MLA-C01 Exam Questions
222 real MLA-C01 exam questions with expert-verified answers and explanations. Page 3 of 5.
- Question #107ML Model Development
A company needs to develop an ML model. The model must identify an item in an image and must provide the location of the item. Which Amazon SageMaker algorithm will meet these requ...
Object detectionComputer visionAmazon SageMaker algorithms - Question #108Data Preparation for Machine Learning
A company has an Amazon S3 bucket that contains 1 ТВ of files from different sources. The S3 bucket contains the following file types in the same S3 folder: CSV, JSON, XLSX, and Ap...
AWS Glue DataBrewData preparationS3 data organizationMixed file types - Question #109ML Model Development
A manufacturing company uses an ML model to determine whether products meet a standard for quality. The model produces an output of "Passed" or "Failed." Robots separate the produc...
ML model evaluationClassification metricsBinary classification - Question #110ML Solution Monitoring, Maintenance, and Security
An ML engineer needs to encrypt all data in transit when an ML training job runs. The ML engineer must ensure that encryption in transit is applied to processes that Amazon SageMak...
SageMaker SecurityEncryption in TransitML Training JobsDistributed Training - Question #111ML Model Development
An ML engineer needs to use metrics to assess the quality of a time-series forecasting model. Which metrics apply to this model? (Choose two.)
Time series forecastingEvaluation metricsRegression metricsQuantile loss - Question #112Deployment and Orchestration of ML Workflows
A company runs Amazon SageMaker ML models that use accelerated instances. The models require real-time responses. Each model has different scaling requirements. The company must no...
SageMaker Inference ComponentsReal-time InferenceNo Cold StartML Model Deployment - Question #113ML Solution Monitoring, Maintenance, and Security
A company uses Amazon SageMaker for its ML process. A compliance audit discovers that an Amazon S3 bucket for training data uses server-side encryption with S3 managed keys (SSE- S...
IAMKMSS3 encryptionSageMaker permissions - Question #114ML Model Development
A data scientist is evaluating different binary classification models. A false positive result is 5 times more expensive (from a business perspective) than a false negative result....
Model EvaluationConfusion MatrixBinary ClassificationCost-Sensitive Learning - Question #115ML Model Development
A data scientist uses logistic regression to build a fraud detection model. While the model accuracy is 99%, 90% of the fraud cases are not detected by the model. What action will...
Classification ThresholdImbalanced DataRecallLogistic Regression - Question #116Data Preparation for Machine Learning
A company is interested in building a fraud detection model. Currently, the data scientist does not have a sufficient amount of information due to the low number of fraud cases. Wh...
Class ImbalanceOversamplingSMOTEData Preprocessing - Question #117Data Preparation for Machine Learning
A machine learning engineer is preparing a data frame for a supervised learning task with the Amazon SageMaker Linear Learner algorithm. The ML engineer notices the target label cl...
Missing value imputationData preprocessingBias reductionFeature engineering - Question #118Data Preparation for Machine Learning
A company has collected customer comments on its products, rating them as safe or unsafe, using decision trees. The training dataset has the following features: id, date, full revi...
Missing Data HandlingData ImputationText PreprocessingTest Data Preparation - Question #119Data Preparation for Machine Learning
An insurance company needs to automate claim compliance reviews because human reviews are expensive and error-prone. The company has a large set of claims and a compliance label fo...
Natural Language Processing (NLP)Feature ExtractionText EmbeddingsSageMaker Built-in Algorithms - Question #120Deployment and Orchestration of ML Workflows
A company uses 10 Reserved Instances of accelerated instance types to serve the current version of an ML model. An ML engineer needs to deploy a new version of the model to an Amaz...
SageMaker DeploymentRolling UpdatesDowntime PreventionResource Optimization - Question #121ML Solution Monitoring, Maintenance, and Security
An IoT company uses Amazon SageMaker to train and test an XGBoost model for object detection. ML engineers need to monitor performance metrics when they train the model with varian...
ML monitoringAWS CloudWatchAWS SNSSageMaker - Question #122Deployment and Orchestration of ML Workflows
A company is working on an ML project that will include Amazon SageMaker notebook instances. An ML engineer must ensure that the SageMaker notebook instances do not allow root acce...
SageMaker NotebooksIAM Condition KeysAccess ControlSecurity Prevention - Question #123ML Solution Monitoring, Maintenance, and Security
A company is using Amazon SageMaker to develop ML models. The company stores sensitive training data in an Amazon S3 bucket. The model training must have network isolation from the...
