Amazon AWS Certified Machine Learning – Specialty (MLS-C01) Exam Dumps and Practice Test Questions Set 2 Q21-40

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Question 21

A data science team needs to perform distributed hyperparameter tuning for a large XGBoost model on AWS using managed infrastructure. They want automatic logging, checkpointing, tracking, and the ability to run many parallel training jobs with minimal overhead. Which service should they use?

A)  AWS Glue
B) Amazon EMR
C) Amazon SageMaker Hyperparameter Tuning
D) AWS Batch

Answer: C

Explanation: 

A)  AWS Glue is a fully managed data integration service primarily designed for ETL tasks, schema discovery, and data cataloging. It is not built for managing machine learning training workflows or for running parallel training jobs. While Glue can process large datasets and even execute custom Python code, it does not provide native capabilities like metric tracking, automatic checkpointing, or orchestration of distributed hyperparameter tuning. Its strengths lie in data preparation, not model training automation, making it unsuitable for scaling complex hyperparameter search tasks.

B) Amazon EMR is highly flexible and can run distributed ML frameworks such as Spark MLlib, XGBoost on Spark, or Horovod. However, EMR requires significant setup, tuning, and cluster administration. It does not automatically track hyperparameters or metrics across jobs unless custom logging and monitoring systems are implemented. Although EMR can achieve distributed hyperparameter tuning through manual scripting, it lacks a built-in managed tuner that orchestrates parallel training jobs across heterogeneous compute resources.

C) Amazon SageMaker Hyperparameter Tuning provides automatic parallel training execution, metric tracking, checkpointing, early stopping, and seamless integration with various built-in and custom algorithms. It distributes training jobs across managed infrastructure without requiring administrators to provision or monitor compute resources. It supports Bayesian optimization, random search, grid search, and multi-objective optimization, and integrates directly with CloudWatch Logs and SageMaker Experiments. Its fully managed nature eliminates overhead and ensures reproducibility and scalability, making it ideal for large XGBoost tuning workloads.

D) AWS Batch is a service for running batch computing jobs at any scale. Although batch jobs can submit ML training tasks, the service does not inherently understand machine learning concepts like hyperparameters, objective metrics, or early stopping. Users must build custom logic for orchestration, metric aggregation, and checkpoint management. It is valuable for training pipelines needing general batch orchestration but not ideal for specialized hyperparameter optimization.

For these reasons, the correct choice is Amazon SageMaker Hyperparameter Tuning because it offers a fully managed environment tailored directly for distributed hyperparameter search, integrated monitoring, checkpointing, and automated orchestration of parallel ML training jobs, making it the most efficient and scalable option for this requirement.

Question 22

A retail company wants to deploy a real-time personalized recommendation API with response times under 30 milliseconds. The model is already trained. They want automatic scaling, built-in A/B testing, and the ability to perform inference acceleration using GPUs or Inferentia chips. What is the best service?

A)  Amazon EC2 Auto Scaling Group
B) Amazon SageMaker Real-Time Inference
C) Amazon API Gateway with Lambda
D) AWS Fargate

Answer: B

Explanation: 

A)  Amazon EC2 Auto Scaling Group allows deployment of scalable compute instances but requires extensive manual configuration. Users must install model servers, implement autoscaling policies, manage container orchestration, and configure health checks. It does not natively support model versioning, A/B testing logic, inference optimization, or GPU/Inferentia acceleration without additional engineering. Response-time consistency also depends on manual tuning, making EC2 less suitable when low-latency ML inference and managed features are required.

B) Amazon SageMaker Real-Time Inference is specifically designed for low-latency model serving. It supports accelerated inference using GPUs, Inferentia, or Habana chips, provides automatic scaling, integrates with SageMaker Model Registry, and supports A/B testing through production variants. It handles model loading, container management, multi-model endpoints, autoscaling, and health monitoring out of the box. It consistently delivers sub-30 ms latency for optimized models and integrates with SageMaker Neo for compilation and acceleration. Its tight coupling with the ML workflow makes it the best option for real-time recommendation systems.

