GCP-DE PDF Guide | GCP-DE Test Valid

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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Operationalizing data and ML pipelines30%- Monitoring and troubleshooting
  • 1. Performance optimization and debugging
    • 2. Logging and observability
      - Pipeline automation and orchestration
      • 1. Cloud Composer workflows
        • 2. Scheduling and monitoring pipelines
          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
                  Building and operationalizing data processing systems30%- Data ingestion and transformation
                  • 1. ETL/ELT workflows
                    • 2. Pub/Sub streaming ingestion
                      - Data pipeline implementation
                      • 1. Dataflow pipeline development
                        • 2. Dataproc and Spark-based processing
                          Designing data processing systems20%- Data pipeline architecture design
                          • 1. Scalable data ingestion design
                            • 2. Batch vs streaming data processing selection
                              - Storage and data modeling
                              • 1. Data warehouse design using BigQuery
                                • 2. Data lake architecture on Google Cloud Storage

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                                  Google Data Engineer Sample Questions (Q48-Q53):

                                  NEW QUESTION # 48
                                  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: C,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 # 49
                                  You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

                                  Answer: D


                                  NEW QUESTION # 50
                                  You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?

                                  Answer: A


                                  NEW QUESTION # 51
                                  You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name Passing Certification Exams Made Easy visit - https://www.2PassEasy.com from the data source and then write to an output PCollection.
                                  Tom,555 X street Tim,553 Y street Sam, 111 Z street
                                  Which operation is best suited for the above data processing requirement?

                                  Answer: C

                                  Explanation:
                                  In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each element in your PCollection.
                                  Reference: https://cloud.google.com/dataflow/model/par-do


                                  NEW QUESTION # 52
                                  You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

                                  Answer: B


                                  NEW QUESTION # 53
                                  ......

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