MLA-C01 Exam Questions
222 real MLA-C01 exam questions with expert-verified answers and explanations. Page 1 of 5.
- Question #1Deployment and Orchestration of ML Workflows
Case Study A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, tra...
SageMaker Model RegistryModel VersioningMLOpsManaged Services - Question #2Deployment and Orchestration of ML Workflows
Case Study A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, tra...
SageMaker warm poolsTraining job optimizationInfrastructure managementMLOps - Question #3Deployment and Orchestration of ML Workflows
Case Study A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, tra...
SageMaker PipelinesModel DeploymentApproval WorkflowModel Registry - Question #4ML Solution Monitoring, Maintenance, and Security
Case Study A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, tra...
SageMaker ClarifyModel monitoringBias detectionAWS Lambda - Question #5Data Preparation for Machine Learning
Case Study An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL data...
Data AggregationData LakeAWS Lake FormationData Integration - Question #11Data Preparation for Machine Learning
Case Study An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL data...
SageMaker Data WranglerData QualityAnomaly DetectionData Visualization - Question #12Data Preparation for Machine Learning
Case Study An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL data...
Data PreparationFeature EngineeringSageMaker Data WranglerCategorical Data Transformation - Question #13Data Preparation for Machine Learning
Case Study An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL data...
Data ImbalanceOversamplingData PreparationSageMaker Data Wrangler - Question #14ML Model Development
Case Study An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL data...
SageMaker Built-in AlgorithmsClass ImbalanceGradient BoostingFraud Detection - Question #15ML Solution Monitoring, Maintenance, and Security
A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to det...
Model MonitoringConcept DriftF1 ScoreAmazon SageMaker Model Monitor - Question #16ML Model Development
A company has a team of data scientists who use Amazon SageMaker notebook instances to test ML models. When the data scientists need new permissions, the company attaches the permi...
IAMAWS SageMakerPermissions ManagementSecurity Best Practices - Question #17ML Model Development
An ML engineer needs to use an ML model to predict the price of apartments in a specific location. Which metric should the ML engineer use to evaluate the model's performance?
Regression EvaluationModel Evaluation MetricsMean Absolute Error (MAE)ML Model Performance - Question #18ML Model Development
An ML engineer has trained a neural network by using stochastic gradient descent (SGD). The neural network performs poorly on the test set. The values for training loss and validat...
Learning RateHyperparameter TuningNeural Network TrainingOptimization - Question #19Data Preparation for Machine Learning
An ML engineer needs to process thousands of existing CSV objects and new CSV objects that are uploaded. The CSV objects are stored in a central Amazon S3 bucket and have the same...
Amazon AthenaData PartitioningOperational OverheadS3 Data Lakes - Question #20Data Preparation for Machine Learning
A company has a large, unstructured dataset. The dataset includes many duplicate records across several key attributes. Which solution on AWS will detect duplicates in the dataset...
DeduplicationData preparationAWS GlueRecord Linkage - Question #21Deployment and Orchestration of ML Workflows
A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handl...
EC2 Purchasing OptionsSpot InstancesCost OptimizationBatch Processing - Question #22Deployment and Orchestration of ML Workflows
An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account B in the same Region. Which solution w...
Amazon ComprehendCustom ModelsCross-account accessIAM Policies - Question #23ML Model Development
An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves su...
OverfittingEarly StoppingDropoutNeural Networks - Question #24Deployment and Orchestration of ML Workflows
A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and m...
Amazon KendraSemantic SearchRAGDocument Ingestion - Question #25ML Model Development
A company uses Amazon Athena to query a dataset in Amazon S3. The dataset has a target variable that the company wants to predict. The company needs to use the dataset in a solutio...
Amazon SageMaker AutopilotAutomated Machine LearningModel DevelopmentPredictive Modeling - Question #26Data Preparation for Machine Learning
A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model....
Feature EngineeringCategorical Data EncodingOne-hot EncodingNeural Networks Data Preparation - Question #27ML Solution Monitoring, Maintenance, and Security
A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon 53 to provide customers with a live conversational engine. The model is using se...
Sensitive Data IdentificationData SecurityAmazon MacieAWS Lambda - Question #28Deployment and Orchestration of ML Workflows
An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets. Which solution will meet these re...
Data IngestionData TransformationML Workflow OrchestrationModel Deployment - Question #29Deployment and Orchestration of ML Workflows
A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry. The data scientists ar...
Amazon SageMaker Model RegistryModel OrganizationModel DiscoverabilityModel Registry Collections - Question #30ML Solution Monitoring, Maintenance, and Security
A company runs an Amazon SageMaker domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker domain. Recently,...
AWS NetworkingNetwork ACLsVPC SecurityTraffic Filtering - Question #31Data Preparation for Machine Learning
A company is gathering audio, video, and text data in various languages. The company needs to use a large language model (LLM) to summarize the gathered data that is in Spanish. Wh...
AWS AI ServicesSpeech-to-TextMachine TranslationText Summarization - Question #32Deployment and Orchestration of ML Workflows
A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records every second. The company ne...
Real-time streamingAnomaly detectionKinesis Data AnalyticsOperational efficiency - Question #33ML Model Development
A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML...
