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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Maintaining and automating data workloads (~15% of the exam)15%- 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. Workflow orchestration
  • 3. Continuous integration and continuous deployment (CI/CD)
- Monitoring data pipelines and data processes
  • 1. Managing quotas and resource usage
  • 2. Logging, monitoring, and troubleshooting
Topic 2: Designing data processing systems (~30% of the exam)30%- 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. Integrating with new data sources
  • 2. Data acquisition and import
  • 3. AI data enrichment
  • 4. Processing logic
  • 5. Batch processing
  • 6. Streaming (e.g., windowing, late arriving data)
- Selecting appropriate storage technologies
  • 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
  • 2. Mapping storage options to business requirements
Topic 3: Ingesting and processing the data (~20% of the exam)20%- Performing security considerations
  • 1. Auditing, privacy, and compliance
  • 2. Data encryption
  • 3. Identity and Access Management (IAM)
- Building and maintaining data structures and databases
  • 1. Planning for analytical and operational use cases
  • 2. Defining data lifecycle
- Deploying and operationalizing the pipelines
  • 1. CI/CD for data pipelines
  • 2. Job automation and orchestration (Cloud Composer, Workflows)
Topic 4: 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
Topic 5: Storing the data (~20% of the exam)20%- Planning for using a data warehouse
  • 1. Designing the data model
  • 2. Defining architecture to support data access patterns
  • 3. Deciding the degree of data normalization
  • 4. Mapping business requirements
- Using a data lake
  • 1. Monitoring the data lake
  • 2. Processing data
  • 3. Managing the lake (data discovery, access, cost controls)
- 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

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Google Certified Professional Data Engineer Exam Sample Questions (Q89-Q94):

NEW QUESTION # 89
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: B


NEW QUESTION # 90
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: C


NEW QUESTION # 91
You are administering a BigQuery dataset that uses a customer-managed encryption key (CMEK). You need to share the dataset with a partner organization that does not have access to your CMEK. What should you do?

Answer: A

Explanation:
If you want to share a BigQuery dataset that uses a customer-managed encryption key (CMEK) with a partner organization that does not have access to your CMEK, you cannot use an authorized view or provide them a copy of your CMEK, because these options would violate the security and privacy of your data. Instead, you can copy the tables you need to share to a dataset without CMEKs, and then create an Analytics Hub listing for this dataset. Analytics Hub is a service that allows you to securely share and discover data assets across your organization and with external partners. By creating an Analytics Hub listing, you can grant the partner organization access to the copied dataset without CMEKs, and also control the level of access and the duration of the sharing. References:
* Customer-managed Cloud KMS keys
* [Authorized views]
* [Analytics Hub overview]
* [Creating an Analytics Hub listing]


NEW QUESTION # 92
Which SQL keyword can be used to reduce the number of columns processed by BigQuery?

Answer: B

Explanation:
SELECT allows you to query specific columns rather than the whole table.
LIMIT, BETWEEN, and WHERE clauses will not reduce the number of columns processed by BigQuery.
Reference: https://cloud.google.com/bigquery/launch-
checklist#architecture_design_and_development_checklist


NEW QUESTION # 93
Which role must be assigned to a service account used by the virtual machines in a Dataproc cluster so they can execute jobs?

Answer: C

Explanation:
Service accounts used with Cloud Dataproc must have Dataproc/Dataproc Worker role (or have all the permissions granted by Dataproc Worker role).


NEW QUESTION # 94
......

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