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MLS-C01 Amazon AWS Certified Machine Learning - Specialty

  • Amazon
  • 5 free sample questions

About this exam

MLS-C01 is the Amazon exam Amazon AWS Certified Machine Learning - Specialty. Paid practice material for this exam is not available yet, but you can try its free sample questions.

Exam code
MLS-C01
Vendor
Amazon
Practice questions
Not available yet
Free sample questions
5
Question bank last updated
Not available yet

Try 2 sample questions

Taken from the free sample set for MLS-C01.

Question 1

A manufacturing company has a large set of labeled historical sales data The manufacturer would like
to predict how many units of a particular part should be produced each quarter Which machine
learning approach should be used to solve this problem?

  1. Option A:
    Logistic regression
  2. Option B:
    Random Cut Forest (RCF)
  3. Option C:
    Principal component analysis (PCA)
  4. Option D:
    Linear regression
Show answer and explanation

Correct answer: D

Linear regression is a machine learning approach that can be used to solve this problem. Linear
regression is a supervised learning technique that can model the relationship between one or more 
input variables (features) and an output variable (target). In this case, the input variables could be
the historical sales data of the part, such as the quarter, the demand, the price, the inventory, etc.
The output variable could be the number of units to be produced for the part. Linear regression can
learn the coefficients (weights) of the input variables that best fit the output variable, and then use
them to make predictions for new data. Linear regression is suitable for problems that involve
continuous and numeric output variables, such as predicting house prices, stock prices, or sales
volumes.
References:
AWS Machine Learning Specialty Exam Guide
Linear Regression

Question 2

A Machine Learning Specialist is using Amazon Sage Maker to host a model for a highly available
customer-facing application. The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production To limit any risk of a negative customer experience, the Specialist 
wants to be able to monitor the model and roll it back, if needed What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?

  1. Option A:
    Create a SageMaker endpoint and configuration for the new model version. Redirect production
    traffic to the new endpoint by updating the client configuration. Revert traffic to the last version if
    the model does not perform as expected.
  2. Option B:
    Create a SageMaker endpoint and configuration for the new model version. Redirect production
    traffic to the new endpoint by using a load balancer Revert traffic to the last version if the model
    does not perform as expected.
  3. Option C:
    Update the existing SageMaker endpoint to use a new configuration that is weighted to send 5%
    of the traffic to the new variant. Revert traffic to the last version by resetting the weights if the model
    does not perform as expected.
  4. Option D:
    Update the existing SageMaker endpoint to use a new configuration that is weighted to send
    100% of the traffic to the new variant Revert traffic to the last version by resetting the weights if the
    model does not perform as expected
Show answer and explanation

Correct answer: C

Updating the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of
the traffic to the new variant is the simplest approach with the least risk to deploy the model and roll
it back, if needed. This is because SageMaker supports A/B testing, which allows the Specialist to
compare the performance of different model variants by sending a portion of the traffic to each
variant. The Specialist can monitor the metrics of each variant and adjust the weights accordingly. If
the new variant does not perform as expected, the Specialist can revert traffic to the last version by
resetting the weights to 100% for the old variant and 0% for the new variant. This way, the Specialist
can deploy the model without affecting the customer experience and roll it back easily if
needed.
References:
Amazon SageMaker
Deploying models to Amazon SageMaker hosting services
See all 5 free sample questions