MLA-C01 Exam Questions
222 real MLA-C01 exam questions with expert-verified answers and explanations. Page 4 of 5.
- Question #159Data Preparation for Machine Learning
A company has significantly increased the amount of data that is stored as .csv files in an Amazon S3 bucket. Data transformation scripts and queries are now taking much longer tha...
Data format optimizationApache ParquetAWS Glue ETLQuery performance - Question #160Data Preparation for Machine Learning
An ML engineer is analyzing a classification dataset before training a model in Amazon SageMarker AI. The ML engineer suspects that the dataset has a significant imbalance between...
Class ImbalanceBias MetricsPre-trainingData Analysis - Question #161Deployment and Orchestration of ML Workflows
An ML engineer uses one ML framework to train multiple ML models. The ML engineer needs to optimize the inference costs and host the models on Amazon SageMaker AI. Which solution w...
SageMaker InferenceMulti-Model EndpointCost OptimizationDeployment Strategies - Question #162Data Preparation for Machine Learning
A company is using an ML model to classify motion in videos. The data is stored in MP4 format in Amazon S3. When the company created the model, the company needed 4 months to label...
Data LabelingHuman-in-the-LoopVideo ProcessingAWS A2I - Question #163ML Solution Monitoring, Maintenance, and Security
An ecommerce company trains an ML model to forecast demand for near real-time inventory management based on historical customer activity. The company successfully deploys the train...
Model monitoringData driftSageMaker ClarifyMLOps - Question #164Deployment and Orchestration of ML Workflows
A logistics company has installed in-vehicle cameras for basic monitoring of its drivers. The company wants to improve driver safety by identifying distractions that could lead to...
Amazon RekognitionComputer VisionManaged AI ServicesOperational Effort - Question #165Deployment and Orchestration of ML Workflows
A company has trained an ML model that is packaged in a container. The company will integrate the model with an existing Python web application. The company needs to host the model...
AWS CDKAmazon EKSInfrastructure as CodeML Model Deployment - Question #166ML Model Development
An ML engineer is developing a linear regression ML model. The model shows high accuracy on the training dataset but performs poorly on unseen new data. Which action should the ML...
OverfittingRegularizationCross-validationModel Development Best Practices - Question #167Deployment and Orchestration of ML Workflows
A company is training a new ML model to replace a model that is deployed on an Amazon SageMaker AI real-time endpoint. An ML engineer needs to determine the latency and the accurac...
Shadow testingModel deployment strategiesReal-time inferenceModel evaluation - Question #168Data Preparation for Machine Learning
An ML engineer wants to use, prepare, and load data from Amazon S3 for analytics. The ML engineer must run an extract, transform, and load (ETL) job to discover the schema of the d...
ETLAWS GlueSchema DiscoveryData Catalog - Question #169Data Preparation for Machine Learning
An ML engineer is building an ML pipeline. The pipeline must process a dataset in two ways by using Amazon Athena. The pipeline must use batch processing to perform large-scale dat...
Data FormatsAmazon AthenaLatency OptimizationML Data Processing - Question #170Deployment and Orchestration of ML Workflows
A company uses an Amazon SageMaker AI ML model to make real-time inferences. The company has configured auto scaling for the Amazon EC2 instances that SageMaker AI uses for the inf...
Auto ScalingSageMaker EndpointsReal-time InferencePerformance Optimization - Question #171Deployment and Orchestration of ML Workflows
A company deployed an Amazon SageMaker AI ML model to an endpoint by calling the CreateModel API operation. The network that was established with the API call includes two private...
VPC EndpointsAmazon S3Private NetworkingSageMaker Deployment - Question #172ML Model Development
A company is developing a new ML model to rank customers in order of their potential to pay back loans. The company needs to use an Amazon SageMaker AI built-in algorithm. Which al...
SageMaker Built-in AlgorithmsSupervised LearningXGBoostClassification/Regression - Question #173ML Solution Monitoring, Maintenance, and Security
An ML engineer is setting up an Amazon SageMaker AI pipeline for an ML model. The pipeline must automatically initiate a re-training job if any data drift is detected. How should t...
Data Drift DetectionSageMaker Model MonitorAutomated RetrainingMLOps Automation - Question #174Deployment and Orchestration of ML Workflows
A company has developed a computer vision model. The company needs to deploy the model into production on Amazon SageMaker AI. The company has not hosted a model on SageMaker AI pr...