C) Amazon API Gateway with Lambda is excellent for general serverless APIs but is not ideal for heavy ML inference. Lambda has cold starts, strict runtime limits, and lacks native GPU or Inferentia support. Large model packages may exceed deployment size limits, forcing external model loading which increases latency significantly. While caching and provisioned concurrency can help, Lambda cannot reliably meet stringent sub-30 ms latency requirements for ML inference workloads at scale.

D) AWS Fargate is a managed container service that removes the need to manage EC2 instances, but it does not provide ML-specific capabilities. It lacks built-in inference acceleration, A/B testing, model management, autoscaling based on inference metrics, or deployment automation for model endpoints. While suitable for microservices, it lacks optimizations essential for high-performance ML inference workloads.

Thus, Amazon SageMaker Real-Time Inference is the correct choice because it is purpose-built for scalable, low-latency ML model serving with built-in acceleration, autoscaling, and A/B testing.

Question 23

A media analytics company needs to preprocess millions of images daily before training a deep learning model. The processing includes resizing, normalization, and augmentation. They require a fully managed, scalable distributed processing framework tightly integrated with S3 and able to run PyTorch or TensorFlow preprocessing code. What should they choose?

A)  AWS Glue
B) Amazon EMR with Apache Spark
C) Amazon SageMaker Processing
D) AWS Batch

Answer: C

Explanation: 

A)  AWS Glue is optimized for ETL tasks involving structured data, schema discovery, and data catalog integration. While it supports Python through Glue jobs, it is not designed for deep learning–oriented image preprocessing. Glue’s environment limits usage of specialized DL libraries such as PyTorch or TensorFlow without significant workarounds. Image augmentation pipelines require GPU support or at least flexible environments, which Glue does not provide natively. Thus, Glue is not an ideal choice for large-scale deep learning preprocessing.

B) Amazon EMR with Apache Spark provides distributed processing at scale and can handle image transformation workloads with custom code. Spark can load binary data and perform distributed map operations. However, configuring EMR clusters, installing deep learning libraries, and maintaining the cluster environment increases operational complexity. EMR lacks native integration for managed deep learning environments, requiring custom AMIs or bootstrap actions. It is powerful but not specialized for ML preprocessing workflows.

C) Amazon SageMaker Processing is purpose-built for distributed data preprocessing, postprocessing, and evaluation tasks using scalable compute. It supports custom containers, built-in ML frameworks, and GPU acceleration when necessary. It integrates tightly with S3 and allows running arbitrary Python, TensorFlow, or PyTorch code. It automatically provisions compute, runs the job, saves outputs, and tears down resources. SageMaker Processing is ideal for large-scale image augmentation workloads and provides simplified operational management, making it the best option for deep learning preprocessing.

D) AWS Batch is excellent for batch workloads, including ML, but lacks native ML-specific integrations. It requires building custom containers, configuring queues, and managing dependencies manually. It does not automatically integrate with ML frameworks or provide optimized preprocessing utilities. While possible to use for image preprocessing, it adds unnecessary operational overhead compared to SageMaker Processing.

SageMaker Processing is the correct answer because it is fully managed, integrates deeply with S3, supports distributed execution, and allows running PyTorch or TensorFlow preprocessing pipelines at scale with minimal setup. It offers the simplest, most ML-oriented approach for the company’s requirements.

Question 24

A financial institution wants to build a fraud detection model requiring real-time inference and regular retraining using automated data ingestion and labeling. They also need lineage tracking and version control for models, datasets, and experiments. Which AWS service provides these capabilities?

A)  Amazon Lookout for Metrics
B) AWS Glue
C) Amazon SageMaker ML Ops features (Pipelines & Model Registry)
D) Amazon Forecast

Answer: C

Explanation: 

A)  Amazon Lookout for Metrics is designed for anomaly detection in time-series datasets. It automatically identifies anomalies without custom model development. While useful for monitoring, it does not support custom fraud detection models, automated retraining workflows, dataset versioning, or experiment lineage. It is not intended for full ML lifecycle management or real-time fraud inference.

