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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Operationalizing machine learning models	20%	- Preparing data for ML 1. Feature engineering and data preparation 2. Handling structured and unstructured data - Deploying and maintaining ML models 1. Model serving and monitoring 2. Optimizing model performance and cost
Designing data processing systems	20%	- Designing for business requirements 1. Designing for reliability and fault tolerance 2. Designing for scalability and elasticity 3. Selecting appropriate storage solutions - Designing for regulatory and security requirements 1. Implementing access control and data protection 2. Ensuring data privacy and compliance
Building and operationalizing data processing systems	25%	- Building data pipelines 1. Ingesting data from various sources 2. Transforming and cleaning data 3. Orchestrating data workflows - Deploying and managing systems 1. Managing infrastructure and resources 2. Monitoring and logging data processes
Maintaining and automating data workloads	18%	- Resource optimization 1. Choosing appropriate compute and storage options 2. Cost management and resource allocation - Automation and repeatability 1. Automating deployment and updates 2. Implementing CI/CD for data systems
Ensuring solution quality and reliability	17%	- Testing and validating data systems 1. Data quality validation 2. Performance and scalability testing - Troubleshooting and optimization 1. Optimizing queries and workloads 2. Diagnosing performance issues

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Updated Google Professional-Data-Engineer CBT | Professional-Data-Engineer Simulation Questions

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Google Certified Professional Data Engineer Exam Sample Questions (Q168-Q173):

NEW QUESTION # 168
The _________ for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline.

A. BiqQuery API
B. Cloud Dataflow connector
C. DataFlow SDK
D. BigQuery Data Transfer Service

Answer: B

Explanation:
Explanation
The Cloud Dataflow connector for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline. You can use the connector for both batch and streaming operations.
Reference: https://cloud.google.com/bigtable/docs/dataflow-hbase

NEW QUESTION # 169
When you design a Google Cloud Bigtable schema it is recommended that you
_________.

A. Avoid schema designs that require atomicity across rows
B. Avoid schema designs that are based on NoSQL concepts
C. Create schema designs that are based on a relational database design
D. Create schema designs that require atomicity across rows

Answer: A

Explanation:
All operations are atomic at the row level. For example, if you update two rows in a table, it's possible that one row will be updated successfully and the other update will fail. Avoid schema designs that require atomicity across rows.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys

NEW QUESTION # 170
Which role must be assigned to a service account used by the virtual machines in a Dataproc cluster so they can execute jobs?

A. Dataproc Editor
B. Dataproc Worker
C. Dataproc Viewer
D. Dataproc Runner

Answer: B

Explanation:
Service accounts used with Cloud Dataproc must have Dataproc/Dataproc Worker role (or have all the permissions granted by Dataproc Worker role).
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes

NEW QUESTION # 171
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics.
Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded.
The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

A. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
B. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
C. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
D. Add capacity (memory and disk space) to the database server by the order of 200.

Answer: A

NEW QUESTION # 172
Your chemical company needs to manually check documentation for customer order. You use a pull subscription in Pub/Sub so that sales agents get details from the order. You must ensure that you do not process orders twice with different sales agents and that you do not add more complexity to this workflow. What should you do?

A. Use a Deduphcate PTransform in Dataflow before sending the messages to the sales agents.
B. Use Pub/Sub exactly-once delivery in your pull subscription.
C. Create a new Pub/Sub push subscription to monitor the orders processed in the agent's system.
D. Create a transactional database that monitors the pending messages.

Answer: B

Explanation:
Pub/Sub exactly-once delivery is a feature that guarantees that subscriptions do not receive duplicate deliveries of messages based on a Pub/Sub-defined unique message ID. This feature is only supported by the pull subscription type, which is what you are using in this scenario. By enabling exactly-once delivery, you can ensure that each order is processed only once by a sales agent, and that no order is lost or duplicated. This also simplifies your workflow, as you do not need to create a separate database or subscription to monitor the pending or processed messages. Reference:
Exactly-once delivery | Cloud Pub/Sub Documentation
Cloud Pub/Sub Exactly-once Delivery feature is now Generally Available (GA)

NEW QUESTION # 173
......

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