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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 in our database: 307 (updated: Mar. 25, 2025)
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.
Disscuss Amazon MLS-C01 Topics, Questions or Ask Anything Related

Cora

14 days ago
Natural Language Processing is definitely covered. Know about text preprocessing, tokenization, and popular NLP models.
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Eulah

26 days ago
Nailed the AWS ML Specialty exam! Pass4Success's prep materials were spot-on. Saved me so much study time!
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Kaitlyn

28 days ago
Be ready for questions on model deployment and A/B testing. SageMaker endpoints and production variants are important topics.
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Ligia

1 months ago
Time series forecasting came up in my exam. Familiarize yourself with algorithms like ARIMA and Prophet. Pass4Success practice tests were spot on!
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Leonida

2 months ago
Just became AWS ML certified! Pass4Success made all the difference. Their questions matched the exam perfectly.
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Lilli

2 months ago
Expect questions on data labeling services like SageMaker Ground Truth. Know when and how to use them effectively.
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Meghann

2 months ago
I passed the AWS Certified Machine Learning - Specialty exam, and the Pass4Success practice questions were a great aid. There was a tough question on Machine Learning Implementation and Operations about the best practices for versioning machine learning models. Should I use Git or a specialized tool like DVC? I wasn't entirely confident, but I passed.
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Gaston

2 months ago
AWS security in ML pipelines is crucial. Understand IAM roles, KMS, and how to secure data at rest and in transit. Pass4Success helped me nail these concepts.
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Torie

3 months ago
AWS ML Specialty exam: check! Couldn't have done it without Pass4Success. Their practice tests were key to my success.
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Lenna

3 months ago
Deep learning architectures are definitely on the exam. Be prepared to identify suitable neural network structures for different tasks.
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Dannie

3 months ago
Kinesis questions surprised me. Know how to configure streams and use Kinesis for real-time data processing. Pass4Success materials covered this well.
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Javier

3 months ago
Just passed the AWS Certified Machine Learning - Specialty exam! The Pass4Success practice questions were a big help. One question that threw me off was in the Modeling domain, asking about the advantages of using ensemble methods like Random Forest over single models. I wasn't completely sure, but I still managed to pass.
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Portia

4 months ago
Passed my AWS ML cert today! Pass4Success helped me prepare efficiently. Their questions were right on target.
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Franklyn

4 months ago
Don't underestimate the importance of model evaluation metrics. ROC curves, confusion matrices, and F1 scores were all fair game. Study these thoroughly!
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Elke

4 months ago
I passed the AWS Certified Machine Learning - Specialty exam, and the Pass4Success practice questions were very useful. A question that I found difficult was about Exploratory Data Analysis, asking which statistical test to use for comparing two sample means. Should it be a t-test or ANOVA? I wasn't sure, but I passed the exam.
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Darrel

4 months ago
I successfully passed the AWS Certified Machine Learning - Specialty exam, thanks to the Pass4Success practice questions. One question that puzzled me was related to Data Engineering, specifically about the most efficient way to handle missing data in a large dataset. Should I use imputation or deletion? I wasn't certain, but I made it through.
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Timmy

4 months ago
AWS Glue came up a lot in my exam. Make sure you understand ETL processes and how to use Glue for data preparation. Thanks to Pass4Success for the spot-on practice questions!
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Alberta

5 months ago
Aced the AWS Certified ML Specialty! Pass4Success questions were incredibly similar to the real thing. Highly recommend!
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Helga

5 months ago
Happy to share that I passed the AWS Certified Machine Learning - Specialty exam. The Pass4Success practice questions were instrumental in my preparation. There was a challenging question on Machine Learning Implementation and Operations about the best way to monitor model performance in production. Should I use a confusion matrix or ROC curve? I wasn't sure, but I still passed.
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Kimi

5 months ago
Know your ML algorithms inside out. The exam tests your ability to select the right algorithm for specific use cases. Brush up on supervised vs unsupervised learning!
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Pamella

