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

TopicDetails
Topic 1
  • Identify the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 2
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments
Topic 3
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 4
  • Describe the advantages of using the pyfunc MLflow flavor
  • Manually log parameters, models, and evaluation metrics using MLflow
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
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift
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
Topic 8
  • 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 9
  • Describe model serving deploys and endpoint for every stage
  • Identify scenarios in which feature drift and
  • or label drift are likely to occur
Topic 10
  • 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 (Q25-Q30):

NEW QUESTION # 25
A machine learning engineer needs to select a deployment strategy for a new machine learning application. The feature values are not available until the time of delivery, and results are needed exceedingly fast for one record at a time.
Which of the following deployment strategies can be used to meet these requirements?

Answer: E


NEW QUESTION # 26
A machine learning engineer has deployed a model recommender using MLflow Model Serving.
They now want to query the version of that model that is in the Staging stage of the MLflow Model Registry. Which model URI can be used to query the described model version?

Answer: D

Explanation:
To query a model version in a specific stage (like Staging) using MLflow Model Serving on Databricks, the correct model URI format is:
https://<databricks-instance>/model/<model-name>/<stage>/invocations
So, for the model named recommender in the Staging stage, the correct URI is:
https://<databricks-instance>/model/recommender/Staging/invocations.


NEW QUESTION # 27
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?

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 # 28
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?

Answer: D


NEW QUESTION # 29
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?

Answer: C


NEW QUESTION # 30
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