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

TopicDetails
Topic 1
  • Describe model serving deploys and endpoint for every stage
  • Identify scenarios in which feature drift and
  • or label drift are likely to occur
Topic 2
  • 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 3
  • Identify the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 4
  • 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 5
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
Topic 6
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments
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 concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 9
  • Identify that data can arrive out-of-order with structured streaming
  • Identify how model serving uses one all-purpose cluster for a model deployment

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Databricks Certified Machine Learning Professional Sample Questions (Q150-Q155):

NEW QUESTION # 150
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?

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 # 151
Which evaluator is used for binary classification?

Answer: A

Explanation:
BinaryClassificationEvaluator computes metrics like:
AUC
areaUnderPR.


NEW QUESTION # 152
A Data Scientist is preparing a Spark ML pipeline on a customer dataset with numeric features age, annual_income, and transaction_count, each varying widely. Because the chosen algorithm requires inputs normalized to the [0,1] range, they need to apply the appropriate Spark ML transformer to these features. Which Spark ML transformer should the Data Scientist use to scale all features to the [0,1] range?

Answer: A

Explanation:
MinMaxScaler rescales each numeric feature to a fixed range, typically [0, 1], by subtracting the minimum value and dividing by the feature's range. This makes it the appropriate Spark ML transformer when an algorithm explicitly requires inputs normalized to the [0,1] interval.


NEW QUESTION # 153
A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client. At the same time, they would like to archive any model versions that are already in the Production stage.
Which of the following code blocks can they use to accomplish the task?

Answer: A


NEW QUESTION # 154
A machine learning engineer has developed the following custom model class with preprocessing logic to combine two columns:

However, instances of this class are unable to compute predictions.
Which set of changes will update the class so predictions can be computed while continuing to apply the preprocessing logic?

Answer: D

Explanation:
The issue is that the predict() method does not apply the same preprocessing as the fit() method.
During training, the model uses preprocessed data (via self.preprocess_input()), but during prediction, it directly uses raw input. This mismatch causes prediction errors because the model expects preprocessed input.
By updating the predict() method to call self.preprocess_input(model_input.copy()), both training and prediction use consistent feature transformations, allowing predictions to be computed successfully.


NEW QUESTION # 155
......

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