5 months ago
I passed the AWS Certified Machine Learning - Specialty exam, and the Pass4Success practice questions were a lifesaver. One question that caught me off guard was about hyperparameter tuning in the Modeling domain. It asked which method, grid search or random search, would be more efficient for a large parameter space. I wasn't completely confident in my answer, but I passed!
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Mitsue

6 months ago
Wow, the AWS ML exam was tough but I made it! Pass4Success materials were a lifesaver, so relevant to the actual test.
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Glenna

6 months ago
Data preprocessing is key! Study feature engineering techniques like one-hot encoding and normalization. Pass4Success really helped me prepare for these questions.
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Adell

6 months ago
Just cleared the AWS Certified Machine Learning - Specialty exam! The Pass4Success practice questions were a great resource. There was a tricky question on Exploratory Data Analysis that asked about the most effective visualization technique for identifying outliers in a dataset. I debated between a box plot and a scatter plot, but I still managed to get through the exam.
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Gladys

6 months ago
Just passed the AWS ML Specialty exam! SageMaker questions were crucial. Be ready to configure hyperparameters and choose instance types for training jobs.
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Farrah

6 months ago
I recently passed the AWS Certified Machine Learning - Specialty exam, and I must say, the Pass4Success practice questions were incredibly helpful. One question that stumped me was about the best practices for data partitioning in a data lake, which is a key aspect of Data Engineering. I wasn't entirely sure if I should choose partitioning by date or by another attribute, but I managed to pass the exam nonetheless.
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Dalene

7 months ago
Just passed the AWS ML Specialty exam! Thanks Pass4Success for the spot-on practice questions. Saved me weeks of prep time!
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Kayleigh

7 months ago
With the assistance of Pass4Success practice questions, I was able to pass the Amazon AWS Certified Machine Learning - Specialty exam. The exam focused on Data Engineering and Exploratory Data Analysis. One question that stood out to me was related to performing feature engineering. Can you provide more information on this topic?
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Royal

8 months ago
My exam experience was successful as I passed the Amazon AWS Certified Machine Learning - Specialty exam using Pass4Success practice questions. The topics of Data Engineering and Exploratory Data Analysis were crucial for the exam. I remember a question that tested my knowledge on creating data repositories for ML. Can you elaborate on this topic further?
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Elza

9 months ago
Just passed the AWS ML Specialty exam! Be ready for questions on feature engineering and data preprocessing. Understanding how to handle missing data and create effective features is crucial. Big thanks to Pass4Success for their spot-on practice questions – they really helped me prep in a short time!
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Herman

9 months ago
I recently passed the AWS Certified Machine Learning - Specialty exam, thanks to Pass4Success for their relevant practice questions! A key topic was data preprocessing. Expect questions on handling missing values and feature scaling. Study different techniques like imputation and normalization. The exam also focused heavily on model selection and evaluation. Be prepared to interpret confusion matrices and ROC curves. Brush up on various performance metrics for different ML tasks. Finally, AWS-specific services were crucial. Know SageMaker's built-in algorithms and when to use each. Understanding deployment options and instance types is essential. Good luck to future exam takers!
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Glory

9 months ago
I passed the Amazon AWS Certified Machine Learning - Specialty exam with the help of Pass4Success practice questions. The exam covered topics like Data Engineering and Exploratory Data Analysis. One question that I was unsure of was related to identifying and implementing a data transformation solution. Can you provide more insights on this topic?
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Therese

9 months ago
Alex Johnson
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Free Amazon MLS-C01 Exam Actual Questions

Note: Premium Questions for MLS-C01 were last updated On Mar. 25, 2025 (see below)

Question #1

An online delivery company wants to choose the fastest courier for each delivery at the moment an order is placed. The company wants to implement this feature for existing users and new users of its application. Data scientists have trained separate models with XGBoost for this purpose, and the models are stored in Amazon S3. There is one model fof each city where the company operates.

The engineers are hosting these models in Amazon EC2 for responding to the web client requests, with one instance for each model, but the instances have only a 5% utilization in CPU and memory, ....operation engineers want to avoid managing unnecessary resources.

Which solution will enable the company to achieve its goal with the LEAST operational overhead?

