A web application needs to return predictions in under 50 milliseconds for each user request. Which inference approach is most appropriate?

A SparkML model applied to a batch DataFrame every hour

A SparkML model used in a Structured Streaming job

A SparkML model applied to a DataFrame on each request

A single-node model served as a REST API

For streaming scoring with SparkML, which API is used to apply a model to a stream of data?

Batch DataFrame with foreachBatch()

Spark Streaming DStreams with map() and predict()

MLlib's StreamingLinearRegressionWithSGD

Structured Streaming with a streaming DataFrame and transform()

DatabricksCertified Machine Learning Professional

Domain 1Objective 1

Using Spark ML MACHINE-LEARNING-PROFESSIONAL Practice Questions (Page 4)

Part of the Model Development domain, which accounts for 44% of the MACHINE-LEARNING-PROFESSIONAL exam. Databricks does not publish an official question count, but from its 120-minute exam (~50–80 total, ~22–35 in this domain), expect 6–9 from this objective — we provide 22 practice questions to prepare you well beyond it. (estimate)

22questions here

5free pages

7concepts

44%of the exam

Questions 16–20

16foundation · easy
A web application needs to return predictions in under 50 milliseconds for each user request. Which inference approach is most appropriate?
Select an answer first
17foundation · easy
Which SparkML class is used to define the set of hyperparameter values to search over during tuning?
Select an answer first
18foundation · easy
Which SparkML method is used to apply a fitted model to a batch DataFrame to generate predictions?
Select an answer first
19foundation · easy
Which metric is commonly used with RegressionEvaluator to measure the average squared difference between predicted and actual values?
Select an answer first
20foundation · easy
For streaming scoring with SparkML, which API is used to apply a model to a stream of data?
Select an answer first

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Using Spark ML MACHINE-LEARNING-PROFESSIONAL Practice Questions (Page 4)

Questions 16–20

A web application needs to return predictions in under 50 milliseconds for each user request. Which inference approach is most appropriate?

Which SparkML class is used to define the set of hyperparameter values to search over during tuning?

Which SparkML method is used to apply a fitted model to a batch DataFrame to generate predictions?

Which metric is commonly used with RegressionEvaluator to measure the average squared difference between predicted and actual values?

For streaming scoring with SparkML, which API is used to apply a model to a stream of data?

Keep studying — Model Development