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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
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
          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. 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. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
                        • 2. Designing scalable and cost-effective data models
                          Ensuring solution quality28%- Security and governance
                          • 1. IAM and access control in GCP
                            • 2. Data governance and compliance
                              - Reliability and performance
                              • 1. Monitoring pipelines and workloads
                                • 2. Fault tolerance and recovery strategies

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                                  Official Professional-Data-Engineer Practice Test, Professional-Data-Engineer Relevant Answers

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q286-Q291):

                                  NEW QUESTION # 286
                                  You are using BigQuery with a regional dataset that includes a table with the daily sales volumes. This table is updated multiple times per day. You need to protect your sales table in case of regional failures with a recovery point objective (RPO) of less than 24 hours, while keeping costs to a minimum. What should you do?

                                  Answer: D

                                  Explanation:
                                  To apply complex business logic on a JSON response using Python's standard library within a Workflow, invoking a Cloud Function is the most efficient and straightforward approach. Here's why option A is the best choice:
                                  * Cloud Functions:
                                  * Cloud Functions provide a lightweight, serverless execution environment for running code in response to events. They support Python and can easily integrate with Workflows.
                                  * This approach ensures simplicity and speed of execution, as Cloud Functions can be invoked directly from a Workflow and handle the complex logic required.
                                  * Flexibility and Simplicity:
                                  * Using Cloud Functions allows you to leverage Python's extensive standard library and ecosystem, making it easier to implement and maintain the complex business logic.
                                  * Cloud Functions abstract the underlying infrastructure, allowing you to focus on the application logic without worrying about server management.
                                  * Performance:
                                  * Cloud Functions are optimized for fast execution and can handle the processing of the JSON response efficiently.
                                  * They are designed to scale automatically based on demand, ensuring that your workflow remains performant.
                                  Steps to Implement:
                                  * Write the Cloud Function:
                                  * Develop a Cloud Function in Python that processes the JSON response and applies the necessary business logic.
                                  * Deploy the function to Google Cloud.
                                  * Invoke Cloud Function from Workflow:
                                  * Modify your Workflow to call the Cloud Function using an HTTP request or Google Cloud Function connector.
                                  steps:
                                  - callCloudFunction:
                                  call: http.post
                                  args:
                                  url: https://REGION-PROJECT_ID.cloudfunctions.net/FUNCTION_NAME
                                  body:
                                  key: value
                                  * Process Results:
                                  * Handle the response from the Cloud Function and proceed with the next steps in the Workflow, such as loading data into BigQuery.
                                  Reference Links:
                                  * Google Cloud Functions Documentation
                                  * Using Workflows with Cloud Functions
                                  * Workflows Standard Library


                                  NEW QUESTION # 287
                                  You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:
                                  SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1:

                                  What is the most likely cause of the delay for this query?

                                  Answer: A


                                  NEW QUESTION # 288
                                  Your company uses Looker Studio connected to BigQuery for reporting. Users are experiencing slow dashboard load times due to complex queries on a large table. The queries involve aggregations and filtering on several columns. You need to optimize query performance to decrease the dashboard load times. What should you do?

                                  Answer: A,D

                                  Explanation:
                                  Pre-calculating commonly used aggregations and filters in a BigQuery materialized view significantly reduces the amount of computation required at query time, which directly improves dashboard responsiveness. Enabling BigQuery BI Engine further accelerates performance by keeping frequently accessed data in memory, optimizing interactive queries from Looker Studio and reducing latency for complex analytical workloads.


                                  NEW QUESTION # 289
                                  Which action can a Cloud Dataproc Viewer perform?

                                  Answer: D

                                  Explanation:
                                  A Cloud Dataproc Viewer is limited in its actions based on its role. A viewer can only list clusters, get cluster details, list jobs, get job details, list operations, and get operation details.
                                  Reference:
                                  https://cloud.google.com/dataproc/docs/concepts/iam#iam_roles_and_cloud_dataproc_ope rations_summary


                                  NEW QUESTION # 290
                                  You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old.
                                  What should you do?

                                  Answer: A


                                  NEW QUESTION # 291
                                  ......

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