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QUESTION: 6

A data scientist has developed a model to predict ice cream sales using the expected temperature and expected number of hours of sun in the day. However, the expected temperature is dropping beneath the range of the input variable on which the model was trained.
Which of the following types of drift is present in the above scenario?

Label drift
None of these
Concept drift
Prediction drift
Feature drift

Answer(s): E

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QUESTION: 7

A data scientist wants to remove the star_rating column from the Delta table at the location path. To do this, they need to load in data and drop the star_rating column.
Which of the following code blocks accomplishes this task?

spark.read.format("delta").load(path).drop("star_rating")
spark.read.format("delta").table(path).drop("star_rating")
Delta tables cannot be modified
spark.read.table(path).drop("star_rating")
spark.sql("SELECT * EXCEPT star_rating FROM path")

Answer(s): D

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QUESTION: 8

Which of the following operations in Feature Store Client fs can be used to return a Spark DataFrame of a data set associated with a Feature Store table?

fs.create_table
fs.write_table
fs.get_table
There is no way to accomplish this task with fs
fs.read_table

Answer(s): A

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QUESTION: 9

A machine learning engineer is in the process of implementing a concept drift monitoring solution.
They are planning to use the following steps:
1. Deploy a model to production and compute predicted values
2. Obtain the observed (actual) label values
3. _____
4. Run a statistical test to determine if there are changes over time Which of the following should be completed as Step #3?

Obtain the observed values (actual) feature values
Measure the latency of the prediction time
Retrain the model
None of these should be completed as Step #3
Compute the evaluation metric using the observed and predicted values

Answer(s): D

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QUESTION: 10

Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov- Smirnov (KS) test for numeric feature drift detection?

All of these reasons
JS is not normalized or smoothed
None of these reasons
JS is more robust when working with large datasets
JS does not require any manual threshold or cutoff determinations

Answer(s): D

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