Databricks Machine Learning Professional exam dumps

Databricks Machine Learning Professional practice question 177 of 280

Databricks Certified Machine Learning Professional. Professional level, Databricks. Free question with the correct answer and a full explanation.

Databricks Machine Learning Professional Question 177

Select 2

A retail company is deploying a real-time recommendation system for its e-commerce platform using a Databricks streaming pipeline. The business logic involves filtering fraudulent transactions, aggregating real-time sales data, and dynamically updating recommendations. Why is it critical to handle this complex business logic directly within the streaming deployment?

  1. A

    Streaming deployments require business logic to ensure low-latency processing of incoming data.

  2. B

    Separating complex business logic into offline systems can lead to stale or inconsistent results in a streaming context.

  3. C

    Business logic in streaming deployments ensures model retraining pipelines are automatically triggered.

  4. D

    Real-time business logic enables seamless scalability of the streaming pipeline to handle spikes in data volume.

  5. E

    Handling business logic in streaming deployments ensures compliance with data governance policies.

Show answer and explanation

Correct answers: A, B

Explanation

Complex business logic must be handled within streaming deployments to maintain the real-time nature of the system. This ensures low-latency processing and prevents stale or inconsistent results that could arise from separating logic into offline systems. Streaming systems are designed to provide actionable insights and decisions in real-time, making it critical to embed the necessary business logic directly into the pipeline.

  • A. Correct.

    Correct: Low-latency processing is a key requirement of streaming systems, and embedding business logic ensures timely decisions and actions on the incoming data.

  • B. Correct.

    Correct: Separating business logic into offline systems can result in outdated or inconsistent results, which defeats the purpose of real-time streaming systems.

  • C. Incorrect.

    Incorrect: While business logic might influence model retraining indirectly, it is not a primary reason for embedding complex logic within streaming deployments.

  • D. Incorrect.

    Incorrect: Scalability is a function of the streaming architecture and infrastructure, not directly tied to business logic handling.

  • E. Incorrect.

    Incorrect: Data governance is important but not the primary reason for implementing business logic directly in streaming deployments.

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