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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Ensuring solution quality28%- Reliability and performance
  • 1. Monitoring pipelines and workloads
    • 2. Fault tolerance and recovery strategies
      - Security and governance
      • 1. Data governance and compliance
        • 2. IAM and access control in GCP
          Topic 2: Designing data processing systems22%- Batch and streaming data processing design
          • 1. Latency, throughput, and consistency trade-offs
            • 2. Event-driven vs batch architectures
              - Data architecture and storage design
              • 1. Designing scalable and cost-effective data models
                • 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
                  Topic 3: Operationalizing machine learning models26%- Model deployment and monitoring
                  • 1. Model monitoring and drift detection
                    • 2. Online vs batch prediction
                      - ML pipeline integration
                      • 1. Feature engineering and feature stores
                        • 2. Vertex AI pipeline deployment
                          Topic 4: Building and operationalizing data processing systems24%- Data ingestion and integration
                          • 1. Batch ingestion pipelines (BigQuery, Cloud Storage)
                            • 2. Streaming ingestion (Pub/Sub, Dataflow)
                              - Data processing and transformation
                              • 1. ETL/ELT pipeline design
                                • 2. Using Dataproc, Dataflow, and BigQuery SQL

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q231-Q236):

                                  NEW QUESTION # 231
                                  Which of the following statements is NOT true regarding Bigtable access roles?

                                  Answer: C

                                  Explanation:
                                  For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
                                  Read from, but not write to, any table within the project. Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances.
                                  Reference:
                                  https://cloud.google.com/bigtable/docs/access-control


                                  NEW QUESTION # 232
                                  Your company is streaming real-time sensor data from their factory floor into Bigtable and they have noticed extremely poor performance. How should the row key be redesigned to improve Bigtable performance on queries that populate real-time dashboards?

                                  Answer: D


                                  NEW QUESTION # 233
                                  Your business users need a way to clean and prepare data before using the data for analysis.
                                  Your business users are less technically savvy and prefer to work with graphical user interfaces to define their transformations. After the data has been transformed, the business users want to perform their analysis directly in a spreadsheet. You need to recommend a solution that they can use. What should you do?

                                  Answer: C


                                  NEW QUESTION # 234
                                  You are designing a data processing pipeline. The pipeline must be able to scale automatically as load increases. Messages must be processed at least once and must be ordered within windows of 1 hour.
                                  How should you design the solution?

                                  Answer: D


                                  NEW QUESTION # 235
                                  Which of these statements about BigQuery caching is true?

                                  Answer: B

                                  Explanation:
                                  When query results are retrieved from a cached results table, you are not charged for the query.
                                  BigQuery caches query results for 24 hours, not 48 hours.
                                  Query results are not cached if you specify a destination table.
                                  A query's results are always cached except under certain conditions, such as if you specify a destination table.


                                  NEW QUESTION # 236
                                  ......

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