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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q18-Q23):

NEW QUESTION # 18
You need to migrate a 2TB relational database to Google Cloud Platform. You do not have the resources to significantly refactor the application that uses this database and cost to operate is of primary concern.
Which service do you select for storing and serving your data?

Answer: B


NEW QUESTION # 19
Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

Answer: C

Explanation:
Explanation/Reference: https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using- tensorflow-30505541d877


NEW QUESTION # 20
When running a pipeline that has a BigQuery source, on your local machine, you continue to get permission denied errors. What could be the reason for that?

Answer: A

Explanation:
When reading from a Dataflow source or writing to a Dataflow sink using DirectPipelineRunner, the Cloud Platform account that you configured with the gcloud executable will need access to the corresponding source/sink Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/ DirectPipelineRunner


NEW QUESTION # 21
The Development and External teams nave the project viewer Identity and Access Management (1AM) role m a folder named Visualization. You want the Development Team to be able to read data from both Cloud Storage and BigQuery, but the External Team should only be able to read data from BigQuery. What should you do?

Answer: B


NEW QUESTION # 22
You are implementing workflow pipeline scheduling using open source-based tools and Google Kubernetes Engine (GKE). You want to use a Google managed service to simplify and automate the task. You also want to accommodate Shared VPC networking considerations. What should you do?

Answer: C

Explanation:
Shared VPC requires that you designate a host project to which networks and subnetworks belong and a service project, which is attached to the host project. When Cloud Composer participates in a Shared VPC, the Cloud Composer environment is in the service project.Reference:https://cloud.google.com/composer/docs
/how-to/managing/configuring-shared-vpc


NEW QUESTION # 23
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