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Here are sample Databricks Certified Machine Learning Professional (Databricks-Machine-Learning-Professional) Exam questions from real exam. You can get more Databricks Machine Learning Professional (Databricks-Machine-Learning-Professional) Exam premium practice questions at TestInsights.

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Question 1

Which of the following is a benefit of logging a model signature with an MLflow model?

AThe model will have a unique identifier in the MLflow experiment

BThe schema of input data can be validated when serving models

CThe model can be deployed using real-time serving tools

DThe model will be secured by the user that developed it

EThe schema of input data will be converted to match the signature

Correct : E

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AThe model will have a unique identifier in the MLflow experiment

BThe schema of input data can be validated when serving models

CThe model can be deployed using real-time serving tools

DThe model will be secured by the user that developed it

EThe schema of input data will be converted to match the signature

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Question 2

Which of the following statements describes streaming with Spark as a model deployment strategy?

AThe inference of batch processed records as soon as a trigger is hit

BThe inference of all types of records in real-time

CThe inference of batch processed records as soon as a Spark job is run

DThe inference of incrementally processed records as soon as trigger is hit

EThe inference of incrementally processed records as soon as a Spark job is run

Correct : E

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AThe inference of batch processed records as soon as a trigger is hit

BThe inference of all types of records in real-time

CThe inference of batch processed records as soon as a Spark job is run

DThe inference of incrementally processed records as soon as trigger is hit

EThe inference of incrementally processed records as soon as a Spark job is run

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Question 3

A machine learning engineer has deployed a model recommender using MLflow Model Serving. They now want to query the version of that model that is in the Production stage of the MLflow Model Registry.

Which of the following model URIs can be used to query the described model version?

Ahttps://<databricks-instance>/model-serving/recommender/Production/invocations

BThe version number of the model version in Production is necessary to complete this task.

Chttps://<databricks-instance>/model/recommender/stage-production/invocations

Dhttps://<databricks-instance>/model-serving/recommender/stage-production/invocations

Ehttps://<databricks-instance>/model/recommender/Production/invocations

Correct : B

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Ahttps://<databricks-instance>/model-serving/recommender/Production/invocations

BThe version number of the model version in Production is necessary to complete this task.

Chttps://<databricks-instance>/model/recommender/stage-production/invocations

Dhttps://<databricks-instance>/model-serving/recommender/stage-production/invocations

Ehttps://<databricks-instance>/model/recommender/Production/invocations

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Question 4

Which of the following tools can assist in real-time deployments by packaging software with its own application, tools, and libraries?

ACloud-based compute

BNone of these tools

CREST APIs

DContainers

EAutoscaling clusters

Correct : A

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ACloud-based compute

BNone of these tools

CREST APIs

DContainers

EAutoscaling clusters

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Question 5

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?

AReplace update_registered_model with update_model_version

BThere no changes necessary

CReplace description with artifact

CReplace client.update_registered_model with mlflow

DAdd a Python model as an argument to update_registered_model

Correct : B

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AReplace update_registered_model with update_model_version

BThere no changes necessary

CReplace description with artifact

CReplace client.update_registered_model with mlflow

DAdd a Python model as an argument to update_registered_model

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Databricks-Machine-Learning-Professional Exam

Databricks-Machine-Learning-Professional Exam Question 1
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