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

Topic	Details
Topic 1	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 2	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 3	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
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 concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 6	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry

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Databricks Certified Machine Learning Professional Sample Questions (Q83-Q88):

NEW QUESTION # 83
Which MLflow feature helps reproduce training runs?

A. MLflow Projects
B. Delta Sharing
C. MLflow Tracking
D. Model Serving

Answer: C

Explanation:
Tracking logs:
parameters
code version
metrics
artifacts
making runs reproducible.

NEW QUESTION # 84
A data scientist wants to examine the data in the Feature Store table table from the database dev as a Spark DataFrame. They have access to Feature Store Client fs. Which line of code can be used to gel the data from table as a Spark DataFrame?

A. fs.create_table("dev.table")
B. fs.get_table("dev.table")
C. fs.get_table("table)
D. fs.read_table("dev.table")

Answer: D

Explanation:
To read data from a Feature Store table as a Spark DataFrame, the correct method is fs.read_table("dev.table"). This retrieves the contents of the table in the dev database using the Feature Store Client (fs) and returns it as a Spark DataFrame.

NEW QUESTION # 85
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?

A. This can only be viewed in the MLflow Experiments UI
B. mlflow.sklearn.load_model(model_uri)
C. client.list_artifacts(run_id)["feature-importances.csv"]
D. mlflow.load_model(model_uri)
E. client.pyfunc.load_model(model_uri)

Answer: D

NEW QUESTION # 86
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. 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.
B. 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.
C. 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.
D. 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.

Answer: A

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 # 87
Which of the following describes label drift?

A. Label drift is when there is a change in the distribution of a target variable
B. Label drift is when there is a change in the distribution of an input variable
C. Label drift is when there is a change in the distribution of the predicted target given by the model
D. None of these describe label drift
E. Label drift is when there is a change in the relationship between input variables and target variables

Answer: B

NEW QUESTION # 88
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