B) AWS Glue provides automated data ingestion, schema discovery, and ETL transformation. It can assist in building data pipelines but does not offer ML-specific experiment tracking, model lineage, or automated deployment systems. Glue is a data engineering tool, not a complete ML lifecycle platform. It cannot manage model versions or orchestrate retraining pipelines without external services.

C) Amazon SageMaker ML Ops features, including Pipelines, Model Registry, and Experiments, provide full ML lifecycle automation. Pipelines orchestrate steps such as preprocessing, training, evaluation, and deployment. Model Registry tracks model versions, approvals, and lineage. Experiments manage trial runs, tracking metrics and parameters. Combined, these tools support automated retraining, lineage tracking, version control, and real-time deployment. SageMaker endpoints can host fraud detection models with low latency. This suite is built specifically for enterprise ML governance requirements.

D) Amazon Forecast is used for time-series forecasting and cannot support fraud detection use cases. It does not allow custom model training pipelines, experiment tracking, or retraining automation. Its domain is exclusively forecasting, excluding fraud detection workflows.

Thus, SageMaker ML Ops features are the correct answer because they offer end-to-end ML governance, automation, tracking, and real-time deployment capabilities required for fraud detection operations.

Question 25

A data engineering team needs a low-cost, highly scalable solution for running inference on thousands of models that share the same container but differ only in model artifacts. The models must load on demand to save memory. Which AWS feature is most suitable?

A)  SageMaker Asynchronous Inference
B) SageMaker Multi-Model Endpoints
C) Amazon ECS with Auto Scaling
D) Amazon EC2 Spot Instances

Answer: B

Explanation: 

A)  SageMaker Asynchronous Inference supports queuing requests and processing them for long-running inference operations. It is excellent for workloads needing scalable, non-real-time computation but does not support dynamically loading multiple models on demand. Each endpoint configuration typically hosts one model or one container, making it inefficient for hosting thousands of small models.

B) SageMaker Multi-Model Endpoints provide the ability to load multiple models dynamically using the same container. Only the requested model is loaded into memory, conserving resources. This approach scales cost-effectively and supports thousands of models by storing artifacts in S3 and loading them lazily. It reduces deployment overhead and supports a single endpoint serving numerous models while efficiently handling memory constraints.

C) Amazon ECS with Auto Scaling can host multiple containers with custom logic for loading models. However, ECS lacks built-in functionality for dynamic model loading or memory-aware model hosting. Managing thousands of model artifacts requires custom orchestration and caching logic. Although possible, it significantly increases the operational burden compared to SageMaker’s native capabilities.

D) Amazon EC2 Spot Instances provide low-cost compute but do not directly address model scaling needs or memory-efficient loading of many models. They require full infrastructure management and offer no built-in ML-specific model hosting capabilities. Spot interruptions further complicate model hosting.

Thus, SageMaker Multi-Model Endpoints are the correct answer because they are engineered for exactly this scenario: memory-efficient hosting of thousands of models using a single scalable endpoint.

Question 26

A research team needs to run distributed training for a PyTorch model using multiple GPU instances. They want to use a managed environment, built-in scaling, and coordinated training with minimal custom setup. What is the best AWS service for this?

A)  SageMaker Data Wrangler
B) SageMaker Distributed Training
C) AWS Batch
D) Amazon EMR

Answer: B

Explanation: 

A)  SageMaker Data Wrangler is a data preparation and visualization tool simplifying feature engineering and transformation tasks. It does not support distributed deep learning training. While it integrates with ML workflows, its purpose is data preprocessing, not orchestrating GPU clusters for model training. It offers no capabilities for synchronized distributed training.

B) SageMaker Distributed Training provides built-in libraries like model parallelism and data parallelism optimized for deep learning models. It supports distributed PyTorch or TensorFlow training with minimal configuration. SageMaker manages cluster provisioning, communication backends like NCCL, elastic training, and fault tolerance. It eliminates the need for manually configuring GPU nodes or distributed frameworks, making it ideal for large-scale PyTorch training workloads.

