Amazon MLS-C01 Practice Exams
Last updated on Oct 22,2026- Exam Code: MLS-C01
- Exam Name: AWS Certified Machine Learning - Specialty
- Certification Provider: Amazon
- Latest update: Oct 22,2026
A Machine Learning Specialist is developing recommendation engine for a photography blog Given a picture, the recommendation engine should show a picture that captures similar objects. The Specialist would like to create a numerical representation feature to perform nearest-neighbor searches
What actions would allow the Specialist to get relevant numerical representations?
- A . Reduce image resolution and use reduced resolution pixel values as features
- B . Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels
- C . Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer
- D . Average colors by channel to obtain three-dimensional representations of images.
C
Explanation:
A neural network pre-trained on ImageNet is a deep learning model that has been trained on a large dataset of images containing 1000 classes of objects. The model can learn to extract high-level features from the images that capture the semantic and visual information of the objects. The penultimate layer of the model is the layer before the final output layer, and it contains a feature vector that represents the input image in a lower-dimensional space. By running images through a pre-trained neural network and collecting the feature vectors from the penultimate layer, the Specialist can obtain relevant numerical representations that can be used for nearest-neighbor searches. The feature vectors can capture the similarity between images based on the presence and appearance of similar objects, and they can be compared using distance metrics such as Euclidean distance or cosine similarity. This approach can enable the recommendation engine to show a picture that captures similar objects to a given picture.
Reference: ImageNet – Wikipedia
How to use a pre-trained neural network to extract features from images | by Rishabh Anand | Analytics Vidhya | Medium
Image Similarity using Deep Ranking | by Aditya Oke | Towards Data Science
A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?
- A . Poisson distribution,
- B . Uniform distribution
- C . Normal distribution
- D . Binomial distribution
A
Explanation:
The prior probability distribution for the discrete random variable that represents the number of minutes New Yorkers wait for a bus is a Poisson distribution. A Poisson distribution is suitable for modeling the number of events that occur in a fixed interval of time or space, given a known average rate of occurrence. In this case, the event is waiting for a bus, the interval is 10 minutes, and the average rate is 3 minutes. The Poisson distribution can capture the variability of the waiting time, which can range from 0 to 10 minutes, with different probabilities.
References:
1: Poisson Distribution – Amazon SageMaker
2: Poisson Distribution – Wikipedia
A Machine Learning Specialist deployed a model that provides product recommendations on a company’s website Initially, the model was performing very well and resulted in customers buying more products on average However within the past few months the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less. The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago
Which method should the Specialist try to improve model performance?
- A . The model needs to be completely re-engineered because it is unable to handle product inventory changes
- B . The model’s hyperparameters should be periodically updated to prevent drift
- C . The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
- D . The model should be periodically retrained using the original training data plus new data as product inventory changes
D
Explanation:
The problem that the Machine Learning Specialist is facing is likely due to concept drift, which is a phenomenon where the statistical properties of the target variable change over time, making the model less accurate and relevant. Concept drift can occur due to various reasons, such as changes in customer preferences, market trends, product inventory, seasonality, etc. In this case, the product recommendations model may have become outdated as the product inventory changed over time, making the recommendations less appealing to the customers. To address this issue, the model should be periodically retrained using the original training data plus new data as product inventory changes. This way, the model can learn from the latest data and adapt to the changing customer behavior and preferences. Retraining the model from scratch using the original data while adding a regularization term may not be sufficient, as it does not account for the new data. Updating the model’s hyperparameters may not help either, as it does not address the underlying data distribution change. Re-engineering the model completely may not be necessary, as the model may still be valid and useful with periodic retraining.
Reference: Concept Drift – Amazon SageMaker
Detecting and Handling Concept Drift – Amazon SageMaker Machine Learning Concepts – Amazon Machine Learning
A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability where malicious code running on the instances could compromise data privacy. The company mandates that all instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS network.
How should the Data Science team configure the notebook instance placement to meet these requirements?
- A . Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.
- B . Associate the Amazon SageMaker notebook with a private subnet in a VPC. Use 1AM policies to grant access to Amazon S3 and Amazon SageMaker.
- C . Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.
