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

TopicDetails
Topic 1
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
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 live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
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
  • Describe the advantages of using the pyfunc MLflow flavor
  • Manually log parameters, models, and evaluation metrics using MLflow
Topic 6
  • 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 7
  • Describe model serving deploys and endpoint for every stage
  • Identify scenarios in which feature drift and
  • or label drift are likely to occur

>> Databricks-Machine-Learning-Professional Standard Answers <<

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Databricks Certified Machine Learning Professional Sample Questions (Q184-Q189):

NEW QUESTION # 184
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: A


NEW QUESTION # 185
A data scientist set up a machine learning pipeline to automatically log feature importance data from a CSV with each run. They now want to view the feature importance data in Databricks.
Which location in Databricks will show this data?

Answer: D

Explanation:
When a data scientist logs feature importance data (e.g., as a CSV or JSON file) using MLflow, it is stored as an artifact of that specific run. These artifacts are visible under the Artifacts section of the MLflow Run page in Databricks, where users can view or download the logged files associated with that particular experiment run.


NEW QUESTION # 186
A machine learning engineer wants to view all of the active MLflow Model Registry Webhooks for a specific model.
They are using the following code block:

Which of the following changes does the machine learning engineer need to make to this code block so it will successfully accomplish the task?

Answer: D


NEW QUESTION # 187
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: B


NEW QUESTION # 188
Which stage in the MLflow Model Registry is typically used for models currently serving production traffic?

Answer: A

Explanation:
In MLflow Model Registry stages:
Staging -> testing before release
Production -> serving real users
Archived -> retired models


NEW QUESTION # 189
......

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