SageMaker Model RegistrySageMaker Inference RecommenderModel DeploymentModel Versioning - Question #175ML Model Development
A company is using Amazon SageMaker AI to develop a credit risk assessment model. During model validation, the company finds that the model achieves 82% accuracy on the validation...
OverfittingRegularizationModel ValidationCross-validation - Question #176Data Preparation for Machine Learning
A company collects customer data every day. The company stores the data as compressed files in an Amazon S3 bucket that is partitioned by date. Every month, analysts download the d...
Data QualityAWS GlueData CatalogServerless Architecture - Question #177ML Solution Monitoring, Maintenance, and Security
A company has an ML model in Amazon SageMaker AI. An ML engineer needs to implement a monitoring solution to automatically detect changes in the input data distribution of model fe...
SageMaker Model MonitorData Drift DetectionML MonitoringOperational Overhead - Question #178Deployment and Orchestration of ML Workflows
A company is using Amazon SageMaker AI to deploy a new recommendation model for its ecommerce website. The model must use data from all client website interactions as input. Traffi...
SageMaker InferenceServerless InferenceCost OptimizationModel Deployment - Question #179ML Solution Monitoring, Maintenance, and Security
A company runs an Amazon SageMaker AI domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker AI domain. Rec...
Network SecurityVPCNetwork ACLIP Blocking - Question #180Data Preparation for Machine Learning
A company's ML engineer is creating a classification model. The ML engineer explores the dataset and notices a column that is named day_of_week. The column's data consists of the f...
Categorical Data EncodingOne-Hot EncodingData PreprocessingFeature Engineering - Question #181Data Preparation for Machine Learning
An ML engineer wants to use Amazon SageMaker AI to prepare data for training. During exploratory data analysis, the ML engineer notices that several categorical features are missin...
SageMaker Data WranglerData PreprocessingMissing Value ImputationCategorical Features - Question #182Data Preparation for Machine Learning
A company stores user clickstream data in an Amazon S3 bucket in AWS Account A. The company needs to use the data to train an ML model in Amazon SageMaker AI in AWS Account
Cross-account data accessAmazon S3Amazon SageMakerVPC Endpoints - Question #183Data Preparation for Machine Learning
A music streaming company constantly streams song ratings from an application to an Amazon S3 bucket. The company wants to use the ratings as an input for training and inference of...
SageMaker Feature StoreFeature EngineeringReal-time InferenceBatch Training - Question #184ML Solution Monitoring, Maintenance, and Security
A hospital is using an ML model to validate x-ray results. The hospital runs a nightly batch inference job. The hospital needs to produce a daily report about model data quality an...
Model MonitoringData Quality MonitoringModel Performance MonitoringSageMaker Model Monitor - Question #185Data Preparation for Machine Learning
A company needs to ingest data from data sources into Amazon SageMaker Data Wrangler. The data sources are Amazon S3, Amazon Redshift, and Snowflake. The ingested data must always...
SageMaker Data WranglerData IngestionAWS Glue Data CatalogData Freshness - Question #186ML Model Development
An ML engineer is using Amazon SageMaker Canvas to build a custom ML model from an imported dataset. The ML engineer wants the model to make continuous numeric predictions based on...
Machine Learning EvaluationRegressionRMSESageMaker Canvas - Question #187Deployment and Orchestration of ML Workflows
A company has built, trained, and tuned two new ML models: - Model A detects if a transaction is fraudulent based on the IP address, location, and user credentials. This model will...
SageMaker deploymentReal-time inferenceBatch transformEndpoint types - Question #188ML Model Development
A bank needs to use Amazon SageMaker AI to create an ML model to determine which customers qualify for a new product. The bank must use algorithms that SageMaker AI directly suppor...
Explainable AIAmazon SageMakerLinear ModelsAlgorithm Selection - Question #189Data Preparation for Machine Learning
A company is preparing data to train a new ML model on Amazon SageMaker AI. The data has not been used before for ML training. The data includes duplicates and is missing some valu...
SageMaker Data WranglerSageMaker ClarifyData QualityML Bias - Question #190Data Preparation for Machine Learning
An ML engineer is building a model to predict house and apartment prices. The model uses three features: Square Meters, Price, and Age of Building. The dataset has 10,000 data rows...
Outlier HandlingLog TransformationNumerical Feature Preprocessing - Question #191ML Solution Monitoring, Maintenance, and Security
A company is developing an ML model by using Amazon SageMaker AI. The company must monitor bias in the model and must display the results on a dashboard. An ML engineer creates a b...
