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30 Free AWS MLS-C01 Practice Questions
Real practice questions for the AWS Machine Learning Specialty (MLS-C01) exam, with answers and detailed explanations. Updated 2026.
Free questions
30
Passing score
750 out of 1000
Exam time
180 minutes
Question pool
136+ Questions
Below are 30 real practice questions for the AWS Machine Learning Specialty (MLS-C01) exam. Each question shows the correct answer and a detailed explanation when you reveal it. Use these to benchmark your readiness — if you score below 70% on these 30 questions, plan for at least 4 more weeks of study before booking.
MLS-C01 Practice Questions
- FreePracticeQuiz.questionLabelModellingA 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. The model produces the following confusion matrix after evaluating on a test dataset of 100 customers. Based on the model evaluation results, why is this a viable model for production? AThe model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.BThe precision of the model is 86%, which is less than the accuracy of the model.CThe model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.DThe precision of the model is 86%, which is greater than the accuracy of the model.
- FreePracticeQuiz.questionLabelData EngineeringA Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users. What should the Specialist do to meet this objective?ABuild a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.BBuild a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.CBuild a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.DBuild a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.
- FreePracticeQuiz.questionLabelData EngineeringA Mobile Network Operator is building an analytics platform to analyze and optimize a company's operations using Amazon Athena and Amazon S3. The source systems send data in .CSV format in real time. The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3. Which solution takes the LEAST effort to implement?AIngest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as ParquetBIngest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.CIngest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.DIngest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.
- FreePracticeQuiz.questionLabelModellingA city wants to monitor its air quality to address the consequences of air pollution. A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city. As this is a prototype, only daily data from the last year is available. Which model is MOST likely to provide the best results in Amazon SageMaker?AUse the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_typeof regressor.BUse Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.CUse the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_typeof regressor.DUse the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_typeof classifier.
- FreePracticeQuiz.questionLabelData EngineeringA Data Engineer needs to build a model using a dataset containing customer credit card information. How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?AUse a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers..BUse an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.CUse an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker Principal Component Analysis (PCA) algorithm to reduce the length of the credit card numbers.DUse AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.
- FreePracticeQuiz.questionLabelML Implementation and OperationsA Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However, the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC. Why is the ML Specialist not seeing the instance visible in the VPC?AAmazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.BAmazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.CAmazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.DAmazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.
- FreePracticeQuiz.questionLabelML Implementation and OperationsA Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker. The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant. Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test?AReview SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon QuickSight to visualize logs as they are being produced.BGenerate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker.CBuild custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the log data as it is generated by Amazon SageMaker.DSend Amazon CloudWatch Logs that were generated by Amazon SageMaker to Amazon ES and use Kibana to query and visualize the log data.
- FreePracticeQuiz.questionLabelExploratory Data AnalysisA manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data. Which solution requires the LEAST effort to be able to query this data?AUse AWS Data Pipeline to transform the data and Amazon RDS to run queries.BUse AWS Glue to catalogue the data and Amazon Athena to run queries.CUse AWS Batch to run ETL on the data and Amazon Aurora to run the queries.DUse AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.
- FreePracticeQuiz.questionLabelML Implementation and OperationsA Machine Learning Specialist is developing a custom video recommendation model for an application. The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket. The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance. Which approach allows the Specialist to use all the data to train the model?ALoad a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.BLaunch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and train using the full datasetCUse AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.DLoad a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.
- FreePracticeQuiz.questionLabelData EngineeringA 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?AWrite a direct connection to the SQL database within the notebook and pull data in.BPush the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.CMove the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in.DMove the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.
- FreePracticeQuiz.questionLabelModellingA Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences, and trends to enhance the website-for better service and smart recommendations. Which solution should the Specialist recommend?ALatent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.BA neural network with a minimum of three layers and random initial weights to identify patterns in the customer database.CCollaborative filtering based on user interactions and correlations to identify patterns in the customer database.DRandom Cut Forest (RCF) over random subsamples to identify patterns in the customer database.
- FreePracticeQuiz.questionLabelModellingA Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist. Which machine learning model type should the Specialist use to accomplish this task?ALinear Regression.BClassification.CClustering.DReinforcement learning.
- FreePracticeQuiz.questionLabelModellingThe displayed graph is from a forecasting model for testing a time series. Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model? AThe model predicts both the trend and the seasonality well.BThe model predicts the trend well, but not the seasonality.CThe model predicts the seasonality well, but not the trend.DThe model does not predict the trend or the seasonality well.
- FreePracticeQuiz.questionLabelModellingA company wants to classify user behavior as either fraudulent or normal. Based on internal research, a machine learning specialist will build a binary classifier based on two features: age of account, denoted by x, and transaction month, denoted by y. The class distributions are illustrated in the provided figure. The positive class is portrayed in red, while the negative class is portrayed in black. Which model would have the HIGHEST accuracy? ALong short-term memory (LSTM) model with scaled exponential linear unit (SELU).BLogistic Regression.CSupport vector machine (SVM) with non-linear kernel.DSingle perceptron with tanh activation function.
- FreePracticeQuiz.questionLabelData EngineeringA Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (PII). The dataset: Must be accessible from a VPC only. Must not traverse the public internet. How can these requirements be satisfied?ACreate a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.BCreate a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.CCreate a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.DCreate a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance.