- D . Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker
C
Explanation:
To configure the notebook instance placement to meet the requirements, the Data Science team should associate the Amazon SageMaker notebook with a private subnet in a VPC. A VPC is a virtual network that is logically isolated from other networks in AWS. A private subnet is a subnet that has no internet gateway attached to it, and therefore cannot communicate with the internet. By placing the notebook instance in a private subnet, the team can ensure that it stays within a secured VPC with no internet access.
However, to access data stored in Amazon S3 buckets and other AWS services, the team needs to ensure that the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it. A VPC endpoint is a gateway that enables private connections between the VPC and supported AWS services. A VPC endpoint does not require an internet gateway, a NAT device, or a VPN connection, and ensures that the traffic between the VPC and the AWS service does not leave the AWS network. By using VPC endpoints, the team can access Amazon S3 and Amazon SageMaker from the notebook instance without compromising data privacy or security.
References:
: What Is Amazon VPC? – Amazon Virtual Private Cloud
: Subnet Routing – Amazon Virtual Private Cloud
: VPC Endpoints – Amazon Virtual Private Cloud
A Machine Learning Specialist has completed a proof of concept for a company using a small data sample and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker. The historical training data is stored in Amazon RDS
Which approach should the Specialist use for training a model using that data?
- A . Write a direct connection to the SQL database within the notebook and pull data in
- B . Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
- C . Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook
to pull data in - D . Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.
B
Explanation:
Pushing the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and providing the S3 location within the notebook is the best approach for training a model using the data stored in Amazon RDS. This is because Amazon SageMaker can directly access data from Amazon S3 and train models on it. AWS Data Pipeline is a service that can automate the movement and transformation of data between different AWS services. It can also use Amazon RDS as a data source and Amazon S3 as a data destination. This way, the data can be transferred efficiently and securely without writing any code within the notebook.
References:
Amazon SageMaker
AWS Data Pipeline
A Machine Learning Specialist is applying a linear least squares regression model to a dataset with 1 000 records and 50 features Prior to training, the ML Specialist notices that two features are perfectly linearly dependent
Why could this be an issue for the linear least squares regression model?
- A . It could cause the backpropagation algorithm to fail during training
- B . It could create a singular matrix during optimization which fails to define a unique solution
- C . It could modify the loss function during optimization causing it to fail during training
- D . It could introduce non-linear dependencies within the data which could invalidate the linear assumptions of the model
B
Explanation:
Linear least squares regression is a method of fitting a linear model to a set of data by minimizing the sum of squared errors between the observed and predicted values. The solution of the linear least squares problem can be obtained by solving the normal equations, which are given by ATAx=ATb,
where A is the matrix of explanatory variables, b is the vector of response variables, and x is the vector of unknown coefficients.
However, if the matrix A has two features that are perfectly linearly dependent, then the matrix ATA will be singular, meaning that it does not have a unique inverse. This implies that the normal equations do not have a unique solution, and the linear least squares problem is ill-posed. In other words, there are infinitely many values of x that can satisfy the normal equations, and the linear model is not identifiable.
This can be an issue for the linear least squares regression model, as it can lead to instability, inconsistency, and poor generalization of the model. It can also cause numerical difficulties when trying to solve the normal equations using computational methods, such as matrix inversion or decomposition. Therefore, it is advisable to avoid or remove the linearly dependent features from the matrix A before applying the linear least squares regression model.
References:
Linear least squares (mathematics)
Linear Regression in Matrix Form
Singular Matrix Problem
A machine learning (ML) specialist wants to create a data preparation job that uses a PySpark script with complex window aggregation operations to create data for training and testing. The ML specialist needs to evaluate the impact of the number of features and the sample count on model performance.
Which approach should the ML specialist use to determine the ideal data transformations for the model?
- A . Add an Amazon SageMaker Debugger hook to the script to capture key metrics. Run the script as an AWS Glue job.
- B . Add an Amazon SageMaker Experiments tracker to the script to capture key metrics. Run the script as an AWS Glue job.
- C . Add an Amazon SageMaker Debugger hook to the script to capture key parameters. Run the script as a SageMaker processing job.
- D . Add an Amazon SageMaker Experiments tracker to the script to capture key parameters. Run the script as a SageMaker processing job.
D
Explanation:
Amazon SageMaker Experiments is a service that helps track, compare, and evaluate different iterations of ML models. It can be used to capture key parameters such as the number of features and the sample count from a PySpark script that runs as a SageMaker processing job. A SageMaker processing job is a flexible and scalable way to run data processing workloads on AWS, such as feature engineering, data validation, model evaluation, and model interpretation.
