Standard Professional-Data-Engineer Answers - Professional-Data-Engineer Valid Test Dumps

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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Building and operationalizing data processing systems24%- Data ingestion and integration
  • 1. Streaming ingestion (Pub/Sub, Dataflow)
    • 2. Batch ingestion pipelines (BigQuery, Cloud Storage)
      - Data processing and transformation
      • 1. ETL/ELT pipeline design
        • 2. Using Dataproc, Dataflow, and BigQuery SQL
          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: 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
                          Topic 4: Operationalizing machine learning models26%- Model deployment and monitoring
                          • 1. Online vs batch prediction
                            • 2. Model monitoring and drift detection
                              - ML pipeline integration
                              • 1. Vertex AI pipeline deployment
                                • 2. Feature engineering and feature stores

                                  >> Standard Professional-Data-Engineer Answers <<

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q184-Q189):

                                  NEW QUESTION # 184
                                  Your company uses separate Google Cloud projects for development, staging, and production.
                                  Developers need edit access in the development project, read-only access in the staging project, and no access in the production project. You need an effective and manageable solution to assign and enforce these permissions according to Google-recommended practices. What should you do?

                                  Answer: D

                                  Explanation:
                                  Using Google Groups to represent access levels and assigning IAM roles to those groups at the project level provides a scalable and maintainable access control model. This approach aligns with Google-recommended best practices by centralizing identity management, simplifying permission changes, and consistently enforcing least-privilege access across development, staging, and production environments.


                                  NEW QUESTION # 185
                                  You have a BigQuery table that contains customer data, including sensitive information such as names and addresses. You need to share the customer data with your data analytics and consumer support teams securely. The data analytics team needs to access the data of all the customers, but must not be able to access the sensitive dat a. The consumer support team needs access to all data columns, but must not be able to access customers that no longer have active contracts. You enforced these requirements by using an authorized dataset and policy tags After implementing these steps, the data analytics team reports that they still have access to the sensitive columns. You need to ensure that the data analytics team does not have access to restricted data What should you do?
                                  Choose 2 answers

                                  Answer: B,E

                                  Explanation:
                                  To ensure that the data analytics team does not have access to sensitive columns, you should:
                                  B . Ensure that the data analytics team members do not have the Data Catalog Fine-Grained Reader role for the policy tags. This role allows users to read metadata for data assets that have policy tags applied, which could include sensitive information.
                                  C . Enforce access control in the policy tag taxonomy. By setting access control at the policy tag level, you can restrict access to specific columns within a dataset, ensuring that only authorized users can view sensitive data.


                                  NEW QUESTION # 186
                                  You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings.
                                  Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight dat

                                  Answer: B


                                  NEW QUESTION # 187
                                  How would you query specific partitions in a BigQuery table?

                                  Answer: A

                                  Explanation:
                                  Partitioned tables include a pseudo column named _PARTITIONTIME that contains a date- based timestamp for data loaded into the table. To limit a query to particular partitions (such as Jan 1st and 2nd of 2017), use a clause similar to this:
                                  WHERE _PARTITIONTIME BETWEEN TIMESTAMP('2017-01-01') AND
                                  TIMESTAMP('2017-01-02')
                                  Reference: https://cloud.google.com/bigquery/docs/partitioned-
                                  tables#the_partitiontime_pseudo_column


                                  NEW QUESTION # 188
                                  When running a pipeline that has a BigQuery source, on your local machine, you continue to get permission denied errors. What could be the reason for that?

                                  Answer: B

                                  Explanation:
                                  When reading from a Dataflow source or writing to a Dataflow sink using DirectPipelineRunner, the Cloud Platform account that you configured with the gcloud executable will need access to the corresponding source/sink


                                  NEW QUESTION # 189
                                  ......

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