C) AWS Batch can run ML training jobs but lacks integrated distributed training capabilities. Users must manually implement cluster coordination, communication libraries, and inter-node networking. Although scalable, it requires extensive setup, making it unsuitable when users prefer minimal custom configuration.

D) Amazon EMR can run distributed frameworks but requires manual installation and orchestration of deep learning environments. It is optimized for big data processing rather than coordinated GPU-based distributed training. EMR lacks native deep learning orchestration features found in SageMaker.

Thus, SageMaker Distributed Training is the correct answer because it provides a fully managed, optimized environment for parallel and distributed training with minimal setup.

Question 27

A logistics company wants to forecast package delivery volumes across hundreds of locations. They want a fully managed forecasting service requiring no model development while supporting related time-series, holidays, and item-level metadata. Which AWS service should they use?

A)  Amazon Forecast
B) Amazon Lookout for Vision
C) SageMaker Autopilot
D) AWS Lambda

Answer: A

Explanation: 

A)  Amazon Forecast is a fully managed forecasting service based on the same technology used by Amazon.com. It supports related time-series, item metadata, holiday effects, and automatic model tuning. It requires no machine learning expertise and offers accurate forecasts for demand planning, inventory management, and logistics. It is specifically designed for this type of problem.

B) Amazon Lookout for Vision is designed for industrial defect detection in images and does not support time-series forecasting. It cannot process numeric historical data or produce delivery volume predictions. Its domain is computer vision anomaly detection.

C) SageMaker Autopilot automates ML model tuning and training, but it still requires the user to prepare the dataset and understand feature engineering concepts. It does not provide specialized forecasting algorithms designed for time-series patterns involving seasonality, related data, and multiple dimensions. Forecast offers better out-of-the-box performance for time-series forecasting and requires far less configuration.

D) AWS Lambda is a serverless compute service. It cannot perform forecasting by itself and requires user-provided ML code and model deployment. It is not a forecasting service and provides no algorithms for time-series analysis.

Thus, Amazon Forecast is the correct answer because it offers automated, scalable, and accurate forecasting with zero ML model development required.

Question 28

A company needs a feature store to maintain consistent features across training and inference. They want offline storage for training datasets and online storage for low-latency real-time inference. Which AWS service fulfills this requirement?

A)  Amazon DynamoDB
B) Amazon SageMaker Feature Store
C) Amazon RDS
D) AWS Glue Data Catalog

Answer: B

Explanation: 

A)  Amazon DynamoDB is a low-latency NoSQL database and could be used to store features, but it is not purpose-built for feature consistency or point-in-time record retrieval required for ML training. It lacks built-in feature lineage tracking, feature metadata, and integration with ML workflows. Ensuring feature consistency would require custom engineering.

B) Amazon SageMaker Feature Store provides both online and offline storage, ensuring consistent feature availability for training and inference. Offline storage in S3 supports batch training, while online storage provides millisecond-latency access during inference. It ensures point-in-time correctness, feature lineage, and integration with SageMaker pipelines and training jobs. It is purpose-built for managing ML features.

C) Amazon RDS is a relational database supporting structured data. While capable of storing features, it is not designed to maintain separate online/offline storage layers, nor does it support ML feature abstractions. Using RDS requires building custom synchronization logic and metadata management.

D) AWS Glue Data Catalog manages metadata about datasets but does not serve as a feature store. It does not provide real-time access to features or consistency guarantees across ML pipelines.

Thus, SageMaker Feature Store is the correct choice because it natively provides consistent offline and online feature access for ML workflows.

Question 29

A team wants to perform large-scale hyperparameter optimization using reinforcement learning techniques and neural architecture search. They need a service that can run thousands of experiments with parallel training jobs and automatic tracking. What should they use?

