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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q338-Q343):

NEW QUESTION # 338
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

Answer: D

Explanation:
Explanation
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers.
You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers


NEW QUESTION # 339
You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time. What should you do?

Answer: C

Explanation:
Pubsub for realtime, Dataflow for pipeline, Bigquery for analytics.


NEW QUESTION # 340
You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query - -dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall data. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?

Answer: D

Explanation:
Explanation


NEW QUESTION # 341
Case Study 2 - MJTelco
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to- many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
* Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
* Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production - to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
* Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
* Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
* Provide reliable and timely access to data for analysis from distributed research workers
* Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
* Ensure secure and efficient transport and storage of telemetry data
* Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
* Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day
* Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
MJTelco's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?

Answer: D


NEW QUESTION # 342
You store historic data in Cloud Storage. You need to perform analytics on the historic dat
a. You want to use a solution to detect invalid data entries and perform data transformations that will not require programming or knowledge of SQL.
What should you do?

Answer: B


NEW QUESTION # 343
......

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