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

TopicDetails
Topic 1
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 2
  • 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 3
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
Topic 4
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments
Topic 5
  • Identify the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 6
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 7
  • 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 8
  • Identify JIT feature values as a need for real-time deployment
  • Describe how to list all webhooks and how to delete a webhook
Topic 9
  • 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

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Databricks Certified Machine Learning Professional Sample Questions (Q169-Q174):

NEW QUESTION # 169
Why are Delta tables often used to store machine learning features?

Answer: B

Explanation:
Delta Lake provides:
ACID transactions
time travel
schema enforcement
These are essential for reproducible ML pipelines.


NEW QUESTION # 170
A machine learning engineer needs to deliver predictions of a machine learning model in real- time. However, the feature values needed for computing the predictions are available one week before the query time. Which feature is a benefit of using a batch serving deployment in this scenario rather than a real-time serving deployment where predictions are computed at query time?

Answer: A


NEW QUESTION # 171
A machine learning engineer is in the process of implementing a feature drift monitoring solution.
They are planning to use the following steps:
1. Measure the distributions of each feature variable in the training
set
2. Deploy a model to production
3. Measure the distributions of each feature variable in inference
4. _______
Which action should be completed as Step #4?

Answer: D

Explanation:
The final step in a feature drift monitoring solution is to run a statistical test (e.g., Kolmogorov- Smirnov test) to determine whether the feature distributions in production have significantly diverged from those in the training set. This helps detect drift and maintain model reliability.


NEW QUESTION # 172
A Machine Learning Engineer is using Lakehouse Monitoring to track the performance of ML models deployed in their environment. They want to monitor significant distributional drift in categorical features with a metric bounded on [0,1] for easy interpretation. Which statistical method should they use?

Answer: A

Explanation:
Jensen-Shannon Distance is well suited for measuring distributional drift in categorical features. It is symmetric, numerically stable, and bounded between 0 and 1, which makes it easy to interpret and ideal for monitoring categorical feature drift in Lakehouse Monitoring.


NEW QUESTION # 173
A Machine Learning Engineer is tasked with building an automated daily pipeline that updates a customer_features table in Unity Catalog. They have implemented a function, compute_customer_features, that returns a DataFrame with a unique customer_id as the primary key and want to ensure the latest feature values are merged into the table each day. Which code snippet implements this requirement?

Answer: C

Explanation:
Using write_table with mode set to merge updates existing rows and inserts new ones based on the table's primary key. This ensures that the latest feature values for each customer_id are merged into the existing feature table each day without overwriting the entire table, which is the correct and scalable approach for maintaining up-to-date customer features in Unity Catalog.


NEW QUESTION # 174
......

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