A)  AWS Step Functions
B) Amazon SageMaker Hyperparameter Tuning
C) Amazon EMR
D) AWS Glue

Answer: B

Explanation: 

When evaluating different AWS services for large-scale hyperparameter optimization, neural architecture search, or reinforcement-learning-driven experiment exploration, it is important to distinguish between workflow-orchestration tools, distributed compute platforms, ETL systems, and purpose-built machine-learning optimization services. AWS Step Functions, while highly effective for coordinating multi-stage pipelines, do not provide any built-in intelligence for hyperparameter search or experiment optimization. They excel in structuring complex workflows, invoking training jobs, managing retries, and connecting data preprocessing tasks with model training and deployment components. However, Step Functions themselves have no capability to evaluate hyperparameter performance, choose new hyperparameter combinations, rank model candidates, or track experimental metrics across thousands of trials. Implementing such logic manually would require substantial engineering effort and custom code, especially if attempting to emulate reinforcement-learning-based search strategies, adaptive exploration policies, or Bayesian optimization workflows.

Amazon EMR is similarly powerful from a distributed compute standpoint but lacks integrated tools for automatic hyperparameter optimization. EMR clusters can run Spark MLlib, distributed TensorFlow, or custom frameworks, yet the user must design and implement their own experiment scheduler, search algorithm, and metric-aggregation logic. This adds operational overhead and can significantly slow down large-scale tuning efforts, especially when rapid iteration or extensive parallelism is required.

AWS Glue is designed for ETL, cataloging, and data preparation, making it completely unrelated to orchestrating or optimizing ML training experiments. Although Glue can support data pipelines upstream of ML workflows, it does not interface with training job execution, parallelization logic, or model evaluation loops.

In contrast, Amazon SageMaker Hyperparameter Tuning is purpose-built for large-scale automated optimization. It provides advanced search techniques such as Bayesian optimization, random search, grid search, and hyperband-style strategies. It also supports early stopping, warm starts that reuse prior tuning knowledge, and the ability to evaluate thousands of training jobs in parallel across diverse compute resources. Metrics are automatically collected, tracked, and fed back into the search algorithm, enabling the service to intelligently guide exploration based on observed performance. Users can extend the search mechanism further by integrating reinforcement-learning-based controllers or custom strategies while still relying on SageMaker’s managed parallelization and monitoring. The service reduces engineering complexity substantially because it handles orchestration, scaling, experiment tracking, failure recovery, and metric aggregation without requiring manual setup.

Thus, SageMaker Hyperparameter Tuning is the correct choice because it provides automated optimization, efficient experiment management, scalable parallel execution, and built-in intelligence specifically designed for large-scale ML model tuning.

Question 30

A healthcare analytics team needs to label sensitive medical images using a private workforce. They require secure VPC-only access, audit logging, and HIPAA-eligible infrastructure. Which AWS service is the best fit?

A)  Amazon Mechanical Turk
B) Amazon SageMaker Ground Truth Private Workforce
C) AWS Batch
D) Amazon Rekognition Custom Labels

Answer: B

Explanation: 

A)  Amazon Mechanical Turk uses a public workforce. It is not appropriate for sensitive healthcare data and cannot meet HIPAA or VPC isolation requirements. It offers no private secure labeling environment or controlled access mechanisms.

B) Amazon SageMaker Ground Truth Private Workforce allows organizations to use their own internal workforce with secure VPC-only access. It supports encryption, audit logging, IAM controls, and operates on HIPAA-eligible infrastructure. It is specifically designed for sensitive labeling tasks, such as medical image annotation, and ensures compliance and data privacy.

C) AWS Batch executes batch computation workloads but is not designed for human labeling tasks. It cannot provide interfaces for annotating images, nor does it manage workforces or labeling tasks.

D) Amazon Rekognition Custom Labels provides automated labeling and training of vision models but does not support human-in-the-loop labeling workflows. It is also unsuitable for sensitive data that requires controlled access by private staff.

Thus, SageMaker Ground Truth Private Workforce is the correct answer because it provides secure, compliant, private labeling environments.

