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 Databricks-Machine-Learning-Professional Dumps with Practice Exam Questions Answers

Questions: 60 questions

Last Update: Jun 19, 2024

Databricks-Machine-Learning-Professional Question Includes: Single Choice Questions: 60,

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

The Databricks Databricks-Machine-Learning-Professional Exam is a certification exam that 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.

2. What topics are covered in the Databricks Databricks-Machine-Learning-Professional Exam?

The Databricks Databricks-Machine-Learning-Professional Exam covers the following topics:

  • Experimentation (30%)
  • Model Lifecycle Management (30%)
  • Model Deployment (25%)
  • Solution and Data Monitoring (15%)

3. How many questions are there in the Databricks Databricks-Machine-Learning-Professional Exam?

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

4. What is the time limit for the Databricks Databricks-Machine-Learning-Professional Exam?

The Databricks Databricks-Machine-Learning-Professional Exam has a time limit of 120 minutes.

5. Are there any prerequisites for taking the Databricks Databricks-Machine-Learning-Professional Exam?

There are no formal prerequisites, but Databricks recommends having at least one year of hands-on experience with Databricks and familiarity with machine learning concepts.

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Absolutely! Our Databricks-Machine-Learning-Professional practice questions and explanations go beyond theory and focus on the real-world application of concepts. By actively engaging with our Databricks ML Data Scientist Study Guide materials, you'll gain valuable hands-on experience.

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Databricks-Machine-Learning-Professional Questions and Answers

Question # 1

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


Starting a testing job when a new model is registered


Updatingdata in a source table for a Databricks SQL dashboard when a model version transitions to the Production stage


Sending an email alert when an automated testing Job fails


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


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

Question # 2

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


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


The inference of all types of records in real-time


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


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


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

Question # 3

A machine learning engineer is using the following code block as part of a batch deployment pipeline:

Which of the following changes needs to be made so this code block will work when theinferencetable is a stream source?


Replace "inference" with the path to the location of the Delta table


Replace schema(schema) with option("maxFilesPerTriqqer", 1}


Replace with spark.readStream


Replace formatfdelta") with format("stream")


Replace predict with a stream-friendly prediction function

Question # 4

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?


Indent the child run blocks within the parent run block


Add the nested=True argument to the parent run


Remove the nested=True argument from the child runs


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


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

Question # 5

A data scientist has developed a modelmodeland computed the RMSE of the model on the test set. They have assigned this value to the variablermse. They now want to manually store the RMSE value with the MLflow run.

They write the following incomplete code block:

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










There is no way to store values like this.