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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
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
          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)
                  Designing data processing systems22%- Batch and streaming data processing design
                  • 1. Event-driven vs batch architectures
                    • 2. Latency, throughput, and consistency trade-offs
                      - Data architecture and storage design
                      • 1. Designing scalable and cost-effective data models
                        • 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
                          Operationalizing machine learning models26%- Model deployment and monitoring
                          • 1. Online vs batch prediction
                            • 2. Model monitoring and drift detection
                              - ML pipeline integration
                              • 1. Feature engineering and feature stores
                                • 2. Vertex AI pipeline deployment

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q339-Q344):

                                  NEW QUESTION # 339
                                  You are architecting a data transformation solution for BigQuery. Your developers are proficient with SOL and want to use the ELT development technique. In addition, your developers need an intuitive coding environment and the ability to manage SQL as code. You need to identify a solution for your developers to build these pipelines. What should you do?

                                  Answer: A

                                  Explanation:
                                  To architect a data transformation solution for BigQuery that aligns with the ELT development technique and provides an intuitive coding environment for SQL-proficient developers, Dataform is an optimal choice. Here's why:
                                  ELT Development Technique:
                                  ELT (Extract, Load, Transform) is a process where data is first extracted and loaded into a data warehouse, and then transformed using SQL queries. This is different from ETL, where data is transformed before being loaded into the data warehouse.
                                  BigQuery supports ELT, allowing developers to write SQL transformations directly in the data warehouse.
                                  Dataform:
                                  Dataform is a development environment designed specifically for data transformations in BigQuery and other SQL-based warehouses.
                                  It provides tools for managing SQL as code, including version control and collaborative development.
                                  Dataform integrates well with existing development workflows and supports scheduling and managing SQL-based data pipelines.
                                  Intuitive Coding Environment:
                                  Dataform offers an intuitive and user-friendly interface for writing and managing SQL queries.
                                  It includes features like SQLX, a SQL dialect that extends standard SQL with features for modularity and reusability, which simplifies the development of complex transformation logic.
                                  Managing SQL as Code:
                                  Dataform supports version control systems like Git, enabling developers to manage their SQL transformations as code.
                                  This allows for better collaboration, code reviews, and version tracking.
                                  Reference:
                                  Dataform Documentation
                                  BigQuery Documentation
                                  Managing ELT Pipelines with Dataform


                                  NEW QUESTION # 340
                                  MJTelco's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations.
                                  You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?

                                  Answer: A


                                  NEW QUESTION # 341
                                  The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster ____.

                                  Answer: A

                                  Explanation:
                                  The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix-for example, if your cluster is named "my- cluster", the master-host-name would be "my-cluster-m".
                                  Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web- interfaces#interfaces


                                  NEW QUESTION # 342
                                  You need to create a new transaction table in Cloud Spanner that stores product sales data. You are deciding what to use as a primary key. From a performance perspective, which strategy should you choose?

                                  Answer: C


                                  NEW QUESTION # 343
                                  Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set.
                                  You want to increase the AUC of the model. What should you do?

                                  Answer: A


                                  NEW QUESTION # 344
                                  ......

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