Question 31

A biotech company needs to run large-scale distributed data preprocessing for genomic sequences. They require a managed Spark environment, the ability to scale to hundreds of nodes, EMRFS integration with S3, and the option to customize cluster configurations. What should they choose?

A)  AWS Glue
B) Amazon EMR
C) AWS Batch
D) Amazon SageMaker Processing

Answer: B

Explanation:
  A)  AWS Glue offers managed ETL capabilities and supports large-scale data transformations, but it is not optimized for deeply customizable distributed environments. Glue runs managed Spark jobs but does not provide the full flexibility required for cluster tuning, custom bootstrap actions, or deep integration with large-scale pipelines that must run on hundreds of nodes. Its goal is simplicity, not cluster-level control or high-performance data engineering experimentation.

B) Amazon EMR is the best fit because it provides a fully customizable managed Hadoop and Spark ecosystem, supports autoscaling, EMRFS integration, spot instances, and allows clusters to be fine-tuned for memory-, CPU-, or GPU-heavy tasks. EMR is designed for large-scale distributed data processing including genomic workloads, supports massive parallelization, and enables custom environment configuration, bootstrap actions, and optimized networking. It scales to hundreds of nodes and integrates seamlessly with S3, making it ideal for bioinformatics pipelines requiring flexible distributed systems.

C) AWS Batch can run large-scale compute workloads but is not designed for Spark or distributed data processing requiring specialized cluster coordination. It excels at batch processing but lacks built-in frameworks like Hadoop or Spark. Setting up distributed workflows would require substantial manual configuration, making it less appropriate than EMR.

D) Amazon SageMaker Processing provides distributed ML-focused preprocessing but is designed around ML workflows rather than large-scale Spark-based pipelines. It supports limited scaling compared to EMR and does not offer full Spark cluster customization. It is better suited for model-related preprocessing rather than massive distributed genomic transformations.

Thus, Amazon EMR is the correct answer because it offers a fully customizable, scalable Spark environment ideal for large genomic preprocessing workloads.

Question 32

A team needs to serve thousands of real-time inference requests per second with strict millisecond latency. The model is small and invoked frequently, and they want full serverless scaling with no warm-up delays. Which service best fits this requirement?

A)  Lambda with Provisioned Concurrency
B) Amazon SageMaker Serverless Inference
C) Amazon SageMaker Batch Transform
D) Amazon EMR

Answer: A

Explanation:  

A)  Lambda with Provisioned Concurrency ensures that functions remain pre-initialized and ready to respond immediately. This eliminates cold starts and supports high request volumes with extremely low latency. For small models and lightweight inference logic, Lambda performs excellently, scaling automatically and providing predictable millisecond response times. Provisioned Concurrency guarantees consistent performance by keeping execution environments warm.

B) Amazon SageMaker Serverless Inference is a good serverless option but is better suited for ML models requiring short processing times with intermittent traffic. It is not optimized for extremely high request throughput with strict latency guarantees. Although it scales automatically, it may not deliver consistent ultra-low latency at the scale required and is best for bursty or unpredictable workloads rather than sustained thousands-per-second traffic.

C) Amazon SageMaker Batch Transform performs offline, asynchronous batch inference. It cannot serve real-time requests or provide immediate millisecond responses. It is designed for large-scale offline scoring of datasets and is unsuitable for user-facing inference scenarios with strict latency constraints.

D) Amazon EMR is irrelevant for real-time inference tasks. It is designed for distributed big data processing and does not provide low-latency inference endpoints or serverless scaling for millisecond API calls.

Thus, Lambda with Provisioned Concurrency is the correct answer because it provides predictable, cold-start–free, millisecond-level inference at massive scale.

Question 33

A company wants to track model experiments, including hyperparameters, datasets, and metrics, and compare different runs using a visualization interface. Which SageMaker feature should they use?

