// aws certified machine learning - specialty (mls-c01)
AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY-MLS-C01
Prepare for AWS Certified Machine Learning - Specialty by mastering key domains and scenarios. Ideal for data scientists and machine learning engineers.
368
questions
65
in exam
240m
duration
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Model Evaluation, Monitoring, and Deploymentmedium
21 votes · last validated recently
A retail company uses a machine learning (ML) model for daily sales forecasting. The model has provided inaccurate results for the past 3 weeks. At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3.
The company's ML team determines that the inaccuracies are occurring because of a change in the value distributions of the model features. The ML team must implement a solution that will detect when this type of change occurs in the future.
Which solution will meet these requirements with the LEAST amount of operational overhead?
The company's ML team determines that the inaccuracies are occurring because of a change in the value distributions of the model features. The ML team must implement a solution that will detect when this type of change occurs in the future.
Which solution will meet these requirements with the LEAST amount of operational overhead?
A
Use Amazon SageMaker Model Monitor to create a data quality baseline. Confirm that the emit_metrics option is set to Enabled in the baseline constraints file. Set up an Amazon CloudWatch alarm for the metric.
100%
B
Use Amazon SageMaker Model Monitor to create a model quality baseline. Confirm that the emit_metrics option is set to Enabled in the baseline constraints file. Set up an Amazon CloudWatch alarm for the metric.
0%
C
Use Amazon SageMaker Debugger to create rules to capture feature values Set up an Amazon CloudWatch alarm for the rules.
0%
D
Use Amazon CloudWatch to monitor Amazon SageMaker endpoints. Analyze logs in Amazon CloudWatch Logs to check for data drift.
0%
04 · coverage
Exam domains
Data Management and Preparation25%
138 q
Machine Learning Model Development40%
121 q
Model Evaluation, Monitoring, and Deployment30%
91 q
Ethical and Legal Considerations in Machine Learning5%
18 q
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368 questions
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updated 7h ago·devco · cert
premiumamazon● updated 7h ago
AWS Certified Machine Learning - Specialty (MLS-C01)
Prepare for AWS Certified Machine Learning - Specialty by mastering key domains and scenarios. Ideal for data scientists and machine learning engineers.
// OFFICIAL EXAM LOGISTICS
source · amazon web services
// PASSING SCORE
750 / 1000· scaled
minimum to pass
75%of scale
// QUESTIONS
65official
// DURATION
240min
// EXAM FEE
300usd
// ITEM TYPES
multiple choice
multiple response
// questions
369in bank
// pass rate
92%self-reported
// last update
7h agoago
// add cadence · 12w
// downloadable pdf bank
Get the whole bank as a PDF
Every question tagged by domain, with a detailed explanation. Preview the first pages free — buy once for lifetime access to the full PDF.
369 questions
// what's inside
- 369 exam-style questions with verified answers
- Every question tagged by domain (4 domains covered)
- A detailed explanation / rationale for every question
// sample · 1 of 368
See what's in the bank.
#000df09e-c7c3-4cbd-86c9-8bd834e497ccModel Evaluation, Monitoring, and Deploymentmedium
↑ +47· 5 comments· seen 1,284×
A retail company uses a machine learning (ML) model for daily sales forecasting. The model has provided inaccurate results for the past 3 weeks. At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3.
The company's ML team determines that the inaccuracies are occurring because of a change in the value distributions of the model features. The ML team must implement a solution that will detect when this type of change occurs in the future.
Which solution will meet these requirements with the LEAST amount of operational overhead?
The company's ML team determines that the inaccuracies are occurring because of a change in the value distributions of the model features. The ML team must implement a solution that will detect when this type of change occurs in the future.
Which solution will meet these requirements with the LEAST amount of operational overhead?
A
Use Amazon SageMaker Model Monitor to create a data quality baseline. Confirm that the emit_metrics option is set to Enabled in the baseline constraints file. Set up an Amazon CloudWatch alarm for the metric.
100%B
Use Amazon SageMaker Model Monitor to create a model quality baseline. Confirm that the emit_metrics option is set to Enabled in the baseline constraints file. Set up an Amazon CloudWatch alarm for the metric.
0%C
Use Amazon SageMaker Debugger to create rules to capture feature values Set up an Amazon CloudWatch alarm for the rules.
0%D
Use Amazon CloudWatch to monitor Amazon SageMaker endpoints. Analyze logs in Amazon CloudWatch Logs to check for data drift.
0%Community converges on A. Official key confirms.
21 votes · last validated recently
// explanation
Model Monitor's data quality baseline detects feature distribution shifts, and CloudWatch alarms provide alerts, meeting the requirement with minimal overhead.
contributed by
N1
@netdiver· revised by @cloud_chris// coverage · 4 domains
What this bank tests.
D125%
Data Management and Preparation138 q
D240%
Machine Learning Model Development121 q
D330%
Model Evaluation, Monitoring, and Deployment91 q
D45%
Ethical and Legal Considerations in Machine Learning18 q
// composition
By format & difficulty.
// by format
82%
18%
single · 303multi · 65
// by difficulty
easy
70· 19%
medium
254· 69%
hard
44· 12%
// contributors · 0 engineers
Built by people who took the exam.
Every question is contributed by someone who recently sat AWS Certified Machine Learning - Specialty, then reviewed by an SME before landing in the bank. These are the most prolific contributors.
No contributors for this exam yet. Be the first to contribute a question.
// community questions
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// top community questionsview all →
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The official passing mark for this exam is 720.
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How long is the certification valid?
The certification is valid for 3 years. Re-certify every three years to maintain your AWS certification.
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What experience is recommended?
Proficiency in Python or R, familiarity with machine learning concepts.
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