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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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
          Topic 2: 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. ETL/ELT pipeline design
                • 2. Using Dataproc, Dataflow, and BigQuery SQL
                  Topic 3: Designing data processing systems22%- Batch and streaming data processing design
                  • 1. Latency, throughput, and consistency trade-offs
                    • 2. Event-driven vs batch architectures
                      - Data architecture and storage design
                      • 1. Designing scalable and cost-effective data models
                        • 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
                          Topic 4: Operationalizing machine learning models26%- Model deployment and monitoring
                          • 1. Model monitoring and drift detection
                            • 2. Online vs batch prediction
                              - ML pipeline integration
                              • 1. Feature engineering and feature stores
                                • 2. Vertex AI pipeline deployment

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                                  Google Certified Professional Data Engineer Exam Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q212-Q217):

                                  212. Frage
                                  You have a requirement to insert minute-resolution data from 50,000 sensors into a BigQuery table. You expect significant growth in data volume and need the data to be available within 1 minute of ingestion for real- time analysis of aggregated trends. What should you do?

                                  Antwort: B

                                  Begründung:
                                  Explanation


                                  213. Frage
                                  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?

                                  Antwort: D


                                  214. Frage
                                  You are migrating your data warehouse to Google Cloud and decommissioning your on-premises data center. Because this is a priority for your company, you know that bandwidth will be made available for the initial data load to the cloud. The files being transferred are not large in number, but each file is 90 GB Additionally, you want your transactional systems to continually update the warehouse on Google Cloud in real time. What tools should you use to migrate the data and ensure that it continues to write to your warehouse?

                                  Antwort: B

                                  Begründung:
                                  Use Gsutil when there is enough bandwidth to meet your project deadline for less than 1 TB of data.
                                  Storage Transfer Service is for much larger volumes for migration. Moreover, Cloud Data Fusion and Dataproc are not ideal for real-time updates. BigQuery Data Transfer Service does not support all on-prem sources.
                                  Reference:
                                  https://cloud.google.com/architecture/migration-to-google-cloud-transferring-your-large-datasets#gsutil_for_smaller_transfers_of_on-premises_data


                                  215. Frage
                                  You are designing a data mesh on Google Cloud by using Dataplex to manage data in BigQuery and Cloud Storage. You want to simplify data asset permissions. You are creating a customer virtual lake with two user groups:
                                  * Data engineers, which require lull data lake access
                                  * Analytic users, which require access to curated data
                                  You need to assign access rights to these two groups. What should you do?

                                  Antwort: A

                                  Begründung:
                                  When designing a data mesh on Google Cloud using Dataplex to manage data in BigQuery and Cloud Storage, it is essential to simplify data asset permissions while ensuring that each user group has the appropriate access levels. Here's why option A is the best choice:
                                  * Data Engineer Group:
                                  * Data engineers require full access to the data lake to manage and operate data assets comprehensively. Granting the dataplex.dataOwner role to the data engineer group on the customer data lake ensures they have the necessary permissions to create, modify, and delete data assets within the lake.
                                  * Analytic User Group:
                                  * Analytic users need access to curated data but do not require full control over all data assets.
                                  Granting the dataplex.dataReader role to the analytic user group on the customer curated zone provides read-only access to the curated data, enabling them to analyze the data without the ability to modify or delete it.
                                  Steps to Implement:
                                  * Grant Data Engineer Permissions:
                                  * Assign the dataplex.dataOwner role to the data engineer group on the customer data lake to ensure full access and management capabilities.
                                  * Grant Analytic User Permissions:
                                  * Assign the dataplex.dataReader role to the analytic user group on the customer curated zone to provide read-only access to curated data.
                                  Reference Links:
                                  * Dataplex IAM Roles and Permissions
                                  * Managing Access in Dataplex


                                  216. Frage
                                  Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
                                  You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

                                  Antwort: B,D


                                  217. Frage
                                  ......

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