AWS Certified Machine Learning - Specialty Dumps July 2026
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330 questions with answers
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Amazon MLS-C01 Sample Questions
Question # 11
A company builds computer-vision models that use deep learning for the autonomous vehicle industry. A machine learning (ML) specialist uses an Amazon EC2 instance that has a CPU: GPU ratio of 12:1 to train the models. The ML specialist examines the instance metric logs and notices that the GPU is idle half of the time The ML specialist must reduce training costs without increasing the duration of the training jobs. Which solution will meet these requirements?
A. Switch to an instance type that has only CPUs. B. Use a heterogeneous cluster that has two different instances groups. C. Use memory-optimized EC2 Spot Instances for the training jobs. D. Switch to an instance type that has a CPU GPU ratio of 6:1.
Answer: D Explanation: Switching to an instance type that has a CPU: GPU ratio of 6:1 will reducethe training costs by using fewer CPUs and GPUs, while maintaining the same level ofperformance. The GPU idle time indicates that the CPU is not able to feed the GPU withenough data, so reducing the CPU: GPU ratio will balance the workload and improve theGPU utilization. A lower CPU: GPU ratio also means less overhead for inter-processcommunication and synchronization between the CPU and GPU processes. References:Optimizing GPU utilization for AI/ML workloads on Amazon EC2Analyze CPU vs. GPU Performance for AWS Machine Learning
Question # 12
An engraving company wants to automate its quality control process for plaques. The company performs the process before mailing each customized plaque to a customer. The company has created an Amazon S3 bucket that contains images of defects that should cause a plaque to be rejected. Low-confidence predictions must be sent to an internal team of reviewers who are using Amazon Augmented Al (Amazon A2I). Which solution will meet these requirements?
A. Use Amazon Textract for automatic processing. Use Amazon A2I with AmazonMechanical Turk for manual review. B. Use Amazon Rekognition for automatic processing. Use Amazon A2I with a privateworkforce option for manual review. C. Use Amazon Transcribe for automatic processing. Use Amazon A2I with a privateworkforce option for manual review. D. Use AWS Panorama for automatic processing Use Amazon A2I with AmazonMechanical Turk for manual review
Answer: B Explanation: Amazon Rekognition is a service that provides computer vision capabilities for image and video analysis, such as object, scene, and activity detection, face and textrecognition, and custom label detection. Amazon Rekognition can be used to automate thequality control process for plaques by comparing the images of the plaques with the imagesof defects in the Amazon S3 bucket and returning a confidence score for each defect.Amazon A2I is a service that enables human review of machine learning predictions, suchas low-confidence predictions from Amazon Rekognition. Amazon A2I can be integratedwith a private workforce option, which allows the engraving company to use its own internalteam of reviewers to manually inspect the plaques that are flagged by AmazonRekognition. This solution meets the requirements of automating the quality controlprocess, sending low-confidence predictions to an internal team of reviewers, and using Amazon A2I for manual review. References:1: Amazon Rekognition documentation2: Amazon A2I documentation3: Amazon Rekognition Custom Labels documentation4: Amazon A2I Private Workforce documentation
Question # 13
An Amazon SageMaker notebook instance is launched into Amazon VPC The SageMaker notebook references data contained in an Amazon S3 bucket in another account The bucket is encrypted using SSE-KMS The instance returns an access denied error when trying to access data in Amazon S3. Which of the following are required to access the bucket and avoid the access denied error? (Select THREE)
A. An AWS KMS key policy that allows access to the customer master key (CMK) B. A SageMaker notebook security group that allows access to Amazon S3 C. An 1AM role that allows access to the specific S3 bucket D. A permissive S3 bucket policy E. An S3 bucket owner that matches the notebook owner F. A SegaMaker notebook subnet ACL that allow traffic to Amazon S3.
