Most Recent Databricks-Machine-Learning-Professional Exam Questions & Answers

Prepare for the Databricks Certified Machine Learning Professional exam with our extensive collection of questions and answers. These practice Q&A are updated according to the latest syllabus, providing you with the tools needed to review and test your knowledge.

QA4Exam focus on the latest syllabus and exam objectives, our practice Q&A are designed to help you identify key topics and solidify your understanding. By focusing on the core curriculum, These Questions & Answers helps you cover all the essential topics, ensuring you're well-prepared for every section of the exam. Each question comes with a detailed explanation, offering valuable insights and helping you to learn from your mistakes. Whether you're looking to assess your progress or dive deeper into complex topics, our updated Q&A will provide the support you need to confidently approach the Databricks-Machine-Learning-Professional exam and achieve success.

The questions for Databricks-Machine-Learning-Professional were last updated on Nov 17, 2024.

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Question No. 1

A data scientist has developed and logged a scikit-learn random forest model model, and then they ended their Spark session and terminated their cluster. After starting a new cluster, they want to review the feature_importances_ of the original model object.

Which of the following lines of code can be used to restore the model object so that feature_importances_ is available?

Amlflow.load_model(model_uri)

Bclient.list_artifacts(run_id)['feature-importances.csv']

Cmlflow.sklearn.load_model(model_uri)

DThis can only be viewed in the MLflow Experiments UI

Eclient.pyfunc.load_model(model_uri)

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Correct Answer: A

Question No. 2

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?

AIndent the child run blocks within the parent run block

BAdd the nested=True argument to the parent run

CRemove the nested=True argument from the child runs

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

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

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Correct Answer: E

Question No. 3

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

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Correct Answer: E

Question No. 4

Which of the following is an advantage of using the python_function(pyfunc) model flavor over the built-in library-specific model flavors?

Apython_function provides no benefits over the built-in library-specific model flavors

Bpython_function can be used to deploy models in a parallelizable fashion

Cpython_function can be used to deploy models without worrying about which library was used to create the model

Dpython_function can be used to store models in an MLmodel file

Epython_function can be used to deploy models without worrying about whether they are deployed in batch, streaming, or real-time environments

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Correct Answer: B

Question No. 5

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

AModels

BModel Registry

CModel Serving

DFeature Store

EExperiments

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Correct Answer: C

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