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Google GCP-DE Exam Syllabus Topics:

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

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                                  Google Data Engineer Sample Questions (Q11-Q16):

                                  NEW QUESTION # 11
                                  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 # 12
                                  What are two methods that can be used to denormalize tables in BigQuery?

                                  Answer: A

                                  Explanation:
                                  The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information. The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
                                  Reference: https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data


                                  NEW QUESTION # 13
                                  You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

                                  Answer: A,E


                                  NEW QUESTION # 14
                                  You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
                                  Which Google database service should you use?

                                  Answer: C


                                  NEW QUESTION # 15
                                  You need to choose a database for a new project that has the following requirements:
                                  Fully managed
                                  Able to automatically scale up
                                  Transactionally consistent
                                  Able to scale up to 6 TB
                                  Able to be queried using SQL Which database do you choose?

                                  Answer: A


                                  NEW QUESTION # 16
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