Answer: A,B,C Explanation: To access an Amazon S3 bucket in another account that is encrypted using SSE-KMS, the following are required:A. An AWS KMS key policy that allows access to the customer master key (CMK).The CMK is the encryption key that is used to encrypt and decrypt the data in theS3 bucket. The KMS key policy defines who can use and manage the CMK. Toallow access to the CMK from another account, the key policy must include astatement that grants the necessary permissions (such as kms:Decrypt) to theprincipal from the other account (such as the SageMaker notebook IAM role).B. A SageMaker notebook security group that allows access to Amazon S3. Asecurity group is a virtual firewall that controls the inbound and outbound traffic forthe SageMaker notebook instance. To allow the notebook instance to access theS3 bucket, the security group must have a rule that allows outbound traffic to theS3 endpoint on port 443 (HTTPS).C. An IAM role that allows access to the specific S3 bucket. An IAM role is anidentity that can be assumed by the SageMaker notebook instance to access AWSresources. The IAM role must have a policy that grants the necessary permissions(such as s3:GetObject) to access the specific S3 bucket. The policy must alsoinclude a condition that allows access to the CMK in the other account.The following are not required or correct:D. A permissive S3 bucket policy. A bucket policy is a resource-based policy thatdefines who can access the S3 bucket and what actions they can perform. Apermissive bucket policy is not required and not recommended, as it can exposethe bucket to unauthorized access. A bucket policy should follow the principle ofleast privilege and grant the minimum permissions necessary to the specificprincipals that need access.E. An S3 bucket owner that matches the notebook owner. The S3 bucket ownerand the notebook owner do not need to match, as long as the bucket owner grantscross-account access to the notebook owner through the KMS key policy and thebucket policy (if applicable).F. A SegaMaker notebook subnet ACL that allow traffic to Amazon S3. A subnetACL is a network access control list that acts as an optional layer of security forthe SageMaker notebook instance’s subnet. A subnet ACL is not required toaccess the S3 bucket, as the security group is sufficient to control the traffic.However, if a subnet ACL is used, it must not block the traffic to the S3 endpoint.
Question # 14
A machine learning (ML) engineer has created a feature repository in Amazon SageMaker Feature Store for the company. The company has AWS accounts for development, integration, and production. The company hosts a feature store in the development account. The company uses Amazon S3 buckets to store feature values offline. The company wants to share features and to allow the integration account and the production account to reuse the features that are in the feature repository. Which combination of steps will meet these requirements? (Select TWO.)
A. Create an IAM role in the development account that the integration account andproduction account can assume. Attach IAM policies to the role that allow access to thefeature repository and the S3 buckets. B. Share the feature repository that is associated the S3 buckets from the developmentaccount to the integration account and the production account by using AWS ResourceAccess Manager (AWS RAM). C. Use AWS Security Token Service (AWS STS) from the integration account and theproduction account to retrieve credentials for the development account. D. Set up S3 replication between the development S3 buckets and the integration andproduction S3 buckets. E. Create an AWS PrivateLink endpoint in the development account for SageMaker.
Answer: A,B Explanation: The combination of steps that will meet the requirements are to create an IAM role in thedevelopment account that the integration account and production account can assume,attach IAM policies to the role that allow access to the feature repository and the S3buckets, and share the feature repository that is associated with the S3 buckets from thedevelopment account to the integration account and the production account by using AWSResource Access Manager (AWS RAM). This approach will enable cross-account accessand sharing of the features stored in Amazon SageMaker Feature Store and Amazon S3.Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store,update, search, and share curated data used in training and prediction workflows. Theservice provides feature management capabilities such as enabling easy feature reuse, lowlatency serving, time travel, and ensuring consistency between features used in trainingand inference workflows. A feature group is a logical grouping of ML features whoseorganization and structure is defined by a feature group schema. A feature group schemaconsists of a list of feature definitions, each of which specifies the name, type, andmetadata of a feature. Amazon SageMaker Feature Store stores the features in both anonline store and an offline store. The online store is a low-latency, high-throughput storethat is optimized for real-time inference. The offline store is a historical store that is backedby an Amazon S3 bucket and is optimized for batch processing and model training1.AWS Identity and Access Management (IAM) is a web service that helps you securelycontrol access to AWS resources for your users. You use IAM to control who can use yourAWS resources (authentication) and what resources they can use and in what ways(authorization). An IAM role is an IAM identity that you can create in your account that hasspecific permissions. You can use an IAM role to delegate access to users, applications, orservices that don’t normally have access to your AWS resources. For example, you can create an IAM role in your development account that allows the integration account and theproduction account to assume the role and access the resources in the developmentaccount. You can attach IAM policies to the role that specify the permissions for the featurerepository and the S3 buckets. You can also use IAM conditions to restrict the accessbased on the source account, IP address, or other factors2.AWS Resource Access Manager (AWS RAM) is a service that enables you to easily andsecurely share AWS resources with any AWS account or within your AWS Organization.You can share AWS resources that you own with other accounts using resource shares. Aresource share is an entity that defines the resources that you want to share, and theprincipals that you want to share with. For example, you can share the feature repositorythat is associated with the S3 buckets from the development account to the integrationaccount and the production account by creating a resource share in AWS RAM. You canspecify the feature group ARN and the S3 bucket ARN as the resources, and theintegration account ID and the production account ID as the principals. You can also useIAM policies to further control the access to the shared resources3.The other options are either incorrect or unnecessary. Using AWS Security Token Service(AWS STS) from the integration account and the production account to retrieve credentialsfor the development account is not required, as the IAM role in the development accountcan provide temporary security credentials for the cross-account access. Setting up S3replication between the development S3 buckets and the integration and production S3buckets would introduce redundancy and inconsistency, as the S3 buckets are alreadyshared through AWS RAM. Creating an AWS PrivateLink endpoint in the developmentaccount for SageMaker is not relevant, as it is used to securely connect to SageMakerservices from a VPC, not from another account.References:1: Amazon SageMaker Feature Store – Amazon Web Services2: What Is IAM? - AWS Identity and Access Management3: What Is AWS Resource Access Manager? - AWS Resource Access Manager
Question # 15
A network security vendor needs to ingest telemetry data from thousands of endpoints that run all over the world. The data is transmitted every 30 seconds in the form of records that contain 50 fields. Each record is up to 1 KB in size. The security vendor uses Amazon Kinesis Data Streams to ingest the data. The vendor requires hourly summaries of the records that Kinesis Data Streams ingests. The vendor will use Amazon Athena to query the records and to generate the summaries. The Athena queries will target 7 to 12 of the available data fields. Which solution will meet these requirements with the LEAST amount of customization to transform and store the ingested data?
