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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. Using Dataproc, Dataflow, and BigQuery SQL 2. ETL/ELT pipeline design - Data ingestion and integration 1. Streaming ingestion (Pub/Sub, Dataflow) 2. Batch ingestion pipelines (BigQuery, Cloud Storage)
Ensuring solution quality	28%	- Security and governance 1. IAM and access control in GCP 2. Data governance and compliance - Reliability and performance 1. Fault tolerance and recovery strategies 2. Monitoring pipelines and workloads
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. Event-driven vs batch architectures 2. Latency, throughput, and consistency trade-offs
Operationalizing machine learning models	26%	- Model deployment and monitoring 1. Online vs batch prediction 2. Model monitoring and drift detection - ML pipeline integration 1. Feature engineering and feature stores 2. Vertex AI pipeline deployment

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Google Certified Professional Data Engineer Exam Sample Questions (Q22-Q27):

NEW QUESTION # 22
You've migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet files (on average 200-400 MB size each). You see some degradation in performance after the migration to Dataproc, so you'd like to optimize for it. You need to keep in mind that your organization is very cost-sensitive, so you'd like to continue using Dataproc on preemptibles (with
2 non-preemptible workers only) for this workload. What should you do?

A. Switch to TFRecords formats (appr. 200MB per file) instead of parquet files.
B. Switch from HDDs to SSDs, override the preemptible VMs configuration to increase the boot disk size.
C. Increase the size of your parquet files to ensure them to be 1 GB minimum.
D. Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.

Answer: B

Explanation:
In order to increase performance switch to SSD which will be costly, so to tackle this increase the boot disk size, bootsize is worker node cache size 100 Gb.

NEW QUESTION # 23
Google Cloud Bigtable indexes a single value in each row. This value is called the _______.

A. row key
B. master key
C. unique key
D. primary key

Answer: A

Explanation:
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key.
Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 24
You are building an application to share financial market data with consumers, who will receive data feeds. Data is collected from the markets in real time. Consumers will receive the data in the following ways:
- Real-time event stream
- ANSI SQL access to real-time stream and historical data
- Batch historical exports
Which solution should you use?

A. Cloud Pub/Sub, Cloud Dataproc, Cloud SQL
B. Cloud Dataproc, Cloud Dataflow, BigQuery
C. Cloud Dataflow, Cloud SQL, Cloud Spanner
D. Cloud Pub/Sub, Cloud Storage, BigQuery

Answer: D

NEW QUESTION # 25
You are using Workflows to call an API that returns a 1 KB JSON response, apply some complex business logic on this response, wait for the logic to complete, and then perform a load from a Cloud Storage file to BigQuery. The Workflows standard library does not have sufficient capabilities to perform your complex logic, and you want to use Python's standard library instead. You want to optimize your workflow for simplicity and speed of execution. What should you do?

A. Invoke a Cloud Function instance that uses Python to apply the logic on your JSON file.
B. Create a Cloud Composer environment and run the logic in Cloud Composer.
C. Invoke a subworkflow in Workflows to apply the logic on your JSON file.
D. Create a Dataproc cluster, and use PySpark to apply the logic on your JSON file.

Answer: A

NEW QUESTION # 26
What are two of the characteristics of using online prediction rather than batch prediction?

A. It is optimized to minimize the latency of serving predictions.
B. Predictions are written to output files in a Cloud Storage location that you specify.
C. It is optimized to handle a high volume of data instances in a job and to run more complex models.
D. Predictions are returned in the response message.

Answer: A,D

Explanation:
Online prediction
Optimized to minimize the latency of serving predictions.
Predictions returned in the response message.
Batch prediction
Optimized to handle a high volume of instances in a job and to run more complex models.
Predictions written to output files in a Cloud Storage location that you specify.
Reference:
https://cloud.google.com/ml-engine/docs/prediction-overview#online_prediction_versus_batch_prediction

NEW QUESTION # 27
......

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