Practice Exam

Explanations for each answer:

• For data processing, use a data pipeline is incorrect. Data pipelines are useful for orchestration and complex workflows, but they require more development effort compared to Dataflow Gen2 for simple filtering and transformation tasks.
• For data processing, use a Dataflow Gen2 dataflow is correct. Dataflow Gen2 provides a low-code interface for data transformation and filtering, making it ideal for batch processing scenarios with minimal development effort.
• For data processing, use an eventstream with a custom endpoint is incorrect. Custom endpoints in eventstreams are designed for routing data to external systems, not for batch processing and filtering operations.
• For data processing, use an eventstream with an external data source is incorrect. External data sources in eventstreams are used to connect to different data sources, not for processing and filtering data that's already being ingested.
• For filtering, use a filter activity in a data pipeline is incorrect. While data pipelines support filtering, they require more development effort and are better suited for complex orchestration rather than simple filter operations.
• For filtering, use a filter in a Dataflow Gen2 dataflow is correct. Dataflow Gen2 includes built-in filter transformations that can be easily configured with a low-code interface, minimizing development effort while effectively filtering rows based on attribute values.
• For filtering, use a KQL query is incorrect. KQL queries are used for querying data in KQL databases or Real-Time Analytics, not for filtering data in an eventstream ingestion pipeline before it reaches the lakehouse.
• For filtering, use an eventstream processor is incorrect. Eventstream processors are designed for real-time stream processing, not for batch ingestion scenarios where Dataflow Gen2 would be more appropriate.