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

Topic	Details
Topic 1	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 2	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 3	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
Topic 4	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 5	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 6	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 7	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors

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Databricks Certified Machine Learning Professional Sample Questions (Q21-Q26):

NEW QUESTION # 21
What does an Estimator do in Spark ML?

A. Evaluates model performance
B. Transforms a dataset
C. Trains a model using data
D. Stores features

Answer: C

Explanation:
An Estimator learns from data and produces a Transformer.

NEW QUESTION # 22
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. Logging and Monitoring Pipeline because it captures all prediction requests and can identify patterns in the fraud predictions.
B. Drift Detection Pipeline because it can identify both the data drift (transaction amount changes) and concept drift (weakened relationship between features and target).
C. Model Health Monitoring Pipeline because it ensures the infrastructure can handle the 10,000 transactions per hour load.
D. Model Performance Monitoring Pipeline because it directly tracks precision metrics and can alert when thresholds are breached.

Answer: B

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 # 23
Which of the following operations in Feature Store Client fs can be used to return a Spark DataFrame of a data set associated with a Feature Store table?

A. fs.get_table
B. fs.read_table
C. fs.write_table
D. fs.create_table
E. There is no way to accomplish this task with fs

Answer: D

NEW QUESTION # 24
What is the main purpose of the Databricks Feature Store?

A. Storing trained models
B. Centralizing reusable ML features
C. Storing raw data
D. Replacing Spark ML pipelines

Answer: B

Explanation:
Feature Store allows teams to:
share features
avoid training/serving skew
maintain feature lineage.

NEW QUESTION # 25
Which tool can assist in real-time deployments by packaging software with its own application, tools, and libraries?

A. Flask
B. Docker
C. Click
D. Structured Streaming

Answer: B

Explanation:
Docker is a containerization tool that packages software along with its dependencies, tools, and libraries into a single container. This ensures consistency across environments and is highly useful for real-time deployments, enabling scalable and portable model serving.

NEW QUESTION # 26
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