A. Use AWS Lambda to read and aggregate the data hourly. Transform the data and storeit in Amazon S3 by using Amazon Kinesis Data Firehose. B. Use Amazon Kinesis Data Firehose to read and aggregate the data hourly. Transformthe data and store it in Amazon S3 by using a short-lived Amazon EMR cluster. C. Use Amazon Kinesis Data Analytics to read and aggregate the data hourly. Transformthe data and store it in Amazon S3 by using Amazon Kinesis Data Firehose. D. Use Amazon Kinesis Data Firehose to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using AWS Lambda.
Answer: C Explanation: The solution that will meet the requirements with the least amount ofcustomization to transform and store the ingested data is to use Amazon Kinesis DataAnalytics to read and aggregate the data hourly, transform the data and store it in AmazonS3 by using Amazon Kinesis Data Firehose. This solution leverages the built-in features ofKinesis Data Analytics to perform SQL queries on streaming data and generate hourlysummaries. Kinesis Data Analytics can also output the transformed data to Kinesis DataFirehose, which can then deliver the data to S3 in a specified format and partitioningscheme. This solution does not require any custom code or additional infrastructure toprocess the data. The other solutions either require more customization (such as usingLambda or EMR) or do not meet the requirement of aggregating the data hourly (such asusing Lambda to read the data from Kinesis Data Streams). References:1: Boosting Resiliency with an ML-based Telemetry Analytics Architecture | AWSArchitecture Blog2: AWS Cloud Data Ingestion Patterns and Practices3: IoT ingestion and Machine Learning analytics pipeline with AWS IoT …4: AWS IoT Data Ingestion Simplified 101: The Complete Guide - Hevo Data
Question # 16
A data scientist is building a linear regression model. The scientist inspects the dataset and notices that the mode of the distribution is lower than the median, and the median is lower than the mean. Which data transformation will give the data scientist the ability to apply a linear regression model?
A. Exponential transformation B. Logarithmic transformation C. Polynomial transformation D. Sinusoidal transformation
Answer: B Explanation: A logarithmic transformation is a suitable data transformation for a linearregression model when the data has a skewed distribution, such as when the mode islower than the median and the median is lower than the mean. A logarithmic transformationcan reduce the skewness and make the data more symmetric and normally distributed,which are desirable properties for linear regression. A logarithmic transformation can alsoreduce the effect of outliers and heteroscedasticity (unequal variance) in the data. Anexponential transformation would have the opposite effect of increasing the skewness andmaking the data more asymmetric. A polynomial transformation may not be able to capturethe nonlinearity in the data and may introduce multicollinearity among the transformedvariables. A sinusoidal transformation is not appropriate for data that does not have aperiodic pattern.References:Data Transformation - Scaler TopicsLinear Regression - GeeksforGeeksLinear Regression - Scribbr
Question # 17
A car company is developing a machine learning solution to detect whether a car is present in an image. The image dataset consists of one million images. Each image in the dataset is 200 pixels in height by 200 pixels in width. Each image is labeled as either having a car or not having a car. Which architecture is MOST likely to produce a model that detects whether a car is present in an image with the highest accuracy?
A. Use a deep convolutional neural network (CNN) classifier with the images as input.Include a linear output layer that outputs the probability that an image contains a car. B. Use a deep convolutional neural network (CNN) classifier with the images as input.Include a softmax output layer that outputs the probability that an image contains a car. C. Use a deep multilayer perceptron (MLP) classifier with the images as input. Include alinear output layer that outputs the probability that an image contains a car. D. Use a deep multilayer perceptron (MLP) classifier with the images as input. Include asoftmax output layer that outputs the probability that an image contains a car.
