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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q67-Q72):

NEW QUESTION # 67
You are designing a cloud-native historical data processing system to meet the following conditions:
The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis

tools including Cloud Dataproc, BigQuery, and Compute Engine.
A streaming data pipeline stores new data daily.

Peformance is not a factor in the solution.

The solution design should maximize availability.

How should you design data storage for this solution?

Answer: D


NEW QUESTION # 68
You are building a Dataflow pipeline to ingest customer feedback. Before loading to your data warehouse, you must validate email addresses and enrich unstructured comment strings with a generative AI sentiment classification. Invalid records need to be routed for manual review. How should you implement this pipeline?

Answer: C

Explanation:
Applying a ParDo transform enables per-record validation directly in the streaming or batch pipeline, ensuring invalid data is detected early. Using the RunInference transform integrates generative AI sentiment classification natively within Dataflow for scalable, low-latency enrichment. Side outputs allow clean separation of valid and invalid records, supporting downstream loading and manual review without additional post-processing steps.


NEW QUESTION # 69
When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?

Answer: B

Explanation:
Cloud Bigtable is not a relational database. It does not support SQL queries, joins, or multi- row transactions. It is not a good solution for less than 1 TB of data.
Reference:
https://cloud.google.com/bigtable/docs/overview#title_short_and_other_storage_options


NEW QUESTION # 70
What are all of the BigQuery operations that Google charges for?

Answer: C

Explanation:
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference:
https://cloud.google.com/bigquery/pricing


NEW QUESTION # 71
You have several different unstructured data sources, within your on-premises data center as well as in the cloud. The data is in various formats, such as Apache Parquet and CSV. You want to centralize this data in Cloud Storage. You need to set up an object sink for your data that allows you to use your own encryption keys. You want to use a GUI-based solution. What should you do?

Answer: B

Explanation:
To centralize unstructured data from various sources into Cloud Storage using a GUI-based solution while allowing the use of your own encryption keys, Cloud Data Fusion is the most suitable option. Here's why:
Cloud Data Fusion:
Cloud Data Fusionis a fully managed, cloud-native data integration service that helps in building and managing ETL pipelines with a visual interface.
It supports a wide range of data sources and formats, including Apache Parquet and CSV, and provides a user- friendly GUI for pipeline creation and management.
Custom Encryption Keys:
Cloud Data Fusion allows the use of customer-managed encryption keys (CMEK) for data encryption, ensuring that your data is securely stored according to your encryption policies.
Centralizing Data:
Cloud Data Fusion simplifies the process of moving data from on-premises and cloud sources into Cloud Storage, providing a centralized repository for your unstructured data.
Steps to Implement:
Set Up Cloud Data Fusion:
Deploy a Cloud Data Fusion instance and configure it to connect to your various data sources.
Create ETL Pipelines:
Use the GUI to create data pipelines that extract data from your sources and load it into Cloud Storage.
Configure the pipelines to use your custom encryption keys.
Run and Monitor Pipelines:
Execute the pipelines and monitor their performance and data movement through the Cloud Data Fusion dashboard.
Reference Links:
Cloud Data Fusion Documentation
Using Customer-Managed Encryption Keys (CMEK)


NEW QUESTION # 72
......

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