Reveal Solution Hide Solution
Correct Answer: B

The best solution for this scenario is to use a multi-model endpoint in Amazon SageMaker, which allows hosting multiple models on the same endpoint and invoking them dynamically at runtime. This way, the company can reduce the operational overhead of managing multiple EC2 instances and model servers, and leverage the scalability, security, and performance of SageMaker hosting services. By using a multi-model endpoint, the company can also save on hosting costs by improving endpoint utilization and paying only for the models that are loaded in memory and the API calls that are made. To use a multi-model endpoint, the company needs to prepare a Docker container based on the open-source multi-model server, which is a framework-agnostic library that supports loading and serving multiple models from Amazon S3. The company can then create a multi-model endpoint in SageMaker, pointing to the S3 bucket containing all the models, and invoke the endpoint from the web client at runtime, specifying the TargetModel parameter according to the city of each request. This solution also enables the company to add or remove models from the S3 bucket without redeploying the endpoint, and to use different versions of the same model for different cities if needed.References:

Use Docker containers to build models

Host multiple models in one container behind one endpoint

Multi-model endpoints using Scikit Learn

Multi-model endpoints using XGBoost


Question #2

A music streaming company is building a pipeline to extract features. The company wants to store the features for offline model training and online inference. The company wants to track feature history and to give the company's data science teams access to the features.

Which solution will meet these requirements with the MOST operational efficiency?

Reveal Solution Hide Solution
Correct Answer: A

Amazon SageMaker Feature Store is a fully managed, purpose-built repository for storing, updating, and sharing machine learning features. It supports both online and offline stores for features, allowing real-time access for online inference and batch access for offline model training. It also tracks feature history, making it easier for data scientists to work with and access relevant feature sets.

This solution provides the necessary storage and access capabilities with high operational efficiency by managing feature history and enabling controlled access through IAM roles, making it a comprehensive choice for the company's requirements.


Question #3

A data scientist needs to create a model for predictive maintenance. The model will be based on historical data to identify rare anomalies in the data.

The historical data is stored in an Amazon S3 bucket. The data scientist needs to use Amazon SageMaker Data Wrangler to ingest the dat

a. The data scientists also needs to perform exploratory data analysis (EDA) to understand the statistical properties of the data.

Which solution will meet these requirements with the LEAST amount of compute resources?

Reveal Solution Hide Solution
Correct Answer: C

To perform efficient exploratory data analysis (EDA) on a large dataset for anomaly detection, using the First K option in SageMaker Data Wrangler is an optimal choice. This option allows the data scientist to select the first K rows, limiting the data loaded into memory, which conserves compute resources.

Given that the First K option allows the data scientist to determine K based on domain knowledge, this approach provides a representative sample without requiring extensive compute resources. Other options like randomized sampling may not provide data samples that are as useful for initial analysis in a time-series or sequential dataset context.


Question #4

An ecommerce company wants to train a large image classification model with 10.000 classes. The company runs multiple model training iterations and needs to minimize operational overhead and cost. The company also needs to avoid loss of work and model retraining.

Which solution will meet these requirements?

Reveal Solution Hide Solution
Correct Answer: D

Amazon SageMaker managed spot training allows for cost-effective training by utilizing Spot Instances, which are lower-cost EC2 instances that can be interrupted when demand is high. By enabling checkpointing in SageMaker, the company can save intermediate model states to Amazon S3, allowing training to resume from the last checkpoint if interrupted. This solution minimizes operational overhead by automating the checkpointing process and resuming work after interruptions, reducing the need for retraining from scratch.

This setup provides a reliable and cost-efficient approach to training large models with minimal operational overhead and risk of data loss.


Question #5

A company stores its documents in Amazon S3 with no predefined product categories. A data scientist needs to build a machine learning model to categorize the documents for all the company's products.

Which solution will meet these requirements with the MOST operational efficiency?

Reveal Solution Hide Solution
Correct Answer: C

Amazon SageMaker's Neural Topic Model (NTM) is designed to uncover underlying topics within text data by clustering documents based on topic similarity. For document categorization, NTM can identify product categories by analyzing and grouping the documents, making it an efficient choice for unsupervised learning where predefined categories do not exist.



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