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Databricks-Machine-Learning-Professional Databricks Certified Machine Learning Professional Questions and Answers

Questions 4

A data scientist has written a function to track the runs of their random forest model. The data scientist is changing the number of trees in the forest across each run.

Which of the following MLflow operations is designed to log single values like the number of trees in a random forest?

Options:

A.

mlflow.log_artifact

B.

mlflow.log_model

C.

mlflow.log_metric

D.

mlflow.log_param

E.

There is no way to store values like this.

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Questions 5

A data scientist would like to enable MLflow Autologging for all machine learning libraries used in a notebook. They want to ensure that MLflow Autologging is used no matter what version of the Databricks Runtime for Machine Learning is used to run the notebook and no matter what workspace-wide configurations are selected in the Admin Console.

Which of the following lines of code can they use to accomplish this task?

Options:

A.

mlflow.sklearn.autolog()

B.

mlflow.spark.autolog()

C.

spark.conf.set(“autologging”, True)

D.

It is not possible to automatically log MLflow runs.

E.

mlflow.autolog()

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Questions 6

Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?

Options:

A.

Streaming

B.

Batch

C.

Edge/on-device

D.

None of these strategies will accomplish the task.

E.

Real-time

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Questions 7

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?

Options:

A.

Z-Ordering

B.

Bin-packing

C.

Write as a Parquet file

D.

Data skipping

E.

Tuning the file size

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Questions 8

A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They want to obtain the input schema and the output schema of the model so they can document what type of data is expected as input.

Which of the following MLflow operations can be used to perform this task?

Options:

A.

mlflow.models.schema.infer_schema

B.

mlflow.models.signature.infer_signature

C.

mlflow.models.Model.get_input_schema

D.

mlflow.models.Model.signature

E.

There is no way to obtain the input schema and the output schema of an unlogged model.

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Questions 9

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

Options:

A.

The model will have a unique identifier in the MLflow experiment

B.

The schema of input data can be validated when serving models

C.

The model can be deployed using real-time serving tools

D.

The model will be secured by the user that developed it

E.

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

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Questions 10

A machine learning engineer needs to select a deployment strategy for a new machine learning application. The feature values are not available until the time of delivery, and results are needed exceedingly fast for one record at a time.

Which of the following deployment strategies can be used to meet these requirements?

Options:

A.

Edge/on-device

B.

Streaming

C.

None of these strategies will meet the requirements.

D.

Batch

E.

Real-time

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Questions 11

Which of the following is a simple statistic to monitor for categorical feature drift?

Options:

A.

Mode

B.

None of these

C.

Mode, number of unique values, and percentage of missing values

D.

Percentage of missing values

E.

Number of unique values

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Questions 12

Which of the following describes concept drift?

Options:

A.

Concept drift is when there is a change in the distribution of an input variable

B.

Concept drift is when there is a change in the distribution of a target variable

C.

Concept drift is when there is a change in the relationship between input variables and target variables

D.

Concept drift is when there is a change in the distribution of the predicted target given by the model

E.

None of these describe Concept drift

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Questions 13

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 themodel_descriptionvariable.

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?

Options:

A.

Replace update_registered_model with update_model_version

B.

There no changes necessary

C.

Replace description with artifact

D.

Replace client.update_registered_model with mlflow

E.

Add a Python model as an argument to update_registered_model

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Questions 14

Which of the following is a probable response to identifying drift in a machine learning application?

Options:

A.

None of these responses

B.

Retraining and deploying a model on more recent data

C.

All of these responses

D.

Rebuilding the machine learning application with a new label variable

E.

Sunsetting the machine learning application

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Questions 15

A machine learning engineer wants to move their model versionmodel_versionfor the MLflow Model Registry modelmodelfrom the Staging stage to the Production stage using MLflow Clientclient.

Which of the following code blocks can they use to accomplish the task?

A)

B)

C)

D)

E)

Options:

A.

Option A

B.

Option B

C.

Option C

D.

Option D

E.

option E

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Questions 16

Which of the following MLflow operations can be used to delete a model from the MLflow Model Registry?

Options:

A.

client.transition_model_version_stage

B.

client.delete_model_version

C.

client.update_registered_model

D.

client.delete_model

E.

client.delete_registered_model

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Questions 17

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

Options:

A.

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

B.

The inference of all types of records in real-time

C.

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

D.

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

E.

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

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Questions 18

A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.

They are using the following code block:

The code block is not nesting the runs in MLflow as they expected.

Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?

Options:

A.

Indent the child run blocks within the parent run block

B.

Add the nested=True argument to the parent run

C.

Remove the nested=True argument from the child runs

D.

Provide the same name to the run name parameter for all three run blocks

E.

Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs

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Exam Name: Databricks Certified Machine Learning Professional
Last Update: Apr 21, 2024
Questions: 60
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