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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
Exam Price:$200 USD
Related Certifications:Databricks Certified Machine Learning Associate
Passing Score:Not publicly disclosed (approximately 70%)
Real Exam Qty:59
Exam Format:Multi-select, Multiple choice
Available Languages:English
Certificate Validity Period:2 years
Exam Duration:120 minutes
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 the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 2
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift
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 less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments
Topic 6
  • 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 7
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines

Databricks Certified Machine Learning Professional Sample Questions (Q46-Q51):

NEW QUESTION # 46
Which MLflow component is used to log parameters, metrics, and artifacts during model training?

Answer: D

Explanation:
MLflow Tracking records experiment runs including:
parameters
metrics
artifacts (models, plots, etc.)


NEW QUESTION # 47
A machine learning engineer has created a webhook with the following code block:

Which of the following code blocks will trigger this webhook to run the associate job?

Answer: C


NEW QUESTION # 48
A Data Scientist is using Spark ML to train a model for detecting fraudulent transactions (1:
fraudulent, 0: non-fraudulent). To maximize the model's overall AUC (ROC) score, the Data Scientist wants to perform hyperparameter tuning on a Random Forest classifier. Due to budget constraints, the tuning process must be completed efficiently to avoid excessive compute costs.
Which approach will fulfill their needs?

Answer: B

Explanation:
TrainValidationSplit evaluates each hyperparameter combination using a single train/validation split, which is significantly more compute-efficient than k-fold cross-validation. Using a BinaryClassificationEvaluator aligns directly with optimizing AUC (ROC) for a binary fraud detection task, making this approach well suited for maximizing model performance under budget and compute constraints.


NEW QUESTION # 49
A machine learning engineer and data scientist are working together to convert a batch deployment to an always-on streaming deployment. The machine learning engineer has expressed that rigorous data tests must be put in place as a part of their conversion to account for potential changes in data formats.
Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?

Answer: A


NEW QUESTION # 50
Which evaluator is used for binary classification?

Answer: D

Explanation:
BinaryClassificationEvaluator computes metrics like:
AUC
areaUnderPR.


NEW QUESTION # 51
......

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