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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q330-Q335):

                                  NEW QUESTION # 330
                                  The Dataflow SDKs have been recently transitioned into which Apache service?

                                  Answer: B

                                  Explanation:
                                  Dataflow SDKs are being transitioned to Apache Beam, as per the latest Google directive
                                  https://cloud.google.com/dataflow/docs/


                                  NEW QUESTION # 331
                                  You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:
                                  SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1:

                                  What is the most likely cause of the delay for this query?

                                  Answer: D


                                  NEW QUESTION # 332
                                  You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now
                                  automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD.You want to
                                  query all of the tables for the past 30 days in legacy SQL. What should you do?

                                  Answer: C

                                  Explanation:
                                  Explanation/Reference:
                                  Reference: https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-
                                  understand-your-mobile-app?hl=am


                                  NEW QUESTION # 333
                                  For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

                                  Answer: B

                                  Explanation:
                                  It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
                                  It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
                                  Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance


                                  NEW QUESTION # 334
                                  How would you query specific partitions in a BigQuery table?

                                  Answer: B

                                  Explanation:
                                  Explanation
                                  Partitioned tables include a pseudo column named _PARTITIONTIME that contains a date-based timestamp for data loaded into the table. To limit a query to particular partitions (such as Jan 1st and 2nd of 2017), use a clause similar to this:
                                  WHERE _PARTITIONTIME BETWEEN TIMESTAMP('2017-01-01') AND TIMESTAMP('2017-01-02') Reference: https://cloud.google.com/bigquery/docs/partitioned-tables#the_partitiontime_pseudo_column


                                  NEW QUESTION # 335
                                  ......

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