SageMaker NetworkingVPC EndpointsNetwork IsolationS3 Private Access - Question #124Deployment and Orchestration of ML Workflows
A company needs an AWS solution that will automatically create versions of ML models as the models are created. Which solution will meet this requirement?
ML Model VersioningAmazon SageMakerModel RegistryMLOps - Question #125Deployment and Orchestration of ML Workflows
An ML engineer is developing a classification model. The ML engineer needs to use custom libraries in processing jobs, training jobs, and pipelines in Amazon SageMaker. Which solut...
SageMaker Custom ContainersML WorkflowsCustom LibrariesDocker - Question #126ML Solution Monitoring, Maintenance, and Security
An ML engineer is deploying a trained model to an Amazon SageMaker endpoint. The ML engineer needs to receive alerts when data quality issues occur in production. Which solution wi...
SageMaker Model MonitorData Quality MonitoringProduction ML MonitoringAlerting - Question #127ML Model Development
A company needs to use Amazon SageMaker to train a model on more than 300 GB of data. The training data is composed of files that are 200 MB in size. The data is stored in Amazon S...
SageMaker Data IngestionS3 StorageCost Optimization - Question #128Deployment and Orchestration of ML Workflows
A company has an ML model that is deployed to an Amazon SageMaker endpoint for real-time inference. The company needs to deploy a new model. The company must compare the new model'...
SageMaker EndpointsA/B TestingModel DeploymentCanary Deployment - Question #129ML Solution Monitoring, Maintenance, and Security
A company runs an ML model on Amazon SageMaker. The company uses an automatic process that makes API calls to create training jobs for the model. The company has new compliance rul...
SageMaker Training ConfigurationMetadata ManagementComplianceData Privacy - Question #130Deployment and Orchestration of ML Workflows
A company is exploring generative AI and wants to add a new product feature. An ML engineer is making API calls from existing Amazon EC2 instances to Amazon Bedrock. The EC2 instan...
AWS PrivateLinkVPC EndpointsPrivate NetworkingAmazon Bedrock - Question #132Deployment and Orchestration of ML Workflows
A company wants to launch a new internal generative AI interface to answer user questions. The interface will be based on a popular open source large language model (LLM). Which co...
Generative AILLM DeploymentServerlessOperational Overhead - Question #133Data Preparation for Machine Learning
A company wants to build a real-time analytics application that uses streaming data from social media. An ML engineer must implement a solution that ingests and transforms 5 GB of...
Streaming Data IngestionReal-time AnalyticsAWS KinesisData Pipelines - Question #134Data Preparation for Machine Learning
A company stores training data as a .csv file in an Amazon S3 bucket. The company must encrypt the data and must control which applications have access to the encryption key. Which...
AWS KMSData EncryptionData SecurityAWS Encryption CLI - Question #135Data Preparation for Machine Learning
A company needs to perform feature engineering, aggregation, and data preparation. After the features are produced, the company must implement a solution on AWS to process and stor...
Feature EngineeringData PreparationSageMaker Feature StoreML Data Storage - Question #136Deployment and Orchestration of ML Workflows
A company is developing a new online application to gather information from customers. An ML engineer has developed a new ML model that will determine a score for each customer. Th...
SageMaker InferenceReal-time InferenceLow LatencyModel Deployment - Question #137Data Preparation for Machine Learning
A company is using Amazon EMR. The company has a large dataset in Amazon S3 that needs to be ingested into Amazon SageMaker Feature Store. The dataset contains historical data and...
SageMaker Feature StoreData IngestionSpark ConnectorOnline/Offline Feature Store - Question #138Deployment and Orchestration of ML Workflows
An ML engineer needs to deploy four ML models in an Amazon SageMaker inference pipeline. The models were built with different frameworks. The ML engineer also needs to give clients...
SageMaker EndpointsMulti-Container EndpointsModel DeploymentCost Optimization - Question #139ML Solution Monitoring, Maintenance, and Security
An ML engineer wants an Amazon SageMaker notebook to automatically stop running after 1 hour of idle time. How can the ML engineer accomplish this goal?
SageMaker Notebook InstancesLifecycle ConfigurationsCost OptimizationAutomation - Question #140Data Preparation for Machine Learning
A company wants to provide services to help other businesses label images. The company wants its labeling specialists to complete human labeling tasks on AWS. How should the compan...
Data LabelingAmazon SageMaker Ground TruthInternal WorkforceHuman-in-the-Loop - Question #141Deployment and Orchestration of ML Workflows
A company wants to use Amazon SageMaker to host an ML model that runs on CPU for real-time predictions. The model will have intermittent traffic during business hours and will have...
