MLA-C01 Exam Questions
222 real MLA-C01 exam questions with expert-verified answers and explanations. Page 2 of 5.
- Question #56ML Model Development
An ML engineer needs to use Amazon SageMaker to fine-tune a large language model (LLM) for text summarization. The ML engineer must follow a low-code no-code (LCNC) approach. Which...
SageMaker JumpStartLLM fine-tuningLow-Code/No-Code (LCNC)SageMaker deployment - Question #57Deployment and Orchestration of ML Workflows
A company has an ML model that needs to run one time each night to predict stock values. The model input is 3 MB of data that is collected during the current day. The model produce...
SageMaker inferenceServerless inferenceModel deploymentCost optimization - Question #58Data Preparation for Machine Learning
An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed-circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a traini...
SageMaker Data WranglerImage PreprocessingData AugmentationModel Robustness - Question #59ML Solution Monitoring, Maintenance, and Security
A company has an application that uses different APIs to generate embeddings for input text. The company needs to implement a solution to automatically rotate the API tokens every...
API Token ManagementSecret RotationAWS Secrets ManagerSecurity Best Practices - Question #60Data Preparation for Machine Learning
An ML engineer receives datasets that contain missing values, duplicates, and extreme outliers. The ML engineer must consolidate these datasets into a single data frame and must pr...
SageMaker Data WranglerData CleaningData PreprocessingData Consolidation - Question #61ML Model Development
A company has historical data that shows whether customers needed long-term support from company staff. The company needs to develop an ML model to predict whether new customers wi...
Logistic RegressionClassificationSupervised LearningModel Selection - Question #62ML Model Development
An ML engineer has developed a binary classification model outside of Amazon SageMaker. The ML engineer needs to make the model accessible to a SageMaker Canvas user for additional...
SageMaker CanvasSageMaker Model RegistryModel SharingAWS S3 Permissions - Question #63ML Model Development
A company is building a deep learning model on Amazon SageMaker. The company uses a large amount of data as the training dataset. The company needs to optimize the model's hyperpar...
Hyperparameter tuningHyperbandModel optimizationComputational efficiency - Question #64Data Preparation for Machine Learning
A company is planning to use Amazon Redshift ML in its primary AWS account. The source data is in an Amazon S3 bucket in a secondary account. An ML engineer needs to set up an ML p...
Redshift MLCross-account S3 accessVPC EndpointsPrivate connectivity - Question #65ML Solution Monitoring, Maintenance, and Security
A company is using an AWS Lambda function to monitor the metrics from an ML model. An ML engineer needs to implement a solution to send an email message when the metrics breach a t...
ML Model MonitoringAWS CloudWatchLambda FunctionsAlerting - Question #66ML Solution Monitoring, Maintenance, and Security
A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer n...
SageMaker Model MonitorData Quality MonitoringModel BaselinesML Model Maintenance - Question #67Deployment and Orchestration of ML Workflows
A company has an ML model that generates text descriptions based on images that customers upload to the company's website. The images can be up to 50 MB in total size. An ML engine...
SageMaker Asynchronous InferenceAuto ScalingOperational OverheadML Deployment - Question #68Data Preparation for Machine Learning
An ML engineer needs to use AWS services to identify and extract meaningful unique keywords from documents. Which solution will meet these requirements with the LEAST operational o...
Natural Language Processing (NLP)Keyword ExtractionOperational OverheadAWS Comprehend - Question #69ML Solution Monitoring, Maintenance, and Security
A company needs to give its ML engineers appropriate access to training data. The ML engineers must access training data from only their own business group. The ML engineers must n...
IAMS3 Access ControlData SecurityLeast Privilege - Question #70Deployment and Orchestration of ML Workflows
A company needs to host a custom ML model to perform forecast analysis. The forecast analysis will occur with predictable and sustained load during the same 2-hour period every day...
SageMaker Serverless InferenceReal-time InferenceModel DeploymentProvisioned Concurrency - Question #71ML Solution Monitoring, Maintenance, and Security
A company's ML engineer has deployed an ML model for sentiment analysis to an Amazon SageMaker endpoint. The ML engineer needs to explain to company stakeholders how the model make...
SageMaker ClarifyModel ExplainabilityML InterpretabilitySageMaker Endpoints - Question #72ML Model Development
An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training. After some training attempts, the ML engineer observes that the instance...
