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Databricks-Machine-Learning-Professional Exam Dumps : Databricks Certified Machine Learning Professional

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Databricks Databricks-Machine-Learning-Professional Exam Dumps FAQs

Q. # 1: What is the Databricks-Machine-Learning-Professional Exam?

The Databricks-Machine-Learning-Professional Exam assesses an individual's ability to use Databricks Machine Learning to perform advanced machine learning tasks in production. It covers experimentation, model lifecycle management, model deployment, and data monitoring.

Q. # 2: Who should take the Databricks-Machine-Learning-Professional Exam?

The Databricks-Machine-Learning-Professional exam is ideal for data scientists, machine learning engineers, and data engineers with experience using Databricks Machine Learning for complex machine learning tasks in production environments.

Q. # 3: What topics are covered in the Databricks-Machine-Learning-Professional Exam?

The Databricks-Machine-Learning-Professional exam focuses on your ability to:

  • Manage the Machine Learning Lifecycle (experiment tracking, version control, model deployment)
  • Leverage Databricks Machine Learning functionalities like AutoML, Feature Store, and MLflow for advanced workflows.
  • Implement strategies for deploying machine learning models into production.
  • Build monitoring solutions to detect and address data drift.
  • Scale machine learning models effectively.

Q. # 4: How many questions are on the Databricks-Machine-Learning-Professional Exam?

The Databricks-Machine-Learning-Professional exam consists of 60 multiple-choice questions.

Q. # 5: What is the time limit for the Databricks-Machine-Learning-Professional Exam?

You have 120 minutes to complete the Databricks-Machine-Learning-Professional exam.

Q. # 6: What is the passing score for the Databricks-Machine-Learning-Professional Exam?

The passing score for the Databricks-Machine-Learning-Professional exam is 70%.

Q. # 7: What is the differebce between Databricks Databricks-Machine-Learning-Professional and Databricks-Machine-Learning-Associate Exams?

The Databricks-Machine-Learning-Professional and Databricks-Machine-Learning-Associate exams differ in terms of their scope, difficulty, and target audience:

  • Databricks-Machine-Learning-Professional Exam: The Databricks-Machine-Learning-Professional Exam focuses on advanced machine learning tasks, including experimentation, model lifecycle management, model deployment, and data monitoring. This exam is designed for data scientists and machine learning engineers with at least one year of hands-on experience in these areas.
  • Databricks-Machine-Learning-Associate Exam: The Databricks-Machine-Learning-Associate Exam is geared towards individuals with foundational knowledge of machine learning and some hands-on experience working with Databricks, around six months. This exam covers basic machine learning tasks, such as understanding and utilizing AutoML, Feature Store, and certain capabilities of MLflow.

Q. # 8: How can CertsTopics help me prepare for the Databricks-Machine-Learning-Professional Exam?

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Databricks Certified Machine Learning Professional Questions and Answers

Question 1

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:

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

Options:

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"

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Question 2

Which of the following tools can assist in real-time deployments by packaging software with its own application, tools, and libraries?

Options:

A.

Cloud-based compute

B.

None of these tools

C.

REST APIs

D.

Containers

E.

Autoscaling clusters

Question 3

A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on customer-level Spark DataFrame spark_df, but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.

Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?

Options:

A.

df = fs.get_missing_features(spark_df, model_uri)

fs.score_model(model_uri, df)

B.

fs.score_model(model_uri, spark_df)

C.

df = fs.get_missing_features(spark_df, model_uri)

fs.score_batch(model_uri, df)

df = fs.get_missing_features(spark_df)

D.

fs.score_batch(model_uri, df)

E.

fs.score_batch(model_uri, spark_df)