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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Ensuring solution quality28%- Security and governance
  • 1. Data governance and compliance
    • 2. IAM and access control in GCP
      - Reliability and performance
      • 1. Monitoring pipelines and workloads
        • 2. Fault tolerance and recovery strategies
          Topic 2: 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
                  Topic 3: 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)
                          Topic 4: Operationalizing machine learning models26%- Model deployment and monitoring
                          • 1. Model monitoring and drift detection
                            • 2. Online vs batch prediction
                              - ML pipeline integration
                              • 1. Vertex AI pipeline deployment
                                • 2. Feature engineering and feature stores

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q192-Q197):

                                  NEW QUESTION # 192
                                  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 # 193
                                  By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

                                  Answer: B

                                  Explanation:
                                  Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections Reference:
                                  https://cloud.google.com/dataflow/model/pcollection


                                  NEW QUESTION # 194
                                  You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

                                  Answer: B

                                  Explanation:
                                  Import and Export to Bigquery from Cloud Storage is FREE. Also, when u store the csv files, Cloud Storage is cheaper than Bigquery. For processing Dataproc is cheaper than Dataflow.


                                  NEW QUESTION # 195
                                  You are configuring networking for a Dataflow job. The data pipeline uses custom container images with the libraries that are required for the transformation logic preinstalled. The data pipeline reads the data from Cloud Storage and writes the data to BigQuery. You need to ensure cost-effective and secure communication between the pipeline and Google APIs and services.
                                  What should you do?

                                  Answer: C

                                  Explanation:
                                  Private Google Access allows VMs without external IP addresses to communicate with Google APIs and services over internal routes. This reduces the cost and increases the security of the data pipeline. Custom container images can be stored in Container Registry, which supports Private Google Access. Dataflow supports Private Google Access for both batch and streaming jobs.


                                  NEW QUESTION # 196
                                  Your company built a TensorFlow neural-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: D

                                  Explanation:
                                  Bad performance of a model is either due to lack of relationship between dependent and independent variables used, or just overfit due to having used too many features and/or bad features.
                                  Dropout Methods can show which features are bad. E.g. if it is one feature causing bad performance, then the dropout method will show it, so you can remove it from the model and retrain it.


                                  NEW QUESTION # 197
                                  ......

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