Sentiment AnalysisAWS ComprehendNatural Language ProcessingManaged ML Services - Question #34ML Model Development
A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar qu...
LLM parametersTemperature parameterTop_k parameterAmazon Bedrock - Question #35ML Model Development
A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of mu...
SageMaker Linear LearnerHyperparameter TuningMulticlass ClassificationPrecision Optimization - Question #36Data Preparation for Machine Learning
A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch S...
Data IngestionReal-time ProcessingAmazon Kinesis Data FirehoseLatency Optimization - Question #37Deployment and Orchestration of ML Workflows
A company has trained an ML model in Amazon SageMaker. The company needs to host the model to provide inferences in a production environment. The model must be highly available and...
SageMaker InferenceReal-time EndpointsAuto ScalingModel Deployment - Question #38Deployment and Orchestration of ML Workflows
An ML engineer needs to use an Amazon EMR cluster to process large volumes of data in batches. Any data loss is unacceptable. Which instance purchasing option will meet these requi...
EMR cluster architectureAWS Spot InstancesCost optimizationData durability - Question #39ML Model Development
A company wants to improve the sustainability of its ML operations. Which actions will reduce the energy usage and computational resources that are associated with the company's tr...
ML SustainabilitySageMaker TrainingCompute OptimizationAWS Trainium - Question #40Deployment and Orchestration of ML Workflows
A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 ТВ in size and consists of CSV, JSON, Apac...
ML Workflow OrchestrationData PreprocessingSageMaker PipelinesBig Data Processing - Question #41Deployment and Orchestration of ML Workflows
An ML engineer needs to use AWS CloudFormation to create an ML model that an Amazon SageMaker endpoint will host. Which resource should the ML engineer declare in the CloudFormatio...
SageMaker ModelCloudFormationML Model DeploymentAWS Resources - Question #42ML Solution Monitoring, Maintenance, and Security
An advertising company uses AWS Lake Formation to manage a data lake. The data lake contains structured data and unstructured data. The company's ML engineers are assigned to speci...
AWS Lake FormationData Lake SecurityFine-grained Access ControlAmazon Athena - Question #43Data Preparation for Machine Learning
An ML engineer needs to use data with Amazon SageMaker Canvas to train an ML model. The data is stored in Amazon S3 and is complex in structure. The ML engineer must use a file for...
Data FormatsColumnar StoragePerformance OptimizationSageMaker Canvas - Question #44ML Model Development
An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of...
ML Evaluation MetricsRecallFalse NegativesModel Selection - Question #45ML Solution Monitoring, Maintenance, and Security
A company has trained and deployed an ML model by using Amazon SageMaker. The company needs to implement a solution to record and monitor all the API call events for the SageMaker...
SageMaker Endpoint MonitoringAWS CloudTrailAmazon CloudWatch AlarmsAPI Logging - Question #46Deployment and Orchestration of ML Workflows
A company has AWS Glue data processing jobs that are orchestrated by an AWS Glue workflow. The AWS Glue jobs can run on a schedule or can be launched manually. The company is devel...
SageMaker PipelinesAWS GlueWorkflow OrchestrationCallback Steps - Question #47ML Solution Monitoring, Maintenance, and Security
A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive. A data scientist needs to use some of the sensitive data from the database....
Amazon RedshiftDynamic Data MaskingData SecurityAccess Control - Question #48ML Model Development
An ML engineer is using a training job to fine-tune a deep learning model in Amazon SageMaker Studio. The ML engineer previously used the same pre-trained model with a similar data...
SageMaker DebuggerDeep Learning TrainingModel MonitoringPerformance Optimization - Question #49Deployment and Orchestration of ML Workflows
A credit card company has a fraud detection model in production on an Amazon SageMaker endpoint. The company develops a new version of the model. The company needs to assess the ne...
Shadow TestingSageMaker DeploymentModel EvaluationSafe Deployment - Question #50Data Preparation for Machine Learning
A company stores time-series data about user clicks in an Amazon S3 bucket. The raw data consists of millions of rows of user activity every day. ML engineers access the data to de...
S3 PartitioningAmazon AthenaTime-series DataData Lake Optimization - Question #51Deployment and Orchestration of ML Workflows
A company has deployed an ML model that detects fraudulent credit card transactions in real time in a banking application. The model uses Amazon SageMaker Asynchronous Inference. C...
SageMaker inferenceReal-time inferenceModel monitoringModel quality drift - Question #52Deployment and Orchestration of ML Workflows
An ML engineer needs to implement a solution to host a trained ML model. The rate of requests to the model will be inconsistent throughout the day. The ML engineer needs a scalable...
Model DeploymentSageMaker EndpointsAuto ScalingCost Optimization - Question #53ML Solution Monitoring, Maintenance, and Security
A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an autom...
Cost ManagementAWS BudgetsSageMaker StudioResource Tagging - Question #54Data Preparation for Machine Learning
A company uses Amazon SageMaker for its ML workloads. The company's ML engineer receives a 50 MB Apache Parquet data file to build a fraud detection model. The file includes severa...
Data PreparationFeature EngineeringSageMaker Data WranglerColumn Transformation - Question #55Deployment and Orchestration of ML Workflows
A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The company must ensure that...
Amazon Q BusinessResponse FilteringContent ModerationGenerative AI