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

Section	Weight	Objectives
ML Ops	44%	- Automated retraining workflows - Testing and validation strategies - Model monitoring and drift detection with Lakehouse Monitoring - Environment management with Databricks Asset Bundles
Model Deployment	12%	- Deployment strategies - Model rollout and version management - Custom model serving
Model Development	44%	- Advanced MLflow usage - Feature Store and automated feature pipelines - Distributed training and hyperparameter tuning - Scalable ML pipelines with SparkML

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Databricks Certified Machine Learning Professional Sample Questions (Q165-Q170):

NEW QUESTION # 165
Which statement describes streaming with Spark as a model deployment strategy?

A. The inference of all types of records in real-time
B. The inference of batch processed records as soon as a Spark job is run
C. The inference of incrementally processed records as soon as trigger is hit
D. The inference of batch processed records as soon as a trigger is hit
E. The inference of incrementally processed records as soon as a Spark job is run

Answer: E

NEW QUESTION # 166
A data scientist has computed updated feature values for all primary key values stored in the Feature Store table features. In addition, feature values for some new primary key values have also been computed. The updated feature values are stored in the DataFrame features_df. They want to replace all data in features with the newly computed data.
Which of the following code blocks can they use to perform this task using the Feature Store Client fs?

Answer: B

NEW QUESTION # 167
Which of the following describes concept drift?

A. Concept drift is when there is a change in the relationship between input variables and target variables
B. Concept drift is when there is a change in the distribution of the predicted target given by the model
C. Concept drift is when there is a change in the distribution of an input variable
D. None of these describe Concept drift
E. Concept drift is when there is a change in the distribution of a target variable

Answer: B

NEW QUESTION # 168
A machine learning engineering manager has asked all of the engineers on their team to add text descriptions to each of the model projects in the MLflow Model Registry. They are starting with the model project "model" and they'd like to add the text in the model_description variable.
The team is using the following line of code:

Which of the following changes does the team need to make to the above code block to accomplish the task?

A. There no changes necessary
B. Replace update_registered_model with update_model_version
C. Replace description with artifact
D. Add a Python model as an argument to update_registered_model
E. Replace client.update_registered_model with mlflow

Answer: A

NEW QUESTION # 169
A machine learning engineer needs to deliver predictions of a machine learning model in real-time. However, the feature values needed for computing the predictions are available one week before the query time.
Which of the following is a benefit of using a batch serving deployment in this scenario rather than a real-time serving deployment where predictions are computed at query time?

A. Testing is not possible in real-time serving deployments
B. Querying stored predictions can be faster than computing predictions in real-time
C. Batch serving has built-in capabilities in Databricks Machine Learning
D. There is no advantage to using batch serving deployments over real-time serving deployments
E. Computing predictions in real-time provides more up-to-date results

Answer: C

NEW QUESTION # 170
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