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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q233-Q238):

                                  NEW QUESTION # 233
                                  You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

                                  Answer: A

                                  Explanation:
                                  Explanation
                                  Reference https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/flex


                                  NEW QUESTION # 234
                                  Your financial services company is moving to cloud technology and wants to store 50 TB of financial time- series data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data.
                                  Which product should they use to store the data?

                                  Answer: C

                                  Explanation:
                                  https://cloud.google.com/blog/products/databases/getting-started-with-time-series-trend-predictions-using- gcp


                                  NEW QUESTION # 235
                                  Your car factory is pushing machine measurements as messages into a Pub/Sub topic in your Google Cloud project. A Dataflow streaming job, that you wrote with the Apache Beam SDK, reads these messages, sends acknowledgment to Pub/Sub, applies some custom business logic in a DoFn instance, and writes the result to BigQuery. You want to ensure that if your business logic fails on a message, the message will be sent to a Pub/Sub topic that you want to monitor for alerting purposes. What should you do?

                                  Answer: D


                                  NEW QUESTION # 236
                                  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 # 237
                                  All Google Cloud Bigtable client requests go through a front-end server ______ they are sent to a Cloud Bigtable node.

                                  Answer: C

                                  Explanation:
                                  In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
                                  The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
                                  When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
                                  Reference:
                                  https://cloud.google.com/bigtable/docs/overview


                                  NEW QUESTION # 238
                                  ......

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