Distributed TrainingAmazon SageMakerNetwork OptimizationData Locality - Question #73ML Model Development
A company is running ML models on premises by using custom Python scripts and proprietary datasets. The company is using PyTorch. The model building requires unique domain knowledg...
SageMaker Script ModeML Model MigrationPyTorchLeast Effort Solution - Question #74Data Preparation for Machine Learning
A company is using Amazon SageMaker and millions of files to train an ML model. Each file is several megabytes in size. The files are stored in an Amazon S3 bucket. The company nee...
AWS StorageFSx for LustreML Data PerformanceS3 Integration - Question #75Data Preparation for Machine Learning
A company wants to develop an ML model by using tabular data from its customers. The data contains meaningful ordered features with sensitive information that should not be discard...
Data PreparationData MaskingAWS Glue DataBrew - Question #76ML Solution Monitoring, Maintenance, and Security
An ML engineer needs to deploy ML models to get inferences from large datasets in an asynchronous manner. The ML engineer also needs to implement scheduled monitoring of the data q...
SageMaker Batch TransformSageMaker Model MonitorData Quality MonitoringAsynchronous Inference - Question #77Data Preparation for Machine Learning
An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize the production inference data in the same way as the tra...
Min-max normalizationFeature scalingData consistencyInference data preparation - Question #78ML Model Development
A company is planning to use Amazon SageMaker to make classification ratings that are based on images. The company has 6 衣 of training data that is stored on an Amazon FSx for NetA...
Amazon SageMaker Data AccessFSx for NetApp ONTAPTraining Data ManagementVPC Networking - Question #79Deployment and Orchestration of ML Workflows
A company regularly receives new training data from the vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days. The c...
Event-driven architectureAmazon EventBridgeSageMaker PipelinesS3 - Question #80ML Model Development
An ML engineer is developing a fraud detection model by using the Amazon SageMaker XGBoost algorithm. The model classifies transactions as either fraudulent or legitimate. During t...
OverfittingXGBoostHyperparameter TuningModel Generalization - Question #81ML Model Development
A company has a binary classification model in production. An ML engineer needs to develop a new version of the model. The new model version must maximize correct predictions of po...
Classification MetricsModel EvaluationAccuracyBinary Classification - Question #82Deployment and Orchestration of ML Workflows
A company is using Amazon SageMaker to create ML models. The company's data scientists need fine-grained control of the ML workflows that they orchestrate. The data scientists also...
SageMaker PipelinesML Workflow OrchestrationModel GovernanceDAG Visualization - Question #83ML Solution Monitoring, Maintenance, and Security
A company wants to reduce the cost of its containerized ML applications. The applications use ML models that run on Amazon EC2 instances, AWS Lambda functions, and an Amazon Elasti...
Cost OptimizationResource OptimizationAWS Compute OptimizerML Infrastructure - Question #84Deployment and Orchestration of ML Workflows
A company needs to create a central catalog for all the company's ML models. The models are in AWS accounts where the company developed the models initially. The models are hosted...
ML Model ManagementModel RegistryAmazon SageMakerMLOps - Question #85Deployment and Orchestration of ML Workflows
A company has developed a new ML model. The company requires online model validation on 10% of the traffic before the company fully releases the model in production. The company us...
SageMaker EndpointsModel DeploymentOnline Model ValidationTraffic Routing - Question #86ML Model Development
A company runs training jobs on Amazon SageMaker by using a compute optimized instance. Demand for training runs will remain constant for the next 55 weeks. The instance needs to r...
SageMakerCost OptimizationAWS Savings PlansMachine Learning Training - Question #87ML Solution Monitoring, Maintenance, and Security
A company deployed an ML model that uses the XGBoost algorithm to predict product failures. The model is hosted on an Amazon SageMaker endpoint and is trained on normal operating d...
SageMaker Model MonitorModel Drift DetectionML MonitoringReal-time Inference - Question #88ML Model Development
A company has an ML model that uses historical transaction data to predict customer behavior. An ML engineer is optimizing the model in Amazon SageMaker to enhance the model's pred...
SageMaker ClarifyModel BiasFairnessModel Evaluation - Question #89Deployment and Orchestration of ML Workflows
A company needs to use Retrieval Augmented Generation (RAG) to supplement an open source large language model (LLM) that runs on Amazon Bedrock. The company's data for RAG is a set...
