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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Ensuring solution quality28%- Security and governance
  • 1. Data governance and compliance
    • 2. IAM and access control in GCP
      - Reliability and performance
      • 1. Monitoring pipelines and workloads
        • 2. Fault tolerance and recovery strategies
          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. Latency, throughput, and consistency trade-offs
                • 2. Event-driven vs batch architectures
                  Operationalizing machine learning models26%- ML pipeline integration
                  • 1. Vertex AI pipeline deployment
                    • 2. Feature engineering and feature stores
                      - Model deployment and monitoring
                      • 1. Model monitoring and drift detection
                        • 2. Online vs batch prediction
                          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

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q422-Q427):

                                  NEW QUESTION # 422
                                  You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

                                  Answer: C


                                  NEW QUESTION # 423
                                  You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old.
                                  What should you do?

                                  Answer: B

                                  Explanation:
                                  Reference https://support.google.com/datastudio/answer/7020039?hl=en


                                  NEW QUESTION # 424
                                  You are working on a linear regression model on BigQuery ML to predict a customer's likelihood of purchasing your company's products. Your model uses a city name variable as a key predictive component in order to train and serve the model your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?

                                  Answer: D


                                  NEW QUESTION # 425
                                  Which SQL keyword can be used to reduce the number of columns processed by BigQuery?

                                  Answer: C

                                  Explanation:
                                  SELECT allows you to query specific columns rather than the whole table.
                                  LIMIT, BETWEEN, and WHERE clauses will not reduce the number of columns processed by BigQuery.


                                  NEW QUESTION # 426
                                  Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable.
                                  The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of data.
                                  They want to improve this performance while minimizing cost. What should they do?

                                  Answer: A


                                  NEW QUESTION # 427
                                  ......

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