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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: Building and operationalizing data processing systems30%- Data pipeline implementation
          • 1. Dataflow pipeline development
            • 2. Dataproc and Spark-based processing
              - Data ingestion and transformation
              • 1. Pub/Sub streaming ingestion
                • 2. ETL/ELT workflows
                  Topic 3: 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
                          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 (Q31-Q36):

                                  NEW QUESTION # 31
                                  Which of these is not a supported method of putting data into a partitioned table?

                                  Answer: B

                                  Explanation:
                                  You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name. Reference: https://cloud.google.com/bigquery/docs/partitioned-tables


                                  NEW QUESTION # 32
                                  Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

                                  Answer: A

                                  Explanation:
                                  When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.
                                  Processing time triggers. These triggers operate on the processing time - the time when the data element is processed at any given stage in the pipeline. Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based.
                                  Reference: https://beam.apache.org/documentation/programming-guide/#triggers


                                  NEW QUESTION # 33
                                  You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

                                  Answer: I


                                  NEW QUESTION # 34
                                  What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

                                  Answer: B

                                  Explanation:
                                  A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
                                  Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys


                                  NEW QUESTION # 35
                                  You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

                                  Answer: D


                                  NEW QUESTION # 36
                                  ......

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