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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Building and operationalizing data processing systems30%- Data ingestion and transformation
  • 1. Pub/Sub streaming ingestion
    • 2. ETL/ELT workflows
      - Data pipeline implementation
      • 1. Dataflow pipeline development
        • 2. Dataproc and Spark-based processing
          Topic 2: 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 3: 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 4: 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

                                  Google Data Engineer Sample Questions:

                                  Question #1

                                  You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

                                  • A. If that number falls below 4000, send an alert.
                                  • B. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
                                  • C. Consume the stream of data in Cloud Dataflow using Kafka I
                                  • D. If that number falls below 4000, send an alert.
                                  • E. Consume the stream of data in Cloud Dataflow using Kafka I
                                  • F. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
                                  • G. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
                                  • H. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
                                  • I. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
                                  • J. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
                                  • K. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
                                  • L. Set a sliding time window of 1 hour every 5 minute
                                  • M. Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
                                  Reveal Solution  Discussion  0

                                  Correct Answer: J  🗳️

                                  Question #2

                                  You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

                                  • A. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
                                  • B. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
                                  • C. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
                                  • D. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
                                  • E. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
                                  Reveal Solution  Discussion  0

                                  Correct Answer: E  🗳️

                                  Question #3

                                  An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
                                  They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

                                  • A. Cloud BigTable
                                  • B. Cloud Datastore
                                  • C. BigQuery
                                  • D. Cloud SQL
                                  Reveal Solution  Discussion  0

                                  Correct Answer: A  🗳️

                                  Question #4

                                  You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

                                  • A. Cloud Composer
                                  • B. Cloud Scheduler
                                  • C. Workflow Templates on Cloud Dataproc
                                  • D. cron
                                  Reveal Solution  Discussion  0

                                  Correct Answer: C  🗳️

                                  Question #5

                                  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?

                                  • A. Cloud Dataflow
                                  • B. Cloud Functions
                                  • C. Cloud Scheduler
                                  • D. Cloud Composer
                                  Reveal Solution  Discussion  0

                                  Correct Answer: C  🗳️

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