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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

Topic	Details
Topic 1	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 2	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
Topic 3	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 4	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 5	Test whether the updated model performs better on the more recent data Identify when retraining and deploying an updated model is a probable solution to drift
Topic 6	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments

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Databricks Certified Machine Learning Professional Sample Questions (Q47-Q52):

NEW QUESTION # 47
A data scientist has written a function to track the runs of their random forest model. The data scientist is changing the number of trees in the forest across each run.
Which of the following MLflow operations is designed to log single values like the number of trees in a random forest?

A. mlflow.log_metric
B. There is no way to store values like this.
C. mlflow.log_param
D. mlflow.log_artifact
E. mlflow.log_model

Answer: A

NEW QUESTION # 48
A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable.
They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df. Which of the following code blocks can they use to create a function called predict that they can use to complete the task?

A. It is not possible to deploy a scikit-learn model on a Spark DataFrame.
B.
C.
D.
E.

Answer: E

NEW QUESTION # 49
A data scientist wants to log outlier feature data from a CSV file at path outlier_path with an MLflow run for model model. Which code block will accomplish this task inside of an existing MLflow run block?

A.
B. mlflow.log_artifact(outlier_path, "outlier-features.csv")
C. mlflow.log_data(outlier_path, "outlier-features.csv")
D.

Answer: B

Explanation:
To log external files, such as a CSV containing outlier feature data, MLflow provides the mlflow.log_artifact() function. This function uploads the specified file or directory (outlier_path) as an artifact under the provided artifact path ("outlier-features.csv"). It is the correct way to associate data files with an MLflow run, whereas mlflow.log_model() is reserved for logging model objects, not arbitrary data.

NEW QUESTION # 50
A Data Scientist at a company with rapidly increasing sales has deployed a scikit-learn model in production, which is retrained weekly on a single-node cluster. During the most recent retraining, the job failed due to an out-of-memory error. Upon investigation, the Data Scientist discovered that the training data had increased to 700GB as a result of the company's expanding customer base. Which approach will reliably resolve this issue in the long term?

A. Increase the size of the current single-node cluster to handle the increase of data while minimizing the need for code refactoring
B. Wrap the current model training code as a Spark UDF and apply it to the Spark DataFrame to distribute model training across multiple nodes.
C. Refactor the code to use the DeepSpeed library to shard the scikit-learn model across multiple nodes, enabling distributed model training on large volumes of data
D. Refactor the code to use MLlib across multiple nodes, enabling distributed model training on large volumes of data

Answer: D

Explanation:
Spark MLlib is designed for distributed model training on large-scale datasets and can natively handle hundreds of gigabytes of data across multiple nodes. Refactoring to MLlib enables the training workload to scale with data growth, avoids single-node memory limitations, and provides a reliable long-term solution as the company's data continues to expand.

NEW QUESTION # 51
A data scientist has created a Python function compute_features that returns a Spark DataFrame with the following schema:

The resulting DataFrame is assigned to the features_df variable. The data scientist wants to create a Feature Store table using features_df.
Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Client fs?

A.
B.
C.
D. features_df.write.mode("feature").path("new_table")
E. features_df.write.mode("fs").path("new_table")

Answer: B

NEW QUESTION # 52
......

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