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  • Exam Name: Databricks Certified Machine Learning Professional
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Databricks-Machine-Learning-Professional Questions and Answers

Question # 1

Which of the following describes the concept of MLflow Model flavors?

A.

A convention that deployment tools can use to wrap preprocessing logic into a Model

B.

A convention that MLflow Model Registry can use to version models

C.

A convention that MLflow Experiments can use to organize their Runs by project

D.

A convention that deployment tools can use to understand the model

E.

A convention that MLflow Model Registrycan use to organize its Models by project

Question # 2

Which of the following describes the purpose of the context parameter in the predict method of Python models for MLflow?

A.

The context parameter allows the user to specify which version of the registered MLflowModel should be used based on the given application's current scenario

B.

The context parameter allows the user to document the performance of a model after it has been deployed

C.

The context parameter allows the user to include relevant details of the business case to allow downstream users to understand the purpose of the model

D.

The context parameter allows the user to provide the model with completely custom if-else logic for the given application's current scenario

E.

The context parameter allows the user to provide the model access to objects like preprocessing models or custom configuration files

Question # 3

A machine learning engineer is attempting to create a webhook that will trigger a Databricks Jobjob_idwhen a model version for modelmodeltransitions into any MLflow Model Registry stage.

They have the following incomplete code block:

Databricks-Machine-Learning-Professional question answer

Which of the following lines of code can be used to fill in the blank so that the code block accomplishes the task?

A.

"MODEL_VERSION_CREATED"

B.

"MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"

C.

"MODEL_VERSION_TRANSITIONED_TO_STAGING"

D.

"MODEL_VERSION_TRANSITIONED_STAGE"

E.

"MODEL_VERSION_TRANSITIONED_TO_STAGING", "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"

Question # 4

A machine learning engineer is manually refreshing a model in an existing machine learning pipeline. The pipeline uses the MLflow Model Registry model "project". The machine learning engineer would like to add a new version of the model to "project".

Which of the following MLflow operations can the machine learning engineer use to accomplish this task?

A.

mlflow.register_model

B.

MlflowClient.update_registered_model

C.

mlflow.add_model_version

D.

MlflowClient.get_model_version

E.

The machine learning engineer needs to create an entirely new MLflow Model Registry model

Question # 5

A machine learning engineering team has written predictions computed in a batch job to a Delta table for querying. However, the team has noticed that the querying is running slowly. The team has alreadytuned the size of the data files. Upon investigating, the team has concluded that the rows meeting the query condition are sparsely located throughout each of the data files.

Based on the scenario, which of the following optimization techniques could speed up the query by colocating similar records while considering values in multiple columns?

A.

Z-Ordering

B.

Bin-packing

C.

Write as a Parquet file

D.

Data skipping

E.

Tuning the file size

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