Retrieval Augmented Generation (RAG)Amazon Bedrock Knowledge BasesLarge Language Models (LLMs)Operational Overhead - Question #90Deployment and Orchestration of ML Workflows
A company plans to deploy an ML model for production inference on an Amazon SageMaker endpoint. The average inference payload size will vary from 100 MB to 300 MB. Inference reques...
SageMaker InferenceAsynchronous InferenceModel DeploymentPayload Handling - Question #91Data Preparation for Machine Learning
An ML engineer notices class imbalance in an image classification training job. What should the ML engineer do to resolve this issue?
Class ImbalanceOversamplingData BalancingImage Classification - Question #92Data Preparation for Machine Learning
A company receives daily .csv files about customer interactions with its ML model. The company stores the files in Amazon S3 and uses the files to retrain the model. An ML engineer...
Data MaskingPII TransformationAWS GlueData Preprocessing - Question #93Data Preparation for Machine Learning
A medical company is using AWS to build a tool to recommend treatments for patients. The company has obtained health records and self-reported textual information in English from p...
Natural Language ProcessingAWS AI ServicesAmazon Comprehend MedicalManaged Services - Question #94Data Preparation for Machine Learning
A company needs to extract entities from a PDF document to build a classifier model. Which solution will extract and store the entities in the LEAST amount of time?
Amazon TextractAmazon ComprehendEntity ExtractionPDF Processing - Question #95ML Solution Monitoring, Maintenance, and Security
A company shares Amazon SageMaker Studio notebooks that are accessible through a VPN. The company must enforce access controls to prevent malicious actors from exploiting presigned...
SageMaker Studio SecurityIAM PoliciesNetwork Access ControlPresigned URL Security - Question #96Data Preparation for Machine Learning
An ML engineer needs to merge and transform data from two sources to retrain an existing ML model. One data source consists of .csv files that are stored in an Amazon S3 bucket. Ea...
AWS GlueETLApache SparkManaged Data Processing - Question #97ML Solution Monitoring, Maintenance, and Security
An ML engineer has deployed an Amazon SageMaker model to a serverless endpoint in production. The model is invoked by the InvokeEndpoint API operation. The model's latency in produ...
SageMaker Serverless EndpointsCloudWatch MetricsLatency MonitoringModel Cold Start - Question #98Data Preparation for Machine Learning
An ML engineer needs to ensure that a dataset complies with regulations for personally identifiable information (PII). The ML engineer will use the data to train an ML model on Ama...
PII RedactionData CleansingAmazon ComprehendAWS Data Storage - Question #99ML Model Development
A company must install a custom script on any newly created Amazon SageMaker notebook instances. Which solution will meet this requirement with the LEAST operational overhead?
SageMaker Notebook InstancesLifecycle ConfigurationsAutomationOperational Overhead - Question #100Data Preparation for Machine Learning
A company is building a real-time data processing pipeline for an ecommerce application. The application generates a high volume of clickstream data that must be ingested, processe...
Real-time streamingData ingestionData processing (SQL)Interactive analysis (Jupyter) - Question #101Data Preparation for Machine Learning
A medical company needs to store clinical data. The data includes personally identifiable information (PII) and protected health information (PHI). An ML engineer needs to implemen...
PII/PHI RedactionData PrivacyAmazon ComprehendData Preprocessing - Question #103ML Model Development
A machine learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on simi...
SageMaker Pipe ModeTraining OptimizationData Ingestion - Question #104Data Preparation for Machine Learning
A term frequency-inverse document frequency (tf-idf) matrix using both unigrams and bigrams is built from a text corpus consisting of the following two sentences: 1. Please call th...
TF-IDFFeature EngineeringN-gramsNatural Language Processing - Question #105Data Preparation for Machine Learning
A company is setting up a system to manage all of the datasets it stores in Amazon S3. The company would like to automate running transformation jobs on the data and maintaining a...
AWS GlueData CatalogETLServerless - Question #106ML Model Development
A data scientist is working on optimizing a model during the training process by varying multiple parameters. The data scientist observes that, during multiple runs with identical...
Model TrainingHyperparameter TuningLearning RateBatch Size