Answer: A Explanation: A deep convolutional neural network (CNN) classifier is a suitable architecture for image classification tasks, as it can learn features from the images andreduce the dimensionality of the input. A linear output layer that outputs the probability thatan image contains a car is appropriate for a binary classification problem, as it can producea single scalar value between 0 and 1. A softmax output layer is more suitable for a multiclassclassification problem, as it can produce a vector of probabilities that sum up to 1. Adeep multilayer perceptron (MLP) classifier is not as effective as a CNN for imageclassification, as it does not exploit the spatial structure of the images and requires a largenumber of parameters to process the high-dimensional input. References:AWS Certified Machine Learning - Specialty Exam GuideAWS Training - Machine Learning on AWSAWS Whitepaper - An Overview of Machine Learning on AWS
Question # 18
A university wants to develop a targeted recruitment strategy to increase new student enrollment. A data scientist gathers information about the academic performance history of students. The data scientist wants to use the data to build student profiles. The university will use the profiles to direct resources to recruit students who are likely to enroll in the university. Which combination of steps should the data scientist take to predict whether a particular student applicant is likely to enroll in the university? (Select TWO)
A. Use Amazon SageMaker Ground Truth to sort the data into two groups named"enrolled" or "not enrolled." B. Use a forecasting algorithm to run predictions. C. Use a regression algorithm to run predictions. D. Use a classification algorithm to run predictions E. Use the built-in Amazon SageMaker k-means algorithm to cluster the data into twogroups named "enrolled" or "not enrolled."
Answer: A,D Explanation: The data scientist should use Amazon SageMaker Ground Truth to sort the data into two groups named “enrolled” or “not enrolled.” This will create a labeled datasetthat can be used for supervised learning. The data scientist should then use a classificationalgorithm to run predictions on the test data. A classification algorithm is a suitable choicefor predicting a binary outcome, such as enrollment status, based on the input features,such as academic performance. A classification algorithm will output a probability for eachclass label and assign the most likely label to each observation.References:Use Amazon SageMaker Ground Truth to Label DataClassification Algorithm in Machine Learning
Question # 19
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 one model can serve user requests at a time. The company must measure the performance of the new experimental model without affecting the current live traffic Which solution will meet these requirements?
A. A/B testing B. Canary release C. Shadow deployment D. Blue/green deployment
Answer: C Explanation: The best solution for this scenario is to use shadow deployment, which is a technique that allows the company to run the new experimental model in parallel with theexisting model, without exposing it to the end users. In shadow deployment, the companycan route the same user requests to both models, but only return the responses from theexisting model to the users. The responses from the new experimental model are loggedand analyzed for quality and performance metrics, such as accuracy, latency, and resourceconsumption12. This way, the company can validate the new experimental model in aproduction environment, without affecting the current live traffic or user experience.The other solutions are not suitable, because they have the following drawbacks:A: A/B testing is a technique that involves splitting the user traffic between two ormore models, and comparing their outcomes based on predefinedmetrics. However, this technique exposes the new experimental model to a portionof the end users, which might affect their experience if the model is not reliable orconsistent with the existing model3.B: Canary release is a technique that involves gradually rolling out the newexperimental model to a small subset of users, and monitoring its performance andfeedback. However, this technique also exposes the new experimental model tosome end users, and requires careful selection and segmentation of the usergroups4.D: Blue/green deployment is a technique that involves switching the user trafficfrom the existing model (blue) to the new experimental model (green) at once,after testing and verifying the new model in a separate environment. However, thistechnique does not allow the company to validate the new experimental model in aproduction environment, and might cause service disruption or inconsistency if thenew model is not compatible or stable5.References:1: Shadow Deployment: A Safe Way to Test in Production | LaunchDarkly Blog2: Shadow Deployment: A Safe Way to Test in Production | LaunchDarkly Blog3: A/B Testing for Machine Learning Models | AWS Machine Learning Blog4: Canary Releases for Machine Learning Models | AWS Machine Learning Blog5: Blue-Green Deployments for Machine Learning Models | AWS MachineLearning Blog
Question # 20
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
Answer: 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 allpredicted positives, and recall is the ratio of true positives to all actual positives. A high F1score indicates that the classifier can correctly identify fraudulent transactions and avoidfalse negatives. The true positive rate is another name for recall, and it measures theproportion of fraudulent transactions that are correctly detected by the classifier. A high truepositive rate means that the classifier can capture as many fraudulent transactions aspossible.References:Fraud Detection Using Machine Learning | Implementations | AWS SolutionsDetect fraudulent transactions using machine learning with Amazon SageMaker |AWS Machine Learning Blog1. Introduction — Reproducible Machine Learning for Credit Card Fraud Detection
