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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
Certificate Validity Period:2 years
Exam Duration:120 minutes
Available Languages:English
Real Exam Qty:59
Exam Format:Multi-select, Multiple choice
Exam Price:$200 USD
Related Certifications:Databricks Certified Machine Learning Associate
Passing Score:Not publicly disclosed (approximately 70%)
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 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 2
  • Describe model serving deploys and endpoint for every stage
  • Identify scenarios in which feature drift and
  • or label drift are likely to occur
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
  • 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 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
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments
Topic 8
  • Identify the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 9
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 10
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift

Databricks Certified Machine Learning Professional Sample Questions (Q111-Q116):

NEW QUESTION # 111
A data scientist wants to track the runs of their random forest model. The data scientist is changing the number of trees and the maximum depth of the trees in the forest across each run.
They write the following code block:

Which Python object type does params need to be an instance of?

Answer: A

Explanation:
The params variable must be a dictionary (dict) because mlflow.log_params() expects a dictionary where each key-value pair represents a parameter name and its corresponding value.
Additionally, the model instantiation RandomForestRegressor(**params) also requires params to be a dictionary to unpack the parameters correctly.


NEW QUESTION # 112
Which of the following statements about built-in library-specific MLflow Model flavors is true?

Answer: C

Explanation:
Built-in library-specific MLflow model flavors (e.g., mlflow.sklearn, mlflow.xgboost) allow models to be exported and later loaded as native library objects, enabling seamless reuse with the original libraries for inference or further training.


NEW QUESTION # 113
Which of the following is an advantage of using the python_function(pyfunc) model flavor over the built-in library-specific model flavors?

Answer: A


NEW QUESTION # 114
A data scientist wants to remove the star_rating column from the Delta table at the location path. To do this, they need to load in data and drop the star_rating column.
Which of the following code blocks accomplishes this task?

Answer: B


NEW QUESTION # 115
A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on customer-level Spark DataFrame spark_df, but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.
Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?

Answer: C


NEW QUESTION # 116
......

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