion pipelines (BigQuery, Cloud Storage)
Designing data processing systems	22%	- Data architecture and storage design 1. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse) 2. Designing scalable and cost-effective data models - Batch and streaming data processing design 1. Latency, throughput, and consistency trade-offs 2. Event-driven vs batch architectures
Operationalizing machine learning models	26%	- ML pipeline integration 1. Feature engineering and feature stores 2. Vertex AI pipeline deployment - Model deployment and monitoring 1. Model monitoring and drift detection 2. Online vs batch prediction
Ensuring solution quality	28%	- Security and governance 1. Data governance and compliance 2. IAM and access control in GCP - Reliability and performance 1. Monitoring pipelines and workloads 2. Fault tolerance and recovery strategies

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Google Certified Professional Data Engineer Exam Sample Questions (Q301-Q306):

NEW QUESTION # 301
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on non-key columns. What should you do?

A. Use Cloud Spanner for storage. Add secondary indexes to support query patterns.
B. Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.
C. Use Cloud SQL for storage. Add secondary indexes to support query patterns.
D. Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.

Answer: A

Explanation:
Spanner allows transaction tables to scale horizontally and secondary indexes for range queries.

NEW QUESTION # 302
You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of data. What should you do?

A. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
B. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
Deploy the models using Cloud Dataproc. Call the model from your application.
C. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.
D. Build and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences. Deploy the models using Cloud Dataproc. Call the models from your application.

Answer: A

NEW QUESTION # 303
You have two projects where you run BigQuery jobs:
* One project runs production jobs that have strict completion time SLAs. These are high priority jobs that must have the required compute resources available when needed. These jobs generally never go below a 300 slot utilization, but occasionally spike up an additional 500 slots.
* The other project is for users to run ad-hoc analytical queries. This project generally never uses more than
200 slots at a time. You want these ad-hoc queries to be billed based on how much data users scan rather than by slot capacity.
You need to ensure that both projects have the appropriate compute resources available. What should you do?

A. Create two reservations, one for each of the projects. For the SLA project, use an Enterprise Edition with a baseline of 300 slots and enable autoscaling up to 500 slots. For the ad-hoc project, configure on- demand billing.
B. Create two Enterprise Edition reservations, one for each of the projects. For the SLA project, set a baseline of 300 slots and enable autoscaling up to 500 slots. For the ad-hoc project, set a reservation baseline of 0 slots and set the ignore_idle_slot3 flag to False.
C. Create two Enterprise Edition reservations, one for each of the projects. For the SLA project, set a baseline of 800 slots. For the ad-hoc project, enable autoscaling up to 200 slots.
D. Create a single Enterprise Edition reservation for both projects. Set a baseline of 300 slots. Enable autoscaling up to 700 slots.

Answer: A

Explanation:
To ensure that both production jobs with strict SLAs and ad-hoc queries have appropriate compute resources available while adhering to cost efficiency, setting up separate reservations and billing models for each project is the best approach. Here's why option B is the best choice:
* Separate Reservations for SLA and Ad-hoc Projects:
* Creating two separate reservations allows for dedicated resource management tailored to the needs of each project.
* The production project requires guaranteed slots with the ability to scale up as needed, while the ad-hoc project benefits from on-demand billing based on data scanned.
* Enterprise Edition Reservation for SLA Project:
* Setting a baseline of 300 slots ensures that the SLA project has the minimum required resources.
* Enabling autoscaling up to 500 additional slots allows the project to handle occasional spikes in workload without compromising on SLAs.
* On-Demand Billing for Ad-hoc Project:
* Using on-demand billing for the ad-hoc project ensures cost efficiency, as users are billed based on the amount of data scanned rather than reserved slot capacity.
* This model suits the less predictable and often lower-utilization nature of ad-hoc queries.
Steps to Implement:
* Set Up Enterprise Edition Reservation for SLA Project:
* Create a reservation with a baseline of 300 slots.
* Enable autoscaling to allow up to an additional 500 slots as needed.
* Configure On-Demand Billing for Ad-hoc Project:
* Ensure that the ad-hoc project is set up to use on-demand billing, which charges based on data scanned by the queries.
* Monitor and Adjust:
* Continuously monitor the usage and performance of both projects to ensure that the configurations meet the needs and make adjustments as necessary.
Reference Links:
* BigQuery Slot Reservations
* BigQuery On-Demand Pricing

NEW QUESTION # 304
You are on the data governance team and are implementing security requirements to deploy resources. You need to ensure that resources are limited to only the europe-west 3 region You want to follow Google-recommended practices What should you do?

A. Create a Cloud Function to monitor all resources created and automatically destroy the ones created outside the europe-west3 region.
B. Set the constraints/gcp. resourceLocations organization policy constraint to in: europe-west3-locations.
C. Set the constraints/gcp. resourceLocations organization policy constraint to in:eu-locations.
D. Deploy resources with Terraform and implement a variable validation rule to ensure that the region is set to the europe-west3 region for all resources.

Answer: B

Explanation:
To ensure that resources are limited to only the europe-west3 region, you should set the organization policy constraint constraints/gcp.resourceLocations to in:europe-west3-locations. This policy restricts the deployment of resources to the specified locations, which in this case is the europe-west3 region. By setting this policy, you enforce location compliance across your Google Cloud resources, aligning with the best practices for data governance and regulatory compliance.
References:
* Professional Data Engineer Certification Exam Guide | Learn - Google Cloud1.
* Preparing for Google Cloud Certification: Cloud Data Engineer2.
* Professional Data Engineer Certification | Learn | Google Cloud3.
3: Professional Data Engineer Certification | Learn | Google Cloud 2: Preparing for Google Cloud Certification: Cloud Data Engineer 1: Professional Data Engineer Certification Exam Guide | Learn - Google Cloud

NEW QUESTION # 305
You are using Cloud Bigtable to persist and serve stock market data for each of the major indices. To serve the trading application, you need to access only the most recent stock prices that are streaming in How should you design your row key and tables to ensure that you can access the data with the most simple query?

A. Create one unique table for all of the indices, and then use the index and timestamp as the row key design
B. For each index, have a separate table and use a reverse timestamp as the row key design
C. Create one unique table for all of the indices, and then use a reverse timestamp as the row key design.
D. For each index, have a separate table and use a timestamp as the row key design

Answer: A

NEW QUESTION # 306
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