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

Topic	Details
Topic 1	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 2	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 3	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 4	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 5	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 6	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 7	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 8	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 9	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 10	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 11	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry

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Databricks Certified Machine Learning Professional Sample Questions (Q180-Q185):

NEW QUESTION # 180
Which of the following describes batch deployment for machine learning projects?

A. None of these describe batch deployment for machine learning projects
B. Predictions are computed immediately as data arrives and stored for later querying
C. Predictions are computed and delivered as soon as feature values are available
D. Predictions are computed prior to delivery and stored for later querying

Answer: D

Explanation:
In batch deployment, predictions are precomputed at scheduled intervals and stored for later querying. This approach is ideal when real-time inference is not required and when predictions can be generated in bulk ahead of time.

NEW QUESTION # 181
Which stage in the MLflow Model Registry is typically used for models currently serving production traffic?

A. Staging
B. Production
C. None
D. Archived

Answer: B

Explanation:
In MLflow Model Registry stages:
Staging -> testing before release
Production -> serving real users
Archived -> retired models

NEW QUESTION # 182
Which of the following is an obstacle related to streaming machine learning applications?

A. All of these
B. None of these
C. Out-of-order data
D. End-to-end fault tolerance

Answer: A

Explanation:
Streaming machine learning applications face multiple challenges, including end-to-end fault tolerance (ensuring recovery from failures without data loss) and out-of-order data (handling events that arrive late or out of sequence). Both are common obstacles in building reliable real- time ML systems.

NEW QUESTION # 183
A Machine Learning Engineer is using joblibspark and MLflowCallback to perform a distributed hyperparameter tuning experiment via Optuna. Assuming they have a single objective they are optimizing for, what will be the default sampler implemented by Optuna?

A. TPESampler
B. NSGAIISampler
C. GridSampler
D. RandomSampler

Answer: A

Explanation:
For single-objective optimization, Optuna uses the Tree-structured Parzen Estimator (TPE) sampler by default. TPESampler is a Bayesian optimization method that efficiently explores the hyperparameter space by modeling promising parameter regions, making it well suited for scalable hyperparameter tuning with joblibspark and MLflowCallback.

NEW QUESTION # 184
Which of the following Databricks-managed MLflow capabilities is a centralized model store?

A. Feature Store
B. Experiments
C. Model Registry
D. Models
E. Model Serving

Answer: C

NEW QUESTION # 185
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