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

Topic	Details
Topic 1	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 2	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 3	Create, overwrite, merge, and read Feature Store tables in machine learning workflows View Delta table history and load a previous version of a Delta table
Topic 4	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 5	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 6	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 7	Identify a use case for HTTP webhooks and where the Webhook URL needs to come Identify advantages of using Job clusters over all-purpose clusters

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Databricks Certified Machine Learning Professional Sample Questions (Q63-Q68):

NEW QUESTION # 63
A machine learning engineer is in the process of implementing a concept drift monitoring solution.
They are planning to use the following steps:
1. Deploy a model to production and compute predicted values
2. Obtain the observed (actual) label values
3. _____
4. Run a statistical test to determine if there are changes over time
Which of the following should be completed as Step #3?

A. Measure the latency of the prediction time
B. Retrain the model
C. None of these should be completed as Step #3
D. Obtain the observed values (actual) feature values
E. Compute the evaluation metric using the observed and predicted values

Answer: C

NEW QUESTION # 64
A machine learning engineer is monitoring categorical input variables for a production machine learning application. The engineer believes that missing values are becoming more prevalent in more recent data for a particular value in one of the categorical input variables.
Which of the following tools can the machine learning engineer use to assess their theory?

A. One-way Chi-squared Test
B. Jenson-Shannon distance
C. None of these
D. Kolmogorov-Smirnov (KS) test
E. Two-way Chi-squared Test

Answer: A

NEW QUESTION # 65
A machine learning engineer needs a python_model to access a collection of files using its load_context operation. The collection of files being accessed by python_model.load_context needs to be saved when the model is being logged. A dictionary of the names and paths of these files is properly stored in my_dict.
The machine learning engineer has written the following incomplete block of code:

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

A. context
B. None of these lines of code can be used to successfully complete the code block
C. model_context
D. artifacts

Answer: D

Explanation:
When logging a custom Python model with mlflow.pyfunc.log_model(), the artifacts parameter is used to specify a dictionary (my_dict) of file names and their paths that the model may need to access during loading or inference. These files are stored as model artifacts and become available to the model through the load_context method.

NEW QUESTION # 66
A Machine Learning Engineer developed a dynamic pricing model in MLflow that requires values from the company's cloud database to generate predictions. At inference time the PyFunc model uses the cloud provider's Python SDK to retrieve the latest values from the database. The inference code works well in a notebook, but when the engineer deploys the model to Databricks Model Serving, they receive 401 errors saying the user is not authenticated when trying to access the database. The engineer deploys the code via the REST API with the following payload:

Their Databricks administrators store cloud credentials under a Databricks secret scope called
"cloud_creds" with key "db_key". These credentials can be used to authenticate to the cloud provider's SDK.
Which change can the engineer make so the endpoint can authenticate to the remote database while avoiding storing the access tokens in plain text?

A. Log the model to MLflow and store {{secrets/cloud_creds/db_key}} as a MLflow secret in
/.mlflow/credentials. Register the model in Unity Catalog. When deployed in Databricks Model Serving, MLflow will automatically authenticate when the model server starts.
B. Add "environment_vars": {"DB_ACCESS_TOKEN": "{{secrets/cloud_creds/db_key}}"} to the served_entity. Change the PyFunc model code to read the environment variable and pass the secret to the cloud provider's SDK.
C. Retrieve the secret values using a notebook with dbutils.secrets.get(scope="cloud_creds", key="db_key"). Add "environment_vars":
{"DB_ACCESS_TOKEN": ""} to the served_entity. Change the PyFunc model code to read the environment variable and pass the secret to the cloud provider's SDK.
D. Retrieve the secret values using a notebook with dbutils.secrets.get(scope="cloud_creds", key="db_key"). Log the model to MLflow with the access token value as a custom MLflow artifact.
Register the model in Unity Catalog. Change the PyFunc model code to read the artifact and pass the secret to the cloud provider's SDK.

Answer: B

Explanation:
Databricks Model Serving supports secure secret injection by referencing Databricks secret scopes directly in the served entity configuration. By mapping the secret to an environment variable using the {{secrets/scope/key}} syntax, the model can securely access the credential at runtime without exposing it in plain text. The PyFunc model can then read the environment variable and authenticate to the cloud provider's SDK, resolving the 401 error while following security best practices.

NEW QUESTION # 67
Which deployment paradigm can centrally compute predictions for a single record with exceedingly fast results?

A. None of these strategies will accomplish the task.
B. Edge/on-device
C. Streaming
D. Real-time
E. Batch

Answer: D

NEW QUESTION # 68
......

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