Databricks Databricks-Machine-Learning-Professional Quiz 1 Topic 1 Questions 1-5

Question: 1

Which of the following Databricks-managed MLflow capabilities is a centralized model store?

AModels

BModel Registry

CModel Serving

DFeature Store

EExperiments

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Question: 2

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

AJensen-Shannon test

BSummary statistics trends

CChi-squared test

DNone of these can be used to monitor feature drift

EKolmogorov-Smirnov (KS) test

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Question: 3

Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?

AStreaming

BBatch

CEdge/on-device

DNone of these strategies will accomplish the task.

EReal-time

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Question: 4

Which of the following describes the concept of MLflow Model flavors?

AA convention that deployment tools can use to wrap preprocessing logic into a Model

BA convention that MLflow Model Registry can use to version models

CA convention that MLflow Experiments can use to organize their Runs by project

DA convention that deployment tools can use to understand the model

EA convention that MLflow Model Registry can use to organize its Models by project

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Question: 5

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?

Adf = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df)

Bfs.score_model(model_uri, spark_df)

Cdf = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df)
df = fs.get_missing_features(spark_df)

Dfs.score_batch(model_uri, df)

Efs.score_batch(model_uri, spark_df)

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Databricks Databricks-Machine-Learning-Professional Quiz:1 Topic:1 Questions:1-5