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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:Databricks
Exam Name:Databricks Certified Machine Learning Professional Exam
Exam Number:Databricks-Machine-Learning-Professional
Real Exam Qty:59
Passing Score:Not publicly disclosed (approximately 70%)
Exam Price:$200 USD
Certificate Validity Period:2 years
Available Languages:English
Exam Format:Multi-select, Multiple choice
Exam Duration:120 minutes
Related Certifications:Databricks Certified Machine Learning Associate
Recommended Training:Machine Learning at Scale
Advanced Machine Learning Operations
Exam Registration:Databricks Certification Portal
Sample Questions:Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:Online proctored or in-person test center
Pre Condition:No mandatory prerequisites; recommended: 6+ months hands-on experience with Databricks ML, SparkML, MLflow, and Python
Official Syllabus URL:https://www.databricks.com/learn/certification/machine-learning-professional

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

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

Databricks Certified Machine Learning Professional Sample Questions (Q38-Q43):

NEW QUESTION # 38
How can you save a trained Spark ML PipelineModel?

Answer: A

Explanation:
Example:
pipelineModel.write().overwrite().save("/model")


NEW QUESTION # 39
Which evaluator is used for binary classification?

Answer: C

Explanation:
BinaryClassificationEvaluator computes metrics like:
AUC
areaUnderPR.


NEW QUESTION # 40
A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. The pipeline needs to automatically refresh its model each time it runs. The project is attached to the existing model model_name in the MLflow Model Registry.
They are using the following code block as part of their solution:

Which statement describes the impact of the registered_model_name=model_name parameter and argument given that model_name already exists in the MLflow Model Registry?

Answer: C

Explanation:
When using registered_model_name=model_name in mlflow.spark.log_model(), MLflow automatically registers the logged model under the specified model name. If that model name already exists in the MLflow Model Registry, MLflow creates a new version of that existing registered model rather than a new model entry. This enables automatic versioning and continuous model refresh with each pipeline run.


NEW QUESTION # 41
A data scientist has developed and logged a scikit-learn random forest model model, and then they ended their Spark session and terminated their cluster. After starting a new cluster, they want to review the feature_importances_ of the original model object.
Which of the following lines of code can be used to restore the model object so that feature_importances_ is available?

Answer: A


NEW QUESTION # 42
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.
Which of the following code blocks can they use to accomplish the task?

Answer: D


NEW QUESTION # 43
......

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