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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q365-Q370):

NEW QUESTION # 365
You have a Cloud SQL for PostgreSQL instance in Region' with one read replica in Region2 and another read replica in Region3. An unexpected event in Region' requires that you perform disaster recovery by promoting a read replica in Region2. You need to ensure that your application has the same database capacity available before you switch over the connections.
What should you do?

A. Create two new read replicas from the new primary instance, one in Region3 and one in a new region.
B. Create a cascading read replica from the existing read replica in Region3.
C. Create a new read replica in Region1, promote the new read replica to be the primary instance, and enable zonal high availability.
D. Enable zonal high availability on the primary instance. Create a new read replica in a new region.

Answer: A

NEW QUESTION # 366
When you design a Google Cloud Bigtable schema it is recommended that you
_________.

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

Answer: B

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 # 367
Which of the following is NOT true about Dataflow pipelines?

A. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources
B. Dataflow pipelines can consume data from other Google Cloud services
C. Dataflow pipelines can be programmed in Java
D. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner

Answer: D

Explanation:
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/

NEW QUESTION # 368
You want to analyze hundreds of thousands of social media posts daily at the lowest cost and with the fewest steps.
You have the following requirements:
- You will batch-load the posts once per day and run them through the
Cloud Natural Language API.
- You will extract topics and sentiment from the posts.
- You must store the raw posts for archiving and reprocessing.
- You will create dashboards to be shared with people both inside and
outside your organization.
You need to store both the data extracted from the API to perform analysis as well as the raw social media posts for historical archiving. What should you do?

A. Feed to social media posts into the API directly from the source, and write the extracted data from the API into BigQuery.
B. Store the social media posts and the data extracted from the API in BigQuery.
C. Store the social media posts and the data extracted from the API in Cloud SQL.
D. Store the raw social media posts in Cloud Storage, and write the data extracted from the API into BigQuery.

Answer: D

Explanation:
Social media posts can images/videos which cannot be stored in bigquery/

NEW QUESTION # 369
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. Which two actions should you take? (Choose two.)

A. Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
B. Configure your Cloud Dataflow pipeline to use local execution
C. Increase the number of nodes in the Cloud Bigtable cluster
D. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions
E. Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable

Answer: A,E

NEW QUESTION # 370
......

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