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Question: 01. A Data Scientist is working on optimizing a model during the
training process by varying multiple parameters. The Data Scientist observes
that, during multiple runs with identical parameters, the loss function converges
to different, yet stable, values.
What should the Data Scientist do to improve the training process?
a) Increase the learning rate. Keep the batch size the same.
b) Reduce the batch size. Decrease the learning rate.
c) Keep the batch size the same. Decrease the learning rate.
d) Do not change the learning rate. Increase the batch size. - ANSWER-Answer:
b) Reduce the batch size. Decrease the learning rate.
Question: 02. A Machine Learning Engineer is preparing a data frame for a
supervised learning task with the Amazon SageMaker Linear Learner algorithm.
The ML Engineer notices the target label classes are highly imbalanced and
multiple feature columns contain missing values. The proportion of missing
values across the entire data frame is less than 5%.
What should the ML Engineer do to minimize bias due to missing values?
a) Replace each missing value by the mean or median across non-missing values
in same row.
b) Delete observations that contain missing values because these represent less
than 5% of the data.
c) Replace each missing value by the mean or median across non-missing values
in the same column.
d) For each feature, approximate the missing values using supervised learning
based on other features. - ANSWER-Answer:
d) For each feature, approximate the missing values using supervised learning
based on other features.