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

Topic	Details
Topic 1	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 2	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 3	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 4	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 5	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
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	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 (Q122-Q127):

NEW QUESTION # 122
Which of the following is a benefit of logging a model signature with an MLflow model?

A. The schema of input data will be converted to match the signature
B. The model will be secured by the user that developed it
C. The model can be deployed using real-time serving tools
D. The schema of input data can be validated when serving models
E. The model will have a unique identifier in the MLflow experiment

Answer: A

NEW QUESTION # 123
A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on customer-level Spark DataFrame spark_df, but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.
Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?

A. fs.score_model(model_uri, spark_df)
B. df = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df)
df = fs.get_missing_features(spark_df)
C. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df)
D. fs.score_batch(model_uri, df)
E. fs.score_batch(model_uri, spark_df)

Answer: E

NEW QUESTION # 124
A Data Scientist is tasked with developing models to forecast product demand. The company offers 5000 different product types, and the Data Scientist must generate weekly forecasts for each type. They have access to two years of historical purchase data and are given ample project budget.
For their next project, they want to build 5000 separate Random Forest models, one for each product type. They aim to train all the models as quickly as possible with minimal setup.
Which approach meets these requirements?

A. Create a Databricks Workflow with 5000 tasks. Each task is configured to accept a product ID as a parameter which will then train a model based on the specified product ID.
B. Leverage the pandas function API (Grouped map) to group the data by product type and apply a custom model training function to each group.
C. Use the RandomForest method from MLlib. This will leverage Spark's parallel processing capability to train 5000 different models.
D. Use the DeepSpeed library to distribute the data by product across different nodes to enable the parallel training of multiple models.

Answer: B

Explanation:
The pandas function API with grouped map allows data to be grouped by product type and applies a custom training function independently to each group. This approach enables massive parallelism across the cluster with minimal orchestration or setup, making it well suited for rapidly training thousands of independent models in parallel.

NEW QUESTION # 125
Which of the following statements about built-in library-specific MLflow Model flavors is true?

A. Built-in library-specific flavors can only be used for logging models
B. Built-in library-specific flavors allow models to be exported as library objects
C. Built-in library-specific flavors are required for model signature use
D. Built-in library-specific flavors allow models to be used with any library/span>

Answer: B

Explanation:
Built-in library-specific MLflow model flavors (e.g., mlflow.sklearn, mlflow.xgboost) allow models to be exported and later loaded as native library objects, enabling seamless reuse with the original libraries for inference or further training.

NEW QUESTION # 126
Which of the following Databricks-managed MLflow capabilities is a centralized model store?

A. Feature Store
B. Model Serving
C. Experiments
D. Model Registry
E. Models

Answer: B

NEW QUESTION # 127
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