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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q154-Q159):

                                  NEW QUESTION # 154
                                  You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud. Your input data is in CSV format. You want to minimize the cost of querying aggregate values for multiple users who will query the data in Cloud Storage with multiple engines. Which storage service and schema design should you use?

                                  Answer: A


                                  NEW QUESTION # 155
                                  If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

                                  Answer: A

                                  Explanation:
                                  If you're running a performance test that depends upon Cloud Bigtable, be sure to follow these steps as you plan and execute your test:
                                  Use a production instance. A development instance will not give you an accurate sense of how a production instance performs under load.
                                  Use at least 300 GB of data. Cloud Bigtable performs best with 1 TB or more of data.
                                  However, 300 GB of data is enough to provide reasonable results in a performance test on a 3-node cluster. On larger clusters, use 100 GB of data per node.
                                  Before you test, run a heavy pre-test for several minutes. This step gives Cloud Bigtable a chance to balance data across your nodes based on the access patterns it observes.
                                  Run your test for at least 10 minutes. This step lets Cloud Bigtable further optimize your data, and it helps ensure that you will test reads from disk as well as cached reads from memory.
                                  Reference: https://cloud.google.com/bigtable/docs/performance


                                  NEW QUESTION # 156
                                  You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

                                  Answer: B

                                  Explanation:
                                  Explanation
                                  In Google Cloud, the Dataflow SDK provides a transform component. It is responsible for the data processing operation. You can use conditional, for loops, and other complex programming structure to create a branching pipeline.
                                  Reference: https://cloud.google.com/dataflow/model/programming-model


                                  NEW QUESTION # 157
                                  Your neural network model is taking days to train. You want to increase the training speed. What can you do?

                                  Answer: C

                                  Explanation:
                                  Explanation/Reference: https://towardsdatascience.com/how-to-increase-the-accuracy-of-a-neural-network-9f5d1c6f407d


                                  NEW QUESTION # 158
                                  A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions.
                                  You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL 'dataset.model', table user_features). How should you create the ML pipeline?

                                  Answer: A


                                  NEW QUESTION # 159
                                  ......

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