Free Databricks Certified-Machine-Learning-Professional Exam Questions
Absolute Free Certified-Machine-Learning-Professional Exam Practice for Comprehensive Preparation
-
Databricks Certified-Machine-Learning-Professional Exam Questions
-
Provided By: Databricks
- Exam: Certified Machine Learning Professional
- Certification: Databricks Certified Associate
-
Total Questions: 60
-
Updated On: Mar 26, 2025
-
Rated: 4.9 |
-
Online Users: 120
-
Question 1
-
Which of the following statements describes streaming with Spark as a model deployment strategy?
Answer: E
-
Which of the following statements describes streaming with Spark as a model deployment strategy?
-
Question 2
-
A machine learning engineer is in the process of implementing a concept drift monitoring solution. They are planning to use the following steps:
1. Deploy a model to production and compute predicted values
2. Obtain the observed (actual) label values
3. _____
4. Run a statistical test to determine if there are changes over time
Which of the following should be completed as Step #3?
Answer: D
-
A machine learning engineer is in the process of implementing a concept drift monitoring solution. They are planning to use the following steps:
-
Question 3
-
A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable. They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df.
Which of the following code blocks can they use to create a function called predict that they can use to complete the task?
Answer: D
-
A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable. They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df.
-
Question 4
-
Which of the following is a benefit of logging a model signature with an MLflow model?
Answer: E
-
Which of the following is a benefit of logging a model signature with an MLflow model?
-
Question 5
-
A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model.
Which of the following is a benefit of this approach when loading the logged pyfunc model for downstream deployment?
Answer: E
-
A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model.