SageMaker ClarifyBias monitoringCloudWatch metricsModel monitoring - Question #192ML Solution Monitoring, Maintenance, and Security
A company is using an Amazon SageMaker AI ML model to predict traffic accidents that potholes cause. An ML engineer has configured SageMaker Model Monitor to run as part of a SageM...
SageMaker Model MonitorData DriftModel RetrainingML Pipeline Monitoring - Question #193ML Model Development
A company uses ML models to predict whether transactions are fraudulent. The company needs to identify as many fraudulent transactions as possible. Which evaluation metric should t...
Machine Learning MetricsRecallModel EvaluationFraud Detection - Question #194Deployment and Orchestration of ML Workflows
A recommendation model uses ML and calls an Amazon SageMaker AI endpoint to get recommendations. An ML engineer must ensure that the model stays available during an expected increa...
SageMaker EndpointsAuto ScalingModel DeploymentScalability - Question #196ML Model Development
An ML engineer has trained an ML model by using Amazon SageMaker AI. The ML engineer determines that the model is overfitting and that the training data contains unnecessary featur...
OverfittingRegularizationFeature SelectionModel Training - Question #197Data Preparation for Machine Learning
An ML engineer wants to use Amazon SageMaker Data Wrangler to perform preprocessing on a dataset. The ML engineer wants to use the processed dataset to train a classification model...
SageMaker Data WranglerData preprocessingSimilarity encodingText feature engineering - Question #198ML Model Development
An ML engineer is training a text generation model on Amazon SageMaker AI. After several epochs, the loss function does not converge, and the model's accuracy on the validation dat...
Hyperparameter TuningModel Training StabilityGeneralizationOptimization - Question #199Data Preparation for Machine Learning
A company uses an NFS-based data store to store data for ML training. Linux-based systems access the data store. The company needs a hybrid system to make the shared data store acc...
AWS StorageAmazon EFSHybrid CloudFile Locking - Question #200Data Preparation for Machine Learning
A company needs to analyze a large dataset that is stored in Amazon S3 in Apache Parquet format. The company wants to use one-hot encoding for some of the columns. The company need...
Data TransformationOne-Hot EncodingNo-code MLAWS Glue DataBrew - Question #201ML Model Development
A company wants to migrate ML models from an on-premises environment to Amazon SageMaker AI. The models are based on the PyTorch algorithm. The company needs to reuse its existing...
SageMaker Script ModePyTorchModel MigrationCustom Training - Question #202Data Preparation for Machine Learning
A company uses an Amazon QuickSight dashboard to track the sale prices of sneakers over time. The dashboard aggregates sale prices scraped from many retail websites. The company wa...
QuickSightOutlier DetectionData VisualizationCalculated Fields - Question #203ML Solution Monitoring, Maintenance, and Security
An ML engineer is using Amazon QuickSight anomaly detection to detect very high or very low machine operating temperatures compared to normal. The ML engineer sets the Severity par...
Anomaly DetectionQuickSightML MonitoringRecall (metric) - Question #204Deployment and Orchestration of ML Workflows
A company runs its ML workflows on an on-premises Kubernetes cluster. The ML workflows include ML services that perform training and inferences for ML models. Each ML service runs...
Container MigrationAmazon ECRAmazon EKSDocker - Question #205ML Model Development
An ML engineer needs to run intensive model training jobs each month that can take 48 to 72 hours to run. The training jobs can be interrupted and resumed without major issues. The...
Cost OptimizationSpot InstancesEC2 Pricing ModelsMachine Learning Training - Question #208Deployment and Orchestration of ML Workflows
A retail company is creating an AI-powered assistant for customers. The company has a large body of documentation that the assistant needs to use for general inquiries. The company...
Amazon Q BusinessRAGDocument FilteringMetadata Filtering - Question #212ML Model Development
A travel company wants to create an ML model to recommend the next airport destination for its users. The company has collected millions of data records about user location, recent...
Recommendation SystemsFactorization MachinesSageMaker Built-in AlgorithmsSparse Data Handling - Question #213Deployment and Orchestration of ML Workflows
An ML engineer is configuring auto scaling for an inference component of a model that runs behind an Amazon SageMaker AI endpoint. The ML engineer configures SageMaker AI auto scal...
SageMaker EndpointsAuto Scaling PoliciesCold Start ProblemProactive Scaling - Question #214ML Model Development
An ML engineer is using an Amazon SageMaker Studio notebook to train a neural network by creating an estimator. The estimator runs a Python training script that uses Distributed Da...
SageMaker ProfilerGPU OptimizationTraining PerformanceDistributed Training