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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
          Operationalizing data and ML pipelines30%- Monitoring and troubleshooting
          • 1. Performance optimization and debugging
            • 2. Logging and observability
              - Pipeline automation and orchestration
              • 1. Scheduling and monitoring pipelines
                • 2. Cloud Composer workflows
                  Designing data processing systems20%- Storage and data modeling
                  • 1. Data lake architecture on Google Cloud Storage
                    • 2. Data warehouse design using BigQuery
                      - Data pipeline architecture design
                      • 1. Scalable data ingestion design
                        • 2. Batch vs streaming data processing selection
                          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

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                                  Google Data Engineer Sample Questions (Q39-Q44):

                                  NEW QUESTION # 39
                                  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 # 40
                                  Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

                                  Answer: A,C,D

                                  Explanation:
                                  Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.
                                  Reference: https://cloud.google.com/dataproc/docs/resources/faq#what_type_of_jobs_can_i_run


                                  NEW QUESTION # 41
                                  By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

                                  Answer: C

                                  Explanation:
                                  Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections Reference: https://cloud.google.com/dataflow/model/pcollection


                                  NEW QUESTION # 42
                                  Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

                                  Answer: D

                                  Explanation:
                                  Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning. So deep is a strictly defined, technical term that means more than one hidden layer.
                                  In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
                                  A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines.
                                  Feature engineering is difficult to perform on raw image data.
                                  K- means Clustering is an unsupervised learning method used to categorize unlabeled data. Reference: https://deeplearning4j.org/neuralnet-overview


                                  NEW QUESTION # 43
                                  You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

                                  Answer: E


                                  NEW QUESTION # 44
                                  ......

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