SageMaker Inference EndpointsServerless MLCost OptimizationReal-time Prediction - Question #142Data Preparation for Machine Learning
An ML engineer needs to train a supervised deep learning model. The available dataset is a large number of unlabeled images that only employees should access. The ML engineer needs...
Data LabelingAWS SageMaker Ground TruthPrivate WorkforceData Privacy - Question #143Data Preparation for Machine Learning
A company is using an Amazon S3 bucket to collect data that will be used for ML workflows. The company needs to use AWS Glue DataBrew to clean and normalize the data. Which solutio...
AWS Glue DataBrewAmazon S3Data IngestionData Cleaning - Question #144Data Preparation for Machine Learning
A company is developing a new ML model that uses the XGBoost algorithm. The company will train the model on data that is stored in an Amazon S3 bucket. The data is in a nested JSON...
Data TransformationAWS GlueNested JSONServerless ETL - Question #145ML Model Development
A medical company ingests streams of data from devices that monitor patients' vital signs. The company uses Amazon SageMaker and plans to prepare ML models to predict adverse event...
SageMaker ExperimentsML Experiment TrackingML Model DevelopmentMLOps - Question #146Deployment and Orchestration of ML Workflows
A company is planning to create an internal-only chat interface to help employees handle customer queries. Currently, the employees need to refer to a massive knowledge base of int...
Serverless ArchitectureGenerative AIRetrieval Augmented Generation (RAG)Vector Embeddings - Question #147Deployment and Orchestration of ML Workflows
An ML engineer needs to deploy a trained model that is based on a genetic algorithm. The algorithm solves a complex problem and can take several minutes to generate predictions. Wh...
Model DeploymentSageMaker Asynchronous InferenceOperational OverheadLarge Payloads - Question #148Data Preparation for Machine Learning
An ML engineer wants to use a set of survey responses as training data for an ML classifier. All the survey responses are either "yes" or "no." The ML engineer needs to convert the...
Feature EngineeringCategorical EncodingData PreprocessingDimensionality - Question #150ML Model Development
A company is planning to use an Amazon SageMaker prebuilt algorithm to create a recommendation model. The algorithm must be able to make predictions on high-dimensional sparse data...
Amazon SageMaker algorithmsRecommendation modelsHigh-dimensional sparse dataFactorization Machines - Question #151Deployment and Orchestration of ML Workflows
A company has several teams that have developed separate prediction models on their own laptops. The teams developed the models by using Python with scikit-learn and TensorFlow fra...
SageMaker integrationModel RegistryManaged ML servicesOperational efficiency - Question #152Deployment and Orchestration of ML Workflows
A company is training a large language model (LLM) by using on-premises infrastructure. A live conversational engine uses the LLM to help customers find real-time insights in credi...
SageMaker Training CompilerLLM trainingSageMaker real-time inferenceModel deployment - Question #153Deployment and Orchestration of ML Workflows
A company has an existing Amazon SageMaker model (v1) on a production endpoint. The company develops a new model version (v2) and needs to test v2 in production before substituting...
Model Deployment StrategiesSageMaker Shadow TestingProduction Risk MitigationLive Traffic Testing - Question #154ML Solution Monitoring, Maintenance, and Security
A company is building an ML model by using Amazon SageMaker, AWS owned libraries, and open source libraries. The company must ensure that SageMaker does not collect metadata about...
SageMaker configurationMetadata controlData privacy - Question #155Data Preparation for Machine Learning
An ML engineer is training an ML model to identify people's health risk based on 20 features and 1 target. The target class has two values: - Likely to have health risk (positive c...
Data ImbalanceBias MitigationUndersamplingData Preprocessing - Question #156ML Model Development
A company is building an Amazon SageMaker AI pipeline for an ML model. The pipeline uses distributed processing and training. An ML engineer needs to encrypt network communication...
SageMakerDistributed TrainingNetwork EncryptionSecurity - Question #157ML Solution Monitoring, Maintenance, and Security
An ML model is deployed in production. The model has performed well and has met its metric thresholds for months. An ML engineer who is monitoring the model observes a sudden degra...
Model monitoringData driftProduction model issuesPerformance degradation - Question #158Data Preparation for Machine Learning
An ML engineer is using AWS Glue to transform proprietary data from a third-party vendor to a format that the ML engineer intends to use with the Amazon SageMaker DeepAR forecastin...
AWS GlueData TransformationData FormatsData Compression