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

Topic	Details
Topic 1	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 2	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
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	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 model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 6	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 7	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow

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Databricks Certified Machine Learning Professional Sample Questions (Q75-Q80):

NEW QUESTION # 75
After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set.
Which of the following SQL commands can be used to accomplish this task?

A. DESCRIBE
B. DESCRIBE HISTORY
C. HISTORY
D. VERSION
E. TIMESTAMP

Answer: B

NEW QUESTION # 76
A machine learning engineering team has decided that they need to have predictions be made available for querying in continuous, equal-sized increments. A computation can be included in one of the increments when all of its feature values are in the inference Spark DataFrame. Which of the following tools can be used to provide this type of continuous inference?

A. MLflow
B. Delta Lake
C. Structured Streaming
D. Spark UDFs

Answer: C

Explanation:
Structured Streaming in Apache Spark enables continuous inference by processing incoming data in microbatches or continuous increments. It ensures that each computation occurs only when all required feature values are available in the inference DataFrame, supporting real-time or near-real-time prediction pipelines with consistent, equal-sized processing intervals.

NEW QUESTION # 77
Which MLflow function logs files like plots or datasets?

A. mlflow.log_artifact()
B. mlflow.log_metric()
C. mlflow.log_param()
D. mlflow.save_model()

Answer: A

Explanation:
Artifacts include:
charts
model files
datasets.

NEW QUESTION # 78
A Machine Learning Engineer needs to deploy a custom model using Databricks Model Serving.
The model requires an external tokenizer file (for example, a vocabulary or pre-trained tokenizer) to function correctly. They need to ensure this tokenizer file is included with the model so it is available during model serving. How should they package this tokenizer file as part of the model deployment?

A. Use the code_paths parameter in mlflow.pyfunc.log_model() to include the tokenizer file, assuming it's part of the custom Python logic needed by the model.
B. Include tokenizer file as part of the model's software dependencies by adding it to the conda_env specification.
C. Use the artifacts parameter in mlflow.pyfunc.log_model() to package the tokenizer file, referencing its path in Volumes.
D. Access the tokenizer file directly from Volumes at model inference time, which requires pre- provisioning access to the Volume for every model serving endpoint.

Answer: C

Explanation:
The artifacts parameter in mlflow.pyfunc.log_model is designed for packaging non-code assets required at inference time, such as tokenizer files. By logging the tokenizer as a model artifact and referencing its path, MLflow ensures the file is versioned with the model and automatically made available to Databricks Model Serving during inference.

NEW QUESTION # 79
A data scientist has developed a model to predict whether or not it will rain using the expected temperature and expected cloud coverage. However, the proportion of days where it actually rains has increased dramatically from the proportion in the data on which the model was trained.
Which type of drift is present in the above scenario?

A. Prediction drift
B. Feature drift
C. Label drift
D. Concept drift

Answer: C

Explanation:
Label drift occurs when the distribution of the target variable (label) changes over time while the relationship between features and the label remains the same. In this scenario, the proportion of days when it rains (the label) has changed significantly compared to the training data, indicating label drift.

NEW QUESTION # 80
......

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