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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Maintaining and optimizing data and ML solutions20%- Machine learning integration
  • 1. BigQuery ML usage
    • 2. Vertex AI integration for pipelines
      - Security and governance
      • 1. IAM and access control
        • 2. Data encryption and compliance
          Topic 2: Operationalizing data and ML pipelines30%- Pipeline automation and orchestration
          • 1. Scheduling and monitoring pipelines
            • 2. Cloud Composer workflows
              - Monitoring and troubleshooting
              • 1. Performance optimization and debugging
                • 2. Logging and observability
                  Topic 3: Building and operationalizing data processing systems30%- Data ingestion and transformation
                  • 1. ETL/ELT workflows
                    • 2. Pub/Sub streaming ingestion
                      - Data pipeline implementation
                      • 1. Dataflow pipeline development
                        • 2. Dataproc and Spark-based processing
                          Topic 4: Designing data processing systems20%- Data pipeline architecture design
                          • 1. Batch vs streaming data processing selection
                            • 2. Scalable data ingestion design
                              - Storage and data modeling
                              • 1. Data lake architecture on Google Cloud Storage
                                • 2. Data warehouse design using BigQuery

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                                  Google Data Engineer Sample Questions (Q34-Q39):

                                  NEW QUESTION # 34
                                  Cloud Dataproc is a managed Apache Hadoop and Apache service.

                                  Answer: B

                                  Explanation:
                                  Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
                                  Reference: https://cloud.google.com/dataproc/docs/


                                  NEW QUESTION # 35
                                  What are all of the BigQuery operations that Google charges for?

                                  Answer: C

                                  Explanation:
                                  Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
                                  Reference: https://cloud.google.com/bigquery/pricing


                                  NEW QUESTION # 36
                                  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: C

                                  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 # 37
                                  You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat a. Which two actions should you take? (Choose two.)

                                  Answer: A,E


                                  NEW QUESTION # 38
                                  Which is not a valid reason for poor Cloud Bigtable performance?

                                  Answer: C

                                  Explanation:
                                  The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.
                                  Reference: https://cloud.google.com/bigtable/docs/performance


                                  NEW QUESTION # 39
                                  ......

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