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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

Topic Details
Topic 1
  • Identify the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 2
  • Test whether the updated model performs better on the more recent data
  • Identify when retraining and deploying an updated model is a probable solution to drift
Topic 3
  • Describe model serving deploys and endpoint for every stage
  • Identify scenarios in which feature drift and
  • or label drift are likely to occur
Topic 4
  • Identify JIT feature values as a need for real-time deployment
  • Describe how to list all webhooks and how to delete a webhook
Topic 5
  • Describe the advantages of using the pyfunc MLflow flavor
  • Manually log parameters, models, and evaluation metrics using MLflow
Topic 6
  • Identify a use case for HTTP webhooks and where the Webhook URL needs to come
  • Identify advantages of using Job clusters over all-purpose clusters
Topic 7
  • Create, overwrite, merge, and read Feature Store tables in machine learning workflows
  • View Delta table history and load a previous version of a Delta table
Topic 8
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments

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Databricks Certified Machine Learning Professional Sample Questions (Q83-Q88):

NEW QUESTION # 83
A Machine Learning Engineer has deployed a fraud detection model that processes 10,000 transactions per hour. The model was trained on data from Q1 2024, but it's now Q4 2024. The ML team notices three concerning trends: (1) the model's precision has dropped from 92% to
78% over the past month, (2) the average transaction amount in recent data has increased from
$150 to $220 and (3) the relationship between transaction frequency and fraud likelihood has weakened significantly due to new payment methods being introduced. The engineer needs to implement a monitoring solution that can detect the root cause of the performance degradation, identify why the precision dropped, and be able to do this at the scale needed. Which monitoring pipeline component will do this?

  • A. Model Health Monitoring Pipeline because it ensures the infrastructure can handle the 10,000 transactions per hour load.
  • B. Logging and Monitoring Pipeline because it captures all prediction requests and can identify patterns in the fraud predictions.
  • C. Drift Detection Pipeline because it can identify both the data drift (transaction amount changes) and concept drift (weakened relationship between features and target).
  • D. Model Performance Monitoring Pipeline because it directly tracks precision metrics and can alert when thresholds are breached.

Answer: C

Explanation:
A drift detection pipeline is designed to diagnose the root causes of model performance degradation at scale. It can detect data drift, such as changes in the distribution of transaction amounts, and concept drift, where the relationship between input features and the fraud label changes due to new payment methods. This directly explains why precision dropped and provides actionable insight beyond simply observing metric degradation.


NEW QUESTION # 84
A Data Scientist is building a product recommendation model that suggests additional items based on a customer's current shopping basket for an online merchant. The model needs to access the real-time basket contents at inference time to avoid recommending items already in the basket. The model will be deployed using Databricks Model Serving. The data scientist now wants to implement real-time feature engineering to incorporate the current basket contents into the model's prediction. Which approach will do this?

  • A. Create a custom pyfunc model that processes the basket contents as part of the model's predict() method.
  • B. Write basket data to a feature table and publish to the online store for low-latency access.
  • C. Store basket data in a Delta table and configure automatic feature lookup during model serving.
  • D. Implement feature preprocessing in a separate microservice that feeds the model serving endpoint.

Answer: A

Explanation:
Real-time basket contents are request-specific and only available at inference time, making them unsuitable for precomputed feature tables or online stores. Implementing a custom pyfunc model allows the predict() method to directly consume and process the basket contents included in the request payload, enabling real-time feature engineering while keeping the logic tightly coupled with model inference in Databricks Model Serving.


NEW QUESTION # 85
Why is Delta Lake time travel useful in ML pipelines?

  • A. Smaller datasets
  • B. Faster model inference
  • C. Reproducible training datasets
  • D. Model tuning

Answer: C

Explanation:
Time travel allows training on exact historical datasets.


NEW QUESTION # 86
A Machine Learning Engineer wants to monitor the quality and stability of their machine learning model's predictions over time. They have a Delta table, retail_inference_log, which records each model prediction along with input features, a timestamp, and (when available) the true label. They need to detect data drift and monitor model performance trends using Databricks Lakehouse Monitoring, ensuring that alerts are triggered if the distribution of predictions or input features changes significantly. Which approach will set up monitoring for this use case?

  • A. Create a monitor with the Inference profile on the retail_inference_log table, and specify a recent batch of production data as the baseline table for drift detection. Use this recent production data to compare against new data for drift and performance monitoring.
  • B. Create a monitor with the Inference profile on the retail_inference_log table, specifying the timestamp column and the columns for model inputs, predictions, and labels. Configure the monitor to compute drift and performance metrics over time windows.
  • C. Create a monitor with the Time Series profile on the retail_inference_log table, specifying the timestamp column and including model input, prediction columns and the true label column. This will track drift in features and predictions over time, and model performance could also be tracked using a custom metric.
  • D. Create a monitor with the Snapshot profile on the retail_inference_log table, so that metrics are calculated over the entire table each time the monitor runs and therefore is able to compare new values with previous ones to compute data drift.

Answer: B

Explanation:
The Inference profile is specifically designed for monitoring production inference logs. By configuring it on the inference table with the timestamp, input feature columns, prediction column, and label column, Databricks Lakehouse Monitoring can automatically compute prediction drift, input feature drift, and model performance metrics over rolling time windows, and trigger alerts when significant distribution changes or performance degradation are detected.


NEW QUESTION # 87
A machine learning engineer wants to programmatically create a new Databricks Job whose schedule depends on the result of some automated tests in a machine learning pipeline. Which Databricks tool can be used to programmatically create the Job?

  • A. MLflow Client
  • B. AutoML APIs
  • C. Jobs cannot be created programmatically
  • D. MLflow APIs
  • E. Databricks REST APIs

Answer: E


NEW QUESTION # 88
......

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