Databricks Certified Machine Learning Professional (Databricks-Machine-Learning-Professional) Free Practice Test

Question 1

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?

A. mlflow.models.schema.infer_schema

B. mlflow.models.Model.signature

C. mlflow.models.signature.infer_signature

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

E. mlflow.models.Model.get_input_schema

Correct Answer: C

Question 2

A Machine Learning Engineer needs to develop fraud detection models with Databricks. They need to ensure seamless collaboration between data engineers and data scientists while maintaining strict governance, version control, and traceability as models progress from development to production. So, they need to choose the Databricks feature that will enable centralized model lineage tracking, cross-workspace access control, and automated synchronization of model versions with their training data. Which Databricks feature will do this?

A. Lakehouse Monitoring for tracking data quality and model drift

B. Mosaic AI Model Serving for deploying models as REST APIs

C. Unity Catalog for governing models via a centralized registry with role-based access

D. MLflow Tracking for logging parameters and metrics during experimentation

Correct Answer: C

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Question 3

A machine learning engineer is converting a Hyperopt-based hyperparameter tuning process from manual MLflow logging to MLflow Autologging. They are trying to determine how to manage nested Hyperopt runs with MLflow Autologging.
Which of the following approaches will create a single parent run for the process and a child run for each unique combination of hyperparameter values when using Hyperopt and MLflow Autologging?

A. Ensuring that a built-in model flavor is used for the model logging

B. MLflow Autoloqqinq will automatically accomplish this task with Hyperopt

C. Starting a manual child run within the objective function

D. There is no way to accomplish nested runs with MLflow Autoloqqinq and Hyperopt

E. Startinq a manual parent run before calling fmin

Correct Answer: E

Question 4

A machine learning engineer has a machine learning pipeline where predictions are updated annually. The final prediction dataset contains millions of rows, and that dataset is irregularly accessed. Which solution should the machine learning engineer use to maintain cost efficiency?

A. Cloud-based low latency database

B. Cloud-based in-memory instance of a Spark DataFrame

C. On-premises low-latency database

D. Cloud-based object storage

Correct Answer: D

Explanation: Only visible for TestSimulate members. You can sign-up / login (it's free).

Question 5

A machine learning engineer has registered a sklearn model in the MLflow Model Registry using the sklearn model flavor with UI model_uri. Which operation can be used to load the model as an sklearn object for batch deployment?

A. mlflow.pyfunc.load_model(model_uri)

B. mlflow.spark.load_model(model_uri)

C. mlflow.pyfunc.read_model(model_uri)

D. mlflow.sklearn.read_model(model_uri)

E. mlflow.sklearn.load_model(model_uri)

Correct Answer: A

Question 6

Which of the following is an obstacle related to streaming machine learning applications?

A. None of these

B. All of these

C. Out-of-order data

D. End-to-end fault tolerance

Correct Answer: B

Explanation: Only visible for TestSimulate members. You can sign-up / login (it's free).

Question 7

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 change does the team need to make to the above code block to accomplish the task?

A. Replace update_registered_model with update_model_version

B. Replace client.update_registered_model with mlflow

C. There no changes necessary

D. Replace description with artifact

E. Add a Python model as an argument to update_registered_model

Correct Answer: C

Question 8

What is the main purpose of the Databricks Feature Store?

A. Centralizing reusable ML features

B. Replacing Spark ML pipelines

C. Storing trained models

D. Storing raw data

Correct Answer: A

Explanation: Only visible for TestSimulate members. You can sign-up / login (it's free).

Question 9

Which of the following operations in Feature Store Client fs can be used to return a Spark DataFrame of a data set associated with a Feature Store table?

A. fs.create_table

B. fs.get_table

C. There is no way to accomplish this task with fs

D. fs.read_table

E. fs.write_table

Correct Answer: A

Question 10

A machine learning engineer wants to view all of the active MLflow Model Registry Webhooks for a specific model.
They are using the following code block:

Which of the following changes does the machine learning engineer need to make to this code block so it will successfully accomplish the task?

A. Replace list with webhooks in the endpoint URL

B. Replace list with view in the endpoint URL

C. Replace POST with GET in the call to http request

D. Replace POST with PUT in the call to http request

E. There are no necessary changes

Correct Answer: C

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Databricks Certified Machine Learning Professional (Databricks-Machine-Learning-Professional) Free Practice Test