A)  SageMaker Data Wrangler
B) SageMaker Experiments
C) SageMaker Edge Manager
D) SageMaker Canvas

Answer: B

Explanation:  

A)  SageMaker Data Wrangler focuses on data preparation and visualization. It simplifies feature engineering but does not provide experiment tracking, hyperparameter comparisons, or trial lineage. It is unrelated to experiment management and does not address the need for tracking training metrics across multiple runs.

B) SageMaker Experiments provides the required functionality. It tracks metrics, parameters, datasets, and model artifacts. It enables organizing runs into trials and comparing different training executions visually in SageMaker Studio. It ensures lineage tracking and reproducibility, making it ideal for experiment management during ML development. It integrates directly with training jobs, pipelines, and notebooks.

C) SageMaker Edge Manager is designed for deploying and monitoring models at the edge. It manages model packaging, versioning, and monitoring on IoT devices but does not provide experiment comparison or training metric visualization. It serves a different purpose than experiment tracking.

D) SageMaker Canvas enables no-code model building and forecasting but does not provide experiment tracking or comparison tools for ML developers. It hides low-level details and therefore lacks the granular tracking needed.

Thus, SageMaker Experiments is the correct answer because it is built specifically for organizing, tracking, and comparing ML experiments.

Question 34

A transportation analytics team needs to convert raw GPS logs into engineered features such as distance traveled, speed, and route clustering. They want a simple visual interface for transformations and integration with training pipelines. Which service is best?

A)  SageMaker Data Wrangler
B) Amazon Athena
C) AWS Glue
D) SageMaker Model Monitor

Answer: A

Explanation:  

SageMaker Data Wrangler is designed specifically to simplify the process of preparing and engineering features for machine learning workflows. It provides a visual interface that allows data scientists and analysts to explore, clean, and transform raw datasets without writing extensive code. For a transportation analytics team working with GPS logs, this capability is especially valuable because Data Wrangler supports a wide range of operations including filtering, joins, aggregations, and geospatial transformations. Users can calculate distances, speeds, and cluster routes directly in the interface, which accelerates the feature engineering process and reduces manual effort.

In addition to these transformations, Data Wrangler integrates seamlessly with SageMaker Pipelines and other SageMaker services. Once features are engineered and validated, they can be exported directly to training workflows, allowing for automated model training and deployment. This integration ensures that the preprocessing steps are reproducible, auditable, and easily maintainable over time. The visual interface also enables iterative experimentation, helping teams quickly test different features or transformation strategies without leaving the tool.

Amazon Athena, while capable of querying raw GPS logs stored in S3 using SQL, is not optimized for ML-specific feature engineering. Athena excels at large-scale querying and aggregation, but it lacks built-in geospatial functions and visual tools for experimenting with features interactively. Complex transformations would require additional coding or integration with other tools, making the workflow less streamlined for ML purposes.

AWS Glue provides ETL capabilities and can perform batch data transformations at scale, but it is primarily designed for data integration rather than interactive feature engineering. It does not offer a visual interface for rapid experimentation, and ML-specific transformations such as calculating distances or clustering routes require custom scripting. SageMaker Model Monitor, on the other hand, is focused on monitoring deployed models for data drift and model quality and is unrelated to feature engineering or preprocessing.

Thus, SageMaker Data Wrangler is the correct choice because it provides an intuitive, visual environment for transforming raw GPS logs into engineered features, supports geospatial operations, and integrates directly with ML training pipelines, enabling teams to prepare data efficiently and reproducibly for modeling.

Question 35

A business wants to continuously monitor their deployed model for concept drift and feature-value changes. They need an automated service that can analyze data sent to an endpoint and generate reports. What should they use?

A)  SageMaker Model Monitor
B) SageMaker Clarify
C) Amazon CloudWatch Metrics
D) AWS Glue

Answer: A

Explanation:  

Amazon SageMaker Model Monitor is specifically designed to continuously monitor deployed machine learning models for drift in both data and features. Concept drift occurs when the statistical properties of the target variable change over time, while feature drift refers to changes in the distribution of input variables. Model Monitor automatically captures inference requests and can store them for analysis, enabling systematic comparison between baseline training data and live production data. This ensures that any significant deviations are detected early, helping maintain model accuracy and reliability.

