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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Designing data processing systems20%- Storage and data modeling
  • 1. Data warehouse design using BigQuery
    • 2. Data lake architecture on Google Cloud Storage
      - Data pipeline architecture design
      • 1. Scalable data ingestion design
        • 2. Batch vs streaming data processing selection
          Topic 2: 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
                  Topic 3: Building and operationalizing data processing systems30%- Data pipeline implementation
                  • 1. Dataproc and Spark-based processing
                    • 2. Dataflow pipeline development
                      - Data ingestion and transformation
                      • 1. ETL/ELT workflows
                        • 2. Pub/Sub streaming ingestion
                          Topic 4: 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

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                                  Google Data Engineer Sample Questions (Q58-Q63):

                                  NEW QUESTION # 58
                                  You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

                                  Answer: B


                                  NEW QUESTION # 59
                                  Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

                                  Answer: A,D

                                  Explanation:
                                  using a timestamp as the first element of a row key can cause a variety of problems.
                                  In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting.
                                  Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes. [https://cloud.google.com/bigtable/docs/schema-design] Reference:
                                  https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti


                                  NEW QUESTION # 60
                                  You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

                                  Answer: C


                                  NEW QUESTION # 61
                                  You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

                                  Answer: C

                                  Explanation:
                                  Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform


                                  NEW QUESTION # 62
                                  MJTelco is building a custom interface to share dat
                                  a. They have these requirements:
                                  They need to do aggregations over their petabyte-scale datasets.
                                  They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?

                                  Answer: B


                                  NEW QUESTION # 63
                                  ......

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