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

Topic
Details

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

• Describe concept drift and its impact on model efficacy
• Describe summary statistic monitoring as a simple solution for numeric feature drift

Topic 4

• Describe the advantages of using the pyfunc MLflow flavor
• Manually log parameters, models, and evaluation metrics using MLflow

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 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

Topic 8

• Identify live serving benefits of querying precomputed batch predictions
• Describe Structured Streaming as a common processing tool for ETL pipelines

Topic 9

• Identify JIT feature values as a need for real-time deployment
• Describe how to list all webhooks and how to delete a webhook

Topic 10

• 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 11

• Identify the requirements for tracking nested runs
• Describe an MLflow flavor and the benefits of using MLflow flavors

 

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Databricks Certified Machine Learning Professional Sample Questions (Q137-Q142):

NEW QUESTION # 137
Which statement is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov- Smirnov (KS) test for numeric feature drift detection?

• A. All of these reasons
• B. JS does not require any manual threshold or cutoff determinations
• C. JS is not normalized or smoothed
• D. JS is more robust when working with large datasets
• E. None of these reasons

Answer: D

 

NEW QUESTION # 138
Which of the following MLflow operations can be used to automatically calculate and log a Shapley feature importance plot?

• A. mlflow.shap.log_explanation
• B. mlflow.log_figure
• C. client.log_artifact
• D. mlflow.shap
• E. None of these operations can accomplish the task.

Answer: D

 

NEW QUESTION # 139
A Machine Learning Engineer needs to digitize millions of historical documents spanning 200+ years with vastly different handwriting styles, fonts, languages, document conditions, and paper types. To do this, the engineer wants to train thousands of specialized OCR models to extract text. The number of documents vary significantly. They need an efficient approach for this parallel model training task. Which approach suits their needs?

• A. Use Spark for data preprocessing and orchestrate parallel model training jobs through Databricks workflows.
• B. Use Spark MLlib with custom Spark UDFs to distribute across partitions.
• C. Use Ray map_in_batches to allocate resources based on the various sizes of models and data volumes.
• D. Use Scikit-learn's ML library within a Pandas UDF to parallelize training across data partitions.
  Use groupby.applyInPandas.

Answer: C

Explanation:
Ray's map_in_batches API is designed for large-scale, heterogeneous parallel workloads where tasks vary significantly in data size, model complexity, and resource requirements. It enables dynamic resource allocation and efficient scheduling across many specialized OCR training jobs, making it well suited for training thousands of models with uneven document volumes and computational needs.

 

NEW QUESTION # 140
Why is Delta Lake time travel useful in ML pipelines?

• A. Faster model inference
• B. Model tuning
• C. Smaller datasets
• D. Reproducible training datasets

Answer: D

Explanation:
Time travel allows training on exact historical datasets.

 

NEW QUESTION # 141
Which statement describes streaming with Spark as a model deployment strategy?

• A. The inference of all types of records in real-time
• B. The inference of batch processed records as soon as a Spark job is run
• C. The inference of incrementally processed records as soon as trigger is hit
• D. The inference of batch processed records as soon as a trigger is hit
• E. The inference of incrementally processed records as soon as a Spark job is run

Answer: E

 

NEW QUESTION # 142
......

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