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

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

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Databricks Certified Machine Learning Professional Sample Questions (Q41-Q46):

NEW QUESTION # 41
A data scientist is building a model to predict which communication channel (Phone, SMS, Email, or Post) is most likely to be effective for a given customer. Which model type is suited to this task?

Answer: A

Explanation:
This task requires predicting one class from multiple mutually exclusive categories. A softmax classifier is designed for multiclass classification problems and outputs a probability distribution across all possible communication channels, allowing selection of the most likely effective channel.


NEW QUESTION # 42
A Machine Learning Engineer needs to build a time series model. In Databricks, they have created isolated environments in different workspaces for development, staging, and production.
To manage this model, they are planning on using a "deploy code" strategy. They are concerned that the model trained in development will not remain consistent across environments, due to differences in the dependencies installed. What can they do to ensure that the model is trained with the same packages?

Answer: A

Explanation:
Using the same Databricks Runtime across environments ensures a consistent base environment, and installing dependencies from a lock file guarantees identical package versions during training. This combination follows best practices for deploy-code workflows by making the training environment reproducible and preventing dependency drift between development, staging, and production.


NEW QUESTION # 43
Which MLflow function registers a model to the registry?

Answer: A

Explanation:
mlflow.register_model() promotes a model into the Model Registry.


NEW QUESTION # 44
Which MLflow feature helps reproduce training runs?

Answer: B

Explanation:
Tracking logs:
parameters
code version
metrics
artifacts
making runs reproducible.


NEW QUESTION # 45
Why are Delta tables often used to store machine learning features?

Answer: A

Explanation:
Delta Lake provides:
ACID transactions
time travel
schema enforcement
These are essential for reproducible ML pipelines.


NEW QUESTION # 46
......

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