Azure Databricks Kantar Day 6

Which among the following are required to use Databricks Lakehouse Monitoring?

• Your workspace must be enabled for Unity Catalog and you must have access to Databricks SQL.

• Only Delta managed tables, external tables, views, and materialized views are supported for monitoring.

• It must be supported by Azure ML.

To monitor the performance of a machine learning model, you attach the monitor to __________that holds the model's inputs and corresponding predictions.

• Snapshot

• Inference Table

• Time series

Drift table is used for storing?

• Known 'good data' for comparison.

• Profile statistics

• Change in profile metrics compared to previous time window

_________ prepares the dataset for model training, and then performs and records a set of trials that creates, tunes, and evaluates multiple models.

• Azure ML

• AutoML

• LLM

Use cases of Features in training include

• Create a TrainingSet when lookup keys do not match the primary keys

• Create a TrainingSet containing two features with the same name from different feature tables

• Create a TrainingSet using the same feature multiple times

Model versioning, model serving, model deployment via aliases are the key features of?

• Unity Catalog

• AutoML

• Databricks SQL

ML Flow models are saved in?

• DBFS

• Hive metastore

• ADLS Gen 2

You can create a Python UDF in a notebook or in Databricks SQL. True or False?

• True

• False

______________ enables creation of scalable GPU endpoints for deep learning models with no extra configuration.

• Databricks Runtime for Machine Learning

• Databricks Model Serving

• Databricks SQL

For real-time serving use cases, publish the features to __________

• Offline store

• Online store

• Unity Catalog