- FreePracticeQuiz.questionLabelModellingDuring mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates. What is the MOST likely cause of this issue?AThe class distribution in the dataset is imbalanced.BDataset shuffling is disabled.CThe batch size is too big.DThe learning rate is very high.
- FreePracticeQuiz.questionLabelML Implementation and OperationsAn employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis. What combination of services is the MOST efficient to accomplish the task?AAmazon Transcribe, Amazon Translate, and Amazon Comprehend.BAmazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seqCAmazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)DAmazon Transcribe, Amazon Translate and Amazon SageMaker BlazingText.
- FreePracticeQuiz.questionLabelML Implementation and OperationsA Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs. What does the Specialist need to do?ABundle the NVIDIA drivers with the Docker image.BBuild the Docker container to be NVIDIA-Docker compatible.COrganize the Docker container's file structure to execute on GPU instances.DSet the GPU flag in the Amazon SageMaker CreateTrainingJob request body.
- FreePracticeQuiz.questionLabelModellingA Machine Learning Specialist is building a Logistic Regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold. What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?AReceiver operating characteristic (ROC) curve.BMisclassification rate.CRoot Mean Square Error (RMSE).DL1 norm.
- FreePracticeQuiz.questionLabelModellingAn interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget. What should the Specialist do to meet these requirements?ACreate one-hot word encoding vectors.BProduce a set of synonyms for every word using Amazon Mechanical Turk.CCreate word embedding vectors that store edit distance with every other word.DDownload word embeddings pre-trained on a large corpus.
- FreePracticeQuiz.questionLabelML Implementation and OperationsA Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked. Which services are integrated with Amazon SageMaker to track this information? (Choose two.)AAWS CloudTrail.BAWS Health.CAWS Trusted Advisor.DAmazon CloudWatch.EAWS Config.
- FreePracticeQuiz.questionLabelData EngineeringA retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose. To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined. The model needs to be retrained daily. Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?ARequire that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3, then use AWS Glue to do the transformation.BDeploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3.CSpin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.DInsert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehose stream that transforms raw record attributes into simple transformed values using SQL.
- FreePracticeQuiz.questionLabelModellingA Machine Learning Specialist is building a Convolutional Neural Network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes. The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes. Which function will produce the desired output?ADropout.BSmooth L1 loss.CSoftmax.DRectified linear units (ReLU).
- FreePracticeQuiz.questionLabelExploratory Data AnalysisA Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target. What option can the Specialist use to determine whether it is overestimating or underestimating the target value?ARoot Mean Square Error (RMSE).BResidual plots.CArea under the curve.DConfusion matrix.
- FreePracticeQuiz.questionLabelModellingA company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided. Based on this information, which model would have the HIGHEST recall with respect to the fraudulent class? ADecision tree.BLinear support vector machine (SVM).CNaive Bayesian classifier.DSingle Perceptron with sigmoidal activation function.
- FreePracticeQuiz.questionLabelML Implementation and OperationsA Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric. This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours. With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s). Which visualization will accomplish this?AA histogram showing whether the most important input feature is Gaussian.BA scatter plot with points colored by target variable that uses t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the large number of input variables in an easier-to-read dimension.CA scatter plot showing the performance of the objective metric over each training iteration.DA scatter plot showing the correlation between maximum tree depth and the objective metric.
- FreePracticeQuiz.questionLabelData EngineeringA Machine Learning Specialist is creating a new natural language processing application that processes a dataset comprised of 1 million sentences. The aim is to then run Word2Vec to generate embeddings of the sentences and enable different types of predictions. Here is an example from the dataset: 'The quck BROWN FOX jumps over the lazy dog.' Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a repeatable manner? (Choose three.)APerform part-of-speech tagging and keep the action verb and the nouns only.BNormalize all words by making the sentence lowercase.CRemove stop words using an English stopword dictionary.DCorrect the typography on 'quck' to 'quick'.EOne-hot encode all words in the sentence.FTokenize the sentence into words.
- FreePracticeQuiz.questionLabelML Implementation and OperationsA company is using Amazon Polly to translate plaintext documents to speech for automated company announcements. However, company acronyms are being mispronounced in the current documents. How should a Machine Learning Specialist address this issue for future documents?AConvert current documents to SSML with pronunciation tags.BCreate an appropriate pronunciation lexicon.COutput speech marks to guide in pronunciation.DUse Amazon Lex to preprocess the text files for pronunciation.
- FreePracticeQuiz.questionLabelModellingAn insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted. The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models. During the model evaluation, the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images. Which of the following should be used to resolve this issue? (Choose two.)AAdd vanishing gradient to the model.BPerform data augmentation on the training data.CMake the neural network architecture complex.DUse gradient checking in the model.EAdd L2 regularization to the model.
- FreePracticeQuiz.questionLabelML Implementation and OperationsWhen submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Choose three.)AThe training channel identifying the location of training data on an Amazon S3 bucket.BThe validation channel identifying the location of validation data on an Amazon S3 bucket.CThe IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users.DHyperparameters in a JSON array as documented for the algorithm used.EThe Amazon EC2 instance class specifying whether training will be run using CPU or GPFThe output path specifying where on an Amazon S3 bucket the trained model will persist.
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