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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Building and operationalizing data processing systems24%- Data processing and transformation
  • 1. Using Dataproc, Dataflow, and BigQuery SQL
    • 2. ETL/ELT pipeline design
      - Data ingestion and integration
      • 1. Streaming ingestion (Pub/Sub, Dataflow)
        • 2. Batch ingestion pipelines (BigQuery, Cloud Storage)
          Operationalizing machine learning models26%- ML pipeline integration
          • 1. Feature engineering and feature stores
            • 2. Vertex AI pipeline deployment
              - Model deployment and monitoring
              • 1. Model monitoring and drift detection
                • 2. Online vs batch prediction
                  Designing data processing systems22%- Data architecture and storage design
                  • 1. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
                    • 2. Designing scalable and cost-effective data models
                      - Batch and streaming data processing design
                      • 1. Event-driven vs batch architectures
                        • 2. Latency, throughput, and consistency trade-offs
                          Ensuring solution quality28%- Reliability and performance
                          • 1. Fault tolerance and recovery strategies
                            • 2. Monitoring pipelines and workloads
                              - Security and governance
                              • 1. Data governance and compliance
                                • 2. IAM and access control in GCP

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q30-Q35):

                                  NEW QUESTION # 30
                                  Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage.
                                  You want to minimize the storage cost of the migration. What should you do?

                                  Answer: A

                                  Explanation:
                                  First rule of dataproc is to keep data in GCS.


                                  NEW QUESTION # 31
                                  Which of these are examples of a value in a sparse vector? (Select 2 answers.)

                                  Answer: A,B

                                  Explanation:
                                  Explanation
                                  Categorical features in linear models are typically translated into a sparse vector in which each possible value has a corresponding index or id. For example, if there are only three possible eye colors you can represent
                                  'eye_color' as a length 3 vector: 'brown' would become [1, 0, 0], 'blue' would become [0, 1, 0] and 'green' would become [0, 0, 1]. These vectors are called "sparse" because they may be very long, with many zeros, when the set of possible values is very large (such as all English words).
                                  [0, 0, 0, 1, 0, 0, 1] is not a sparse vector because it has two 1s in it. A sparse vector contains only a single 1.
                                  [0, 5, 0, 0, 0, 0] is not a sparse vector because it has a 5 in it. Sparse vectors only contain 0s and 1s.
                                  Reference: https://www.tensorflow.org/tutorials/linear#feature_columns_and_transformations


                                  NEW QUESTION # 32
                                  Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

                                  Answer: A,B,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 # 33
                                  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 data. Which two actions should you take? (Choose two.)

                                  Answer: D,E


                                  NEW QUESTION # 34
                                  By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

                                  Answer: D

                                  Explanation:
                                  Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections


                                  NEW QUESTION # 35
                                  ......

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