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

TopicDetails
Topic 1
  • 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 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 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 4
  • 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 5
  • Identify JIT feature values as a need for real-time deployment
  • Describe how to list all webhooks and how to delete a webhook
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 the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 8
  • Describe model serving deploys and endpoint for every stage
  • Identify scenarios in which feature drift and
  • or label drift are likely to occur
Topic 9
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
Topic 10
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 11
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments

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Databricks Certified Machine Learning Professional Sample Questions (Q122-Q127):

NEW QUESTION # 122
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?

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 # 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?

Answer: A


NEW QUESTION # 124
A machine learning engineer would like to compute predictions on inference data as it becomes available through the pipeline in microbatches. The predictions should be stored in a table for query later. Which deployment strategy can the engineer use?

Answer: C

Explanation:
A streaming deployment strategy processes incoming data continuously or in microbatches, allowing predictions to be computed as new data arrives. The results can then be written to a table for later querying. This approach balances near-real-time inference with manageable resource use, making it ideal for scenarios where data flows steadily through a pipeline.


NEW QUESTION # 125
A machine learning engineer has a machine learning pipeline where predictions are updated annually. The final prediction dataset contains millions of rows, and that dataset is irregularly accessed. Which solution should the machine learning engineer use to maintain cost efficiency?

Answer: B

Explanation:
For data that is large in size, infrequently accessed, and updated only periodically, cloud-based object storage (such as AWS S3, Azure Blob Storage, or Google Cloud Storage) is the most cost- efficient option. It offers durable, scalable, and inexpensive storage compared to in-memory or low-latency databases, which are optimized for frequent access and real-time performance rather than long-term, infrequent retrieval.


NEW QUESTION # 126
Which of the following is a simple, low-cost method of monitoring numeric feature drift?

Answer: A


NEW QUESTION # 127
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