The service can generate detailed reports, provide automated alerts, and even visualize drift patterns over time. Users can define custom monitoring schedules, thresholds, and metrics, making it flexible for business-specific monitoring requirements. Unlike manual monitoring approaches, Model Monitor eliminates the need to write complex scripts or custom pipelines to track data changes and ensures that all monitoring is integrated into the AWS ecosystem.

SageMaker Clarify, while valuable for detecting bias and explaining predictions, does not provide automated tracking of concept or feature drift. CloudWatch Metrics, on the other hand, primarily monitors infrastructure metrics such as CPU, memory, and latency, without offering ML-specific insights. AWS Glue is an ETL service that focuses on data integration and transformation but does not have any functionality for monitoring deployed machine learning models.

Thus, SageMaker Model Monitor is the correct choice because it is purpose-built for automated drift detection, reporting, and alerting, allowing organizations to maintain production model quality with minimal manual intervention.

Question 36

A startup needs a fast, easy way to generate training labels for an image classification dataset. They want minimal cost and do not require a private workforce. Which should they choose?

A)  SageMaker Ground Truth with Mechanical Turk
B) Ground Truth Private Workforce
C) Amazon Rekognition Custom Labels
D) AWS Batch

Answer: A

Explanation:  

SageMaker Ground Truth provides a fully managed solution for generating high-quality labeled datasets, including support for image classification tasks. When paired with Amazon Mechanical Turk, it leverages a public workforce to quickly annotate large datasets at a relatively low cost. This is ideal for startups or organizations that do not have strict privacy constraints and need to generate training data efficiently. Mechanical Turk allows organizations to scale labeling efforts dynamically without maintaining a dedicated labeling team.

Ground Truth also supports workflows for quality control, including consensus checking, annotation verification, and automated pre-labeling using machine learning models. This significantly reduces manual effort while ensuring the labels meet acceptable accuracy standards. By using a public workforce, organizations can save both time and resources compared to hiring internal or private labelers.

In contrast, Ground Truth’s Private Workforce option is designed for sensitive data and requires maintaining a controlled set of labelers, which can increase costs and administrative overhead. Amazon Rekognition Custom Labels automates the training of computer vision models but does not provide a labeling workforce for creating annotated datasets. AWS Batch is a general-purpose batch compute service and does not assist in labeling or annotation tasks.

Therefore, SageMaker Ground Truth with Mechanical Turk is the most appropriate choice for fast, cost-effective labeling when privacy is not a primary concern.

Question 37

A company wants to deploy a large model but minimize cost. The endpoint receives irregular traffic. They want automatic scaling to zero when not used. Which should they choose?

A)  SageMaker Real-Time Inference
B) SageMaker Serverless Inference
C) SageMaker Asynchronous Inference
D) ECS Fargate

Answer: B

Explanation:  

SageMaker Serverless Inference is designed for scenarios where ML endpoints experience irregular or unpredictable traffic. It automatically provisions and scales compute resources based on incoming requests and can scale down to zero when there is no activity. This enables businesses to minimize operational costs while still providing reliable inference services. Serverless Inference abstracts away infrastructure management, making it ideal for startups or applications with variable usage patterns.

Traditional Real-Time Inference endpoints maintain persistent instances, which can lead to unnecessary costs if the endpoint is idle for extended periods. Asynchronous Inference is suitable for long-running or batch-style inference requests but does not scale-to-zero automatically, making it less cost-effective for intermittent traffic. Using ECS Fargate would require managing containers and orchestrating scaling manually, which adds operational complexity without providing ML-specific inference optimization.

Serverless Inference integrates seamlessly with SageMaker pipelines, logging, and monitoring, ensuring that deployments are simple, cost-efficient, and maintainable. It is particularly advantageous for workloads with unpredictable or spiky usