Databricks Machine Learning Associate exam dumps

Databricks Machine Learning Associate practice question 644 of 656

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

Databricks Machine Learning Associate Question 644

Select 3

You are tasked with building a machine learning model that continuously predicts customer churn based on streaming data from a customer activity feed. You decide to use Delta Live Tables (DLT) to perform streaming inference. Which of the following steps are required to correctly implement streaming inference using Delta Live Tables?

  1. A

    Define a streaming input table using the @dlt.view or @dlt.table decorator.

  2. B

    Use a pre-trained ML model to apply batch inference on the streaming data using a Python UDF.

  3. C

    Deploy the pre-trained ML model as an MLflow model and apply it to the streaming data using the APPLY TRANSFORM clause.

  4. D

    Set up the Delta Live Table pipeline to process data in trigger-based micro-batches.

  5. E

    Store the inference output into a Delta table for downstream applications.

Show answer and explanation

Correct answers: A, C, E

Explanation

To perform streaming inference with Delta Live Tables, you need to define a streaming input table, deploy a pre-trained ML model (e.g., via MLflow) to apply inference on the streaming data, and store the results into a Delta table for further use. These steps ensure seamless integration of machine learning inference into a real-time streaming pipeline.

  • A. Correct.

    Correct: Delta Live Tables requires streaming input tables to be defined using the @dlt.view or @dlt.table decorator to process incoming streaming data.

  • B. Incorrect.

    Incorrect: Streaming inference is not performed using batch inference; it requires real-time processing, often by integrating with MLflow-deployed models.

  • C. Correct.

    Correct: Deploying the pre-trained ML model with MLflow and using the APPLY TRANSFORM clause is the correct way to integrate ML models for inference in Delta Live Tables.

  • D. Incorrect.

    Incorrect: Delta Live Tables pipelines process data in continuous mode rather than trigger-based micro-batches for streaming use cases.

  • E. Correct.

    Correct: Storing the inference output into a Delta table ensures the results are available for downstream applications or further processing.

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