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Amazon Web Services MLS-C01 Dumps

Exam Code:
MLS-C01
Exam Name:
AWS Certified Machine Learning - Specialty
Last Update: Aug 16, 2022
208 Questions
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MLS-C01 Exam Details

Format: Multiple choice, Multiple Answer
Exam Type: Specialty
Exam Method: Testing center or online proctored exam
Time: 180 Min
Exam Price: $300 USD
Language: Available in English, Japanese, Korean, and Simplified Chinese

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AWS Certified Machine Learning - Specialty Questions and Answers

Questions 1

A large company has developed a B1 application that generates reports and dashboards using data collected from various operational metrics The company wants to provide executives with an enhanced experience so they can use natural language to get data from the reports The company wants the executives to be able ask questions using written and spoken interlaces

Which combination of services can be used to build this conversational interface? (Select THREE )

Options:

A.

Alexa for Business

B.

Amazon Connect

C.

Amazon Lex

D.

Amazon Poly

E.

Amazon Comprehend

F.

Amazon Transcribe

Questions 2

A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting. Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.

What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?

Options:

A.

Implement an AWS Lambda function to long Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

B.

Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

C.

Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

D.

Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting.

Questions 3

A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.

Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population.

How should the Data Scientist correct this issue?

Options:

A.

Drop all records from the dataset where age has been set to 0.

B.

Replace the age field value for records with a value of 0 with the mean or median value from the dataset.

C.

Drop the age feature from the dataset and train the model using the rest of the features.

D.

Use k-means clustering to handle missing features.

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