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

TopicDetails
Topic 1
  • 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 2
  • Identify JIT feature values as a need for real-time deployment
  • Describe how to list all webhooks and how to delete a webhook
Topic 3
  • Describe the advantages of using the pyfunc MLflow flavor
  • Manually log parameters, models, and evaluation metrics using MLflow
Topic 4
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
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 that data can arrive out-of-order with structured streaming
  • Identify how model serving uses one all-purpose cluster for a model deployment
Topic 7
  • Identify the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 8
  • Describe model serving deploys and endpoint for every stage
  • Identify scenarios in which feature drift and
  • or label drift are likely to occur

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Databricks Certified Machine Learning Professional Sample Questions (Q76-Q81):

NEW QUESTION # 76
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: C

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 # 77
A machine learning engineering manager has asked all of the engineers on their team to add text descriptions to each of the model projects in the MLflow Model Registry. They are starting with the model project "model" and they'd like to add the text in the model_description variable.
The team is using the following line of code:

Which change does the team need to make to the above code block to accomplish the task?

Answer: E


NEW QUESTION # 78
A data scientist has computed updated rows that contain new feature values for primary keys already stored in the Feature Store table features. The updated feature values are stored in the DataFrame features_df. They want to update the rows in features if the associated primary key is in features_df. If a row's primary key is not in features_df, they want the row to remain unchanged in features. Which code block can they use to perform this task using the Feature Store Client fs?

Answer: B

Explanation:
To update existing rows based on primary keys while leaving other rows unchanged, the correct mode to use with fs.write_table() is "merge". This performs an upsert operation - updating rows where keys match and keeping others intact - making it ideal for updating feature values in a Feature Store table.


NEW QUESTION # 79
A machine learning engineer needs to select a deployment strategy for a new machine learning application. The machine learning application requires central prediction computation and exceedingly fast results, but only a handful of predictions need to be computed at a time. Which deployment strategy can be used to meet these requirements?

Answer: C

Explanation:
Real-time deployment is the appropriate strategy when predictions need to be computed centrally with very low latency, even if only a small number of predictions are required at a time. This ensures fast responses for applications requiring immediate inference.


NEW QUESTION # 80
A machine learning engineer has registered a sklearn model in the MLflow Model Registry using the sklearn model flavor with UI model_uri. Which operation can be used to load the model as an sklearn object for batch deployment?

Answer: C


NEW QUESTION # 81
......

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