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

TopicDetails
Topic 1
  • 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 2
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 3
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
Topic 4
  • Describe the advantages of using the pyfunc MLflow flavor
  • Manually log parameters, models, and evaluation metrics using MLflow
Topic 5
  • Identify the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 6
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments
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
  • 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 9
  • Describe model serving deploys and endpoint for every stage
  • Identify scenarios in which feature drift and
  • or label drift are likely to occur
Topic 10
  • 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 (Q28-Q33):

NEW QUESTION # 28
A data scientist set up a machine learning pipeline to automatically log a data visualization with each run. They now want to view the visualizations in Databricks.
Which of the following locations in Databricks will show these data visualizations?

Answer: D


NEW QUESTION # 29
How can you save a trained Spark ML PipelineModel?

Answer: C

Explanation:
Example:
pipelineModel.write().overwrite().save("/model")


NEW QUESTION # 30
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 the training set logged with the model, but a few of the feature values in the column spend have since been updated and arc present in the customer-level Spark DataFrame spark_df. The customer_id column is the primary key of spark_df and the training set used when training and logging the model. Which code block can be used to compute predictions for the training set while overwriting its old spend values with the new spend values from spark_df?

Answer: B

Explanation:
To perform batch inference while incorporating updated feature values (like spend) from a DataFrame (spark_df), the correct approach is to use fs.get_updated_features(spark_df) to refresh the relevant features based on the primary key (customer_id), then score the model using fs.score_batch(...). This ensures predictions are made with the latest data.


NEW QUESTION # 31
A Machine Learning Engineer is building an application that requires low latency data lookups in response to a user's question following a RAG based search. They want to ensure their users can receive as recent data as possible for urgent requests, so data should not be more than a few minutes late. The underlying data is a large table that may contain hundreds of gigabytes of data. Which data serving approach will suit their use case?

Answer: D

Explanation:
Online tables with continuous sync mode are designed for low-latency serving while keeping data fresh within minutes. Continuous sync incrementally propagates updates from the large underlying table to the online store, ensuring near-real-time availability for RAG-based lookups without requiring full refreshes, which is ideal for urgent, freshness-sensitive queries on large datasets.


NEW QUESTION # 32
A data scientist has run the following line of code to create a Feature Store table:

Which statement correctly describes the data in the Feature Store table new_table?

Answer: B

Explanation:
The fs.create_table() command defines metadata for a Feature Store table, such as its name, primary key, schema, and description - but it does not insert any data into the table. This command simply registers an empty table structure based on the provided schema (df.schema).
To populate the table with data, a separate fs.write_table() or equivalent write operation must be executed afterward.


NEW QUESTION # 33
......

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