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Databricks Certified Machine Learning Professional Exam Questions

Are you ready to prepare for the Databricks Certified Machine Learning Professional Exam? PassQuestion is here to provide you with the most up-to-date and comprehensive Databricks Certified Machine Learning Professional Exam Questions that are designed to cover all the key topics and concepts that you need to master to obtain your certification with ease. With our valuable Databricks Certified Machine Learning Professional Exam Questions, you can confidently prepare yourself and increase your chances of achieving success in the Databricks Certified Machine Learning Professional Exam.

Databricks Certified Machine Learning Professional

The Databricks Certified Machine Learning Professional certification exam assesses an individual's ability to use Databricks Machine Learning and its capabilities to perform advanced machine learning in production tasks. This includes the ability to track, version, and manage machine learning experiments and manage the machine learning model lifecycle. In addition, the certification exam assesses the ability to implement strategies for deploying machine learning models. Finally, test-takers will also be assessed on their ability to build monitoring solutions to detect data drift. Individuals who pass this certification exam can be expected to perform advanced machine learning engineering tasks using Databricks Machine Learning.


Exam Information

Type: Proctored certification

Number of items: 60 multiple-choice questions

Time limit: 120 minutes

Registration fee: $200

Languages: English

Delivery method: Online proctored

Prerequisites: None, but related training highly recommended

Recommended experience: 1+ years of hands-on experience performing the machine learning tasks outlined in the exam guide  

Validity period: 2 years

Recertification: Recertification is required to maintain your certification status. Databricks Certifications are valid for two years from issue date.

Exam Sections and Objectives

Section 1: Experimentation - 30%

Section 2: Model Lifecycle Management - 30%

Section 3: Model Deployment - 25%

Section 4: Solution and Data Monitoring - 15%

View Online Databricks Certified Machine Learning Professional Free Questions

1. Which of the following Databricks-managed MLflow capabilities is a centralized model store?

A.Models

B.Model Registry

C.Model Serving

D.Feature Store

E.Experiments

Answer: C

2. A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model.

Which of the following is a benefit of this approach when loading the logged pyfunc model for downstream deployment?

A.The pvfunc model can be used to deploy models in a parallelizable fashion

B.The same preprocessing logic will automatically be applied when calling fit

C.The same preprocessing logic will automatically be applied when calling predict

D.This approach has no impact when loading the logged Pvfunc model for downstream deployment

E.There is no longer a need for pipeline-like machine learning objects

Answer: E

3. Which of the following MLflow Model Registry use cases requires the use of an HTTP Webhook?

A.Starting a testing job when a new model is registered

B.Updating data in a source table for a Databricks SQL dashboard when a model version transitions to the Production stage

C.Sending an email alert when an automated testing Job fails

D.None of these use cases require the use of an HTTP Webhook

E.Sending a message to a Slack channel when a model version transitions stages

Answer: B

4. Which of the following lists all of the model stages are available in the MLflow Model Registry?

A.Development. Staging. Production

B.None. Staging. Production

C.Staging. Production. Archived

D.None. Staging. Production. Archived

E.Development. Staging. Production. Archived

Answer: A

5. 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.Batch serving has built-in capabilities in Databricks Machine Learning

B.There is no advantage to using batch serving deployments over real-time serving deployments

C.Computing predictions in real-time provides more up-to-date results

D.Testing is not possible in real-time serving deployments

E.Querying stored predictions can be faster than computing predictions in real-time

Answer: A

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