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

TopicDetails
Topic 1
  • 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 2
  • Identify the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 3
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
Topic 4
  • 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 5
  • Describe the advantages of using the pyfunc MLflow flavor
  • Manually log parameters, models, and evaluation metrics using MLflow
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 live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 8
  • 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 9
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift

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Databricks Certified Machine Learning Professional Sample Questions (Q148-Q153):

NEW QUESTION # 148
Label drift occurs where there is a change in which element?

Answer: C

Explanation:
Label drift refers to a change in the distribution of the target variable over time. This means the frequencies or proportions of classes or target values shift, which can impact model performance even if the input feature distributions remain unchanged.


NEW QUESTION # 149
A Machine Learning Engineer needs to build a time series model. In Databricks, they have created isolated environments in different workspaces for development, staging, and production.
To manage this model, they are planning on using a "deploy code" strategy. They are concerned that the model trained in development will not remain consistent across environments, due to differences in the dependencies installed. What can they do to ensure that the model is trained with the same packages?

Answer: B

Explanation:
Using the same Databricks Runtime across environments ensures a consistent base environment, and installing dependencies from a lock file guarantees identical package versions during training. This combination follows best practices for deploy-code workflows by making the training environment reproducible and preventing dependency drift between development, staging, and production.


NEW QUESTION # 150
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: A

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 # 151
A Machine Learning Engineer uses Lakehouse Monitoring to track their credit scoring model's performance. The existing profile metrics table contains three aggregate metrics:
- adefault_risk_score
- payment_history_score
- credit_utilization_score
They need to:
1. Create a composite risk rating that combines these three scores using weights of 0.5, 0.3, and 0.2 respectively.
2. Monitor drift of this composite score against an established baseline.
Which approach should be used to implement both requirements within Lakehouse Monitoring?

Answer: B

Explanation:
Lakehouse Monitoring supports derived metrics that are computed from existing profile metrics using custom expressions. By defining a derived metric for the composite_risk_rating using the specified weights, the composite score becomes a first-class metric in the monitoring framework.
A drift metric can then be directly configured on this derived metric to compare current values against the baseline, fulfilling both the composite calculation and drift monitoring requirements in a native, governed way.


NEW QUESTION # 152
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: E


NEW QUESTION # 153
......

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