Amazon MLS-C01 Exam Questions

Exam Name: AWS Certified Machine Learning – Specialty

Exam Code: MLS-C01 AWS ML Specialty

Related Certification(s): Amazon Specialty Certifications, Amazon AWS Certified Machine Learning Certifications

Certification Provider: Amazon

Actual Exam Duration: 180 Minutes

Number of MLS-C01 Practice Questions: 330 (updated: )

Expected MLS-C01 Exam Topics, as suggested by Amazon:
Topic 1: Data Engineering
It discusses creating data repositories for ML, identifying and implementing a data ingestion solution. Lastly, the topic delves into identifying and implementing a data transformation solution.
Topic 2: Exploratory Data Analysis
This topic covers sanitizing and preparing data for modeling and performing feature engineering. Additionally, it discusses analyzing and visualizing data for ML.
Topic 3: Modeling
The topic of modeling deals with framing business problems as ML problems, choosing the suitable model(s) for a given ML problem, training ML models. It also discusses hyperparameter optimization and evaluation of ML models.
Topic 4: Machine Learning Implementation and Operations
Building ML solutions for performance, availability, scalability, resiliency, and fault tolerance is discussed in this topic. It also focuses on suitable ML services and features for a given problem. Lastly, the topic delves into applying basic AWS security practices to ML solutions and deploying and operationalizing ML solutions.
Free AWS MLS-C01 Exam Actual Questions
Note: AWS MLS-C01 Premium Questions were last updated on

Q1#
[Modeling]

A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker
The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm
The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the model

What should the Specialist do to address the performance issues with the current solution?

Q2#
[Data Engineering]

Acybersecurity company is collecting on-premises server logs, mobile app logs, and loT sensor dat
a. The company backs up the ingested data in an Amazon S3 bucket and sends the ingested data to Amazon OpenSearch Service for further analysis.
Currently, the company has a custom ingestion pipeline that is running on Amazon EC2 instances.
The company needs to implement a new serverless ingestion pipeline that can automatically scale to handle sudden changes in the data flow.

Which solution will meet these requirements MOST cost-effectively?

Q3#
[Modeling]

An insurance company developed a new experimental machine learning (ML) model to replace an existing model that is in production.
The company must validate the quality of predictions from the new experimental model in a production environment before the company uses the new experimental model to serve general user requests.

Which solution will meet these requirements?

Q4#
[Modeling]

A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service.
The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.

Based on the model evaluation results, why is this a viable model for production?

Q5#
[Exploratory Data Analysis]

A Machine Learning Specialist is deciding between building a naive Bayesian model or a full Bayesian network for a classification problem.
The Specialist computes the Pearson correlation coefficients between each feature and finds that their absolute values range between 0.1 to 0.95.

Which model describes the underlying data in this situation?