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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Maintaining and automating data workloads (~15% of the exam)	15%	- Monitoring data pipelines and data processes 1. Managing quotas and resource usage 2. Logging, monitoring, and troubleshooting - Automating data processes 1. Workflow orchestration 2. Scheduling jobs 3. Continuous integration and continuous deployment (CI/CD) - Designing for reliability and fidelity 1. Planning for monitoring and alerting 2. Performing data quality and validation checks 3. Recovering from failures
Ingesting and processing the data (~20% of the exam)	20%	- Performing security considerations 1. Identity and Access Management (IAM) 2. Auditing, privacy, and compliance 3. Data encryption - Building and maintaining data structures and databases 1. Defining data lifecycle 2. Planning for analytical and operational use cases - Deploying and operationalizing the pipelines 1. CI/CD for data pipelines 2. Job automation and orchestration (Cloud Composer, Workflows)
Designing data processing systems (~30% of the exam)	30%	- Designing data processing resources 1. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) 2. Cost optimization 3. Cluster sizing and autoscaling - Selecting appropriate storage technologies 1. Mapping storage options to business requirements 2. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB - Designing data pipelines 1. Data acquisition and import 2. Integrating with new data sources 3. Streaming (e.g., windowing, late arriving data) 4. Processing logic 5. AI data enrichment 6. Batch processing
Preparing and using data for analysis (~15% of the exam)	15%	- Preparing data for visualization 1. Connecting to Looker and other BI tools 2. Preparing data for reporting and dashboards - Sharing data securely 1. Publishing datasets 2. Data sharing and collaboration
Storing the data (~20% of the exam)	20%	- Selecting storage systems 1. Lifecycle management of data 2. Planning for storage costs and performance 3. Analyzing data access patterns - Designing for a data platform 1. Building a federated governance model for distributed data systems 2. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage - Using a data lake 1. Managing the lake (data discovery, access, cost controls) 2. Monitoring the data lake 3. Processing data - Planning for using a data warehouse 1. Deciding the degree of data normalization 2. Defining architecture to support data access patterns 3. Designing the data model 4. Mapping business requirements

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Google Certified Professional Data Engineer Exam Sample Questions (Q337-Q342):

NEW QUESTION # 337
You are preparing an organization-wide dataset. You need to preprocess customer data stored in a restricted bucket in Cloud Storage. The data will be used to create consumer analyses. You need to comply with data privacy requirements. What should you do?

A. Use Dataflow and Cloud KMS to encrypt sensitive fields and write the encrypted data in BigQuery. Share the encryption key by following the principle of least privilege.
B. Use the Cloud Data Loss Prevention API and Dataflow to detect and remove sensitive fields from the data in Cloud Storage. Write the filtered data in BigQuery.
C. Use customer-managed encryption keys (CMEK) to directly encrypt the data in Cloud Storage.
Use federated queries from BigQuery. Share the encryption key by following the principle of least privilege.
D. Use Dataflow and the Cloud Data Loss Prevention API to mask sensitive data. Write the processed data in BigQuery.

Answer: D

NEW QUESTION # 338
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

A. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
B. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
C. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
D. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role

Answer: B

NEW QUESTION # 339
All Google Cloud Bigtable client requests go through a front-end server ______ they are sent to a Cloud Bigtable node.

A. after
B. once
C. only if
D. before

Answer: D

Explanation:
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.

NEW QUESTION # 340
You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name from the data source and then write to an output PCollection.
Tom,555 X street
Tim,553 Y street
Sam, 111 Z street
Which operation is best suited for the above data processing requirement?

A. ParDo
B. Data extraction
C. Sink API
D. Source API

Answer: A

Explanation:
In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each element in your PCollection.
Reference: https://cloud.google.com/dataflow/model/par-do

NEW QUESTION # 341
You are part of a healthcare organization where data is organized and managed by respective data owners in various storage services. As a result of this decentralized ecosystem, discovering and managing data has become difficult You need to quickly identify and implement a cost-optimized solution to assist your organization with the following
* Data management and discovery
* Data lineage tracking
* Data quality validation
How should you build the solution?

A. Use BigOuery to track data lineage, and use Dataprep to manage data and perform data quality validation.
B. Build a new data discovery tool on Google Kubernetes Engine that helps with new source onboarding and data lineage tracking.
C. Use Dataplex to manage data, track data lineage, and perform data quality validation.
D. Use BigLake to convert the current solution into a data lake architecture.

Answer: C

Explanation:
Dataplex is a Google Cloud service that provides a unified data fabric for data lakes and data warehouses. It enables data governance, management, and discovery across multiple data domains, zones, and assets.
Dataplex also supports data lineage tracking, which shows the origin and transformation of data over time.
Dataplex also integrates with Dataprep, a data preparation and quality tool that allows users to clean, enrich, and transform data using a visual interface. Dataprep can also monitor data quality and detect anomalies using machine learning. Therefore, Dataplex is the most suitable solution for the given scenario, as it meets all the requirements of data management and discovery, data lineage tracking, and data quality validation. References:
Dataplex overview
Automate data governance, extend your data fabric with Dataplex-BigLake integration Dataprep documentation

NEW QUESTION # 342
......

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