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Google GCP-DE Exam Syllabus Topics:

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

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                                  Google Data Engineer Sample Questions (Q56-Q61):

                                  NEW QUESTION # 56
                                  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 # 57
                                  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: D

                                  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 # 58
                                  All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.

                                  Answer: C

                                  Explanation:
                                  In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
                                  The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
                                  When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
                                  Reference: https://cloud.google.com/bigtable/docs/overview


                                  NEW QUESTION # 59
                                  Which of these sources can you not load data into BigQuery from?

                                  Answer: B

                                  Explanation:
                                  You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.
                                  Reference: https://cloud.google.com/bigquery/loading-data


                                  NEW QUESTION # 60
                                  When a Cloud Bigtable node fails, is lost.

                                  Answer: A

                                  Explanation:
                                  A Cloud Bigtable table is sharded into blocks of contiguous rows, called tablets, to help balance the workload of queries. Tablets are stored on Colossus, Google's file system, in SSTable format. Each tablet is associated with a specific Cloud Bigtable node.
                                  Data is never stored in Cloud Bigtable nodes themselves; each node has pointers to a set of tablets that are stored on Colossus. As a result:
                                  Rebalancing tablets from one node to another is very fast, because the actual data is not copied. Cloud Bigtable simply updates the pointers for each node.
                                  Recovery from the failure of a Cloud Bigtable node is very fast, because only metadata needs to be migrated to the replacement node.
                                  When a Cloud Bigtable node fails, no data is lost Reference: https://cloud.google.com/bigtable/docs/overview


                                  NEW QUESTION # 61
                                  ......

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