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Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Topic 1: Ensuring solution quality and reliability | 17% | - Testing and validating data systems
- 1. Data quality validation
- 2. Performance and scalability testing
- Troubleshooting and optimization
- 1. Optimizing queries and workloads
- 2. Diagnosing performance issues
|
| Topic 2: Operationalizing machine learning models | 20% | - Deploying and maintaining ML models
- 1. Model serving and monitoring
- 2. Optimizing model performance and cost
- Preparing data for ML
- 1. Feature engineering and data preparation
- 2. Handling structured and unstructured data
|
| Topic 3: Designing data processing systems | 20% | - Designing for business requirements
- 1. Designing for reliability and fault tolerance
- 2. Selecting appropriate storage solutions
- 3. Designing for scalability and elasticity
- Designing for regulatory and security requirements
- 1. Implementing access control and data protection
- 2. Ensuring data privacy and compliance
|
| Topic 4: Building and operationalizing data processing systems | 25% | - Deploying and managing systems
- 1. Monitoring and logging data processes
- 2. Managing infrastructure and resources
- Building data pipelines
- 1. Transforming and cleaning data
- 2. Ingesting data from various sources
- 3. Orchestrating data workflows
|
| Topic 5: Maintaining and automating data workloads | 18% | - Resource optimization
- 1. Cost management and resource allocation
- 2. Choosing appropriate compute and storage options
- Automation and repeatability
- 1. Automating deployment and updates
- 2. Implementing CI/CD for data systems
|
>> Professional-Data-Engineer模擬試験 <<
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Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q286-Q291):
質問 # 286
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?
- A. Sessions
- B. OutputCriteria
- C. Windows
- D. Triggers
正解:D
解説:
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
質問 # 287
You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?
- A. Sink API
- B. PCollection
- C. Transform
- D. Pipeline
正解:C
解説:
In Google Cloud, the Dataflow SDK provides a transform component. It is responsible for the data processing operation. You can use conditional, for loops, and other complex programming structure to create a branching pipeline.
質問 # 288
Your organization has thousands of images stored in Cloud Storage. The organization's data analysts need to categorize these images upon analyzing the image content and store the results in a structured format for integration with other BigQuery datasets. You need a solution that allows the analysts to perform this task with the least amount of coding. What should you do?
- A. Use the Gemini 2.5 Pro model in Vertex AI to analyze the images and store the results in BigQuery.
- B. Use Vertex AI Vision API to analyze the images and store the results in BigQuery.
- C. Use the ML.GENERATE_TEXT function along with object tables to analyze the images and store the JSON output in a BigQuery table.
- D. Use the ML.UNDERSTAND_TEXT function along with object tables to analyze the images, and store the JSON output in a Cloud Storage bucket.
正解:B
解説:
Vertex AI Vision API provides a managed service to analyze image content with minimal coding and can output structured results that can be stored directly in BigQuery for integration with other datasets.
質問 # 289
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.)
- A. HBase
- B. HDFS with Hive
- C. MySQL
- D. Redis
- E. MongoDB
- F. Cassandra
正解:A、B、E
質問 # 290
You are deploying a MySQL database workload onto Cloud SQL. The database must be able to scale up to support several readers from various geographic regions. The database must be highly available and meet low RTO and RPO requirements, even in the event of a regional outage. You need to ensure that interruptions to the readers are minimal during a database failover. What should you do?
- A. Create a highly available Cloud SQL instance in region A. Create a highly available read replica in region B. Scale up read workloads by creating cascading read replicas in multiple regions.
Promote the read replica in region B when region A is down. - B. Create a highly available Cloud SQL instance in region A. Scale up read workloads by creating read replicas in multiple regions. Promote one of the read replicas when region A is down.
- C. Create a highly available Cloud SQL instance in region A. Scale up read workloads by creating read replicas in the same region. Failover to the standby Cloud SQL instance when the primary instance fails.
- D. Create a highly available Cloud SQL instance in region Create a highly available read replica in region B. Scale up read workloads by creating cascading read replicas in multiple regions.
Backup the Cloud SQL instances to a multi-regional Cloud Storage bucket. Restore the Cloud SQL backup to a new instance in another region when Region A is down.
正解:A
質問 # 291
......
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