References:
Amazon SageMaker Experiments
Process Data and Evaluate Models
An insurance company is creating an application to automate car insurance claims. A machine learning (ML) specialist used an Amazon SageMaker Object Detection – TensorFlow built-in algorithm to train a model to detect scratches and dents in images of cars. After the model was trained, the ML specialist noticed that the model performed better on the training dataset than on the testing dataset.
Which approach should the ML specialist use to improve the performance of the model on the testing data?
- A . Increase the value of the momentum hyperparameter.
- B . Reduce the value of the dropout_rate hyperparameter.
- C . Reduce the value of the learning_rate hyperparameter.
- D . Increase the value of the L2 hyperparameter.
D
Explanation:
The machine learning model in this scenario shows signs of overfitting, as evidenced by better performance on the training dataset than on the testing dataset. Overfitting indicates that the model is capturing noise or details specific to the training data rather than general patterns.
One common approach to reduce overfitting is L2 regularization, which adds a penalty to the loss function for large weights and helps the model generalize better by smoothing out the weight distribution. By increasing the value of the L2 hyperparameter, the ML specialist can increase this penalty, helping to mitigate overfitting and improve performance on the testing dataset.
Options like increasing momentum or reducing dropout are less effective for addressing overfitting in this context.
A company wants to detect credit card fraud. The company has observed that an average of 2% of credit card transactions are fraudulent. A data scientist trains a classifier on a year’s worth of credit card transaction data. The classifier needs to identify the fraudulent transactions. The company wants to accurately capture as many fraudulent transactions as possible.
Which metrics should the data scientist use to optimize the classifier? (Select TWO.)
- A . Specificity
- B . False positive rate
- C . Accuracy
- D . Fl score
- E . True positive rate
D, E
Explanation:
The F1 score is a measure of the harmonic mean of precision and recall, which are both important for fraud detection. Precision is the ratio of true positives to all predicted positives, and recall is the ratio of true positives to all actual positives. A high F1 score indicates that the classifier can correctly identify fraudulent transactions and avoid false negatives. The true positive rate is another name for recall, and it measures the proportion of fraudulent transactions that are correctly detected by the classifier. A high true positive rate means that the classifier can capture as many fraudulent transactions as possible.
References:
Fraud Detection Using Machine Learning | Implementations | AWS Solutions
Detect fraudulent transactions using machine learning with Amazon SageMaker | AWS Machine Learning Blog
A company uses sensors on devices such as motor engines and factory machines to measure parameters, temperature and pressure. The company wants to use the sensor data to predict equipment malfunctions and reduce services outages.
The Machine learning (ML) specialist needs to gather the sensors data to train a model to predict device malfunctions. The ML spoctafst must ensure that the data does not contain outliers before training the ..el.
What can the ML specialist meet these requirements with the LEAST operational overhead?
- A . Load the data into an Amazon SagcMaker Studio notebook. Calculate the first and third quartile Use a SageMaker Data Wrangler data (low to remove only values that are outside of those quartiles.
- B . Use an Amazon SageMaker Data Wrangler bias report to find outliers in the dataset Use a Data Wrangler data flow to remove outliers based on the bias report.
- C . Use an Amazon SageMaker Data Wrangler anomaly detection visualization to find outliers in the dataset. Add a transformation to a Data Wrangler data flow to remove outliers.
- D . Use Amazon Lookout for Equipment to find and remove outliers from the dataset.
C
Explanation:
Amazon SageMaker Data Wrangler is a tool that helps data scientists and ML developers to prepare data for ML. One of the features of Data Wrangler is the anomaly detection visualization, which uses an unsupervised ML algorithm to identify outliers in the dataset based on statistical properties. The ML specialist can use this feature to quickly explore the sensor data and find any anomalous values that may affect the model performance. The ML specialist can then add a transformation to a Data Wrangler data flow to remove the outliers from the dataset. The data flow can be exported as a script or a pipeline to automate the data preparation process. This option requires the least operational overhead compared to the other options.
References:
Amazon SageMaker Data Wrangler – Amazon Web Services (AWS) Anomaly Detection Visualization – Amazon SageMaker Transform Data – Amazon SageMaker