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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Building and operationalizing data processing systems24%- Data processing and transformation
  • 1. Using Dataproc, Dataflow, and BigQuery SQL
    • 2. ETL/ELT pipeline design
      - Data ingestion and integration
      • 1. Batch ingestion pipelines (BigQuery, Cloud Storage)
        • 2. Streaming ingestion (Pub/Sub, Dataflow)
          Topic 2: Ensuring solution quality28%- Reliability and performance
          • 1. Fault tolerance and recovery strategies
            • 2. Monitoring pipelines and workloads
              - Security and governance
              • 1. IAM and access control in GCP
                • 2. Data governance and compliance
                  Topic 3: Operationalizing machine learning models26%- ML pipeline integration
                  • 1. Feature engineering and feature stores
                    • 2. Vertex AI pipeline deployment
                      - Model deployment and monitoring
                      • 1. Online vs batch prediction
                        • 2. Model monitoring and drift detection
                          Topic 4: 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

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q229-Q234):

                                  NEW QUESTION # 229
                                  You have a network of 1000 sensors. The sensors generate time series data: one metric per sensor per second, along with a timestamp. You already have 1 TB of data, and expect the data to grow by 1 GB every day. You need to access this data in two ways. The first access pattern requires retrieving the metric from one specific sensor stored at a specific timestamp, with a median single-digit millisecond latency. The second access pattern requires running complex analytic queries on the data, including joins, once a day. How should you store this data?

                                  Answer: D


                                  NEW QUESTION # 230
                                  You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of- Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about
                                  100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required.
                                  You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)

                                  Answer: B,D,F


                                  NEW QUESTION # 231
                                  You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 3090 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

                                  Answer: B


                                  NEW QUESTION # 232
                                  Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage.
                                  You want to minimize the storage cost of the migration. What should you do?

                                  Answer: C


                                  NEW QUESTION # 233
                                  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: B


                                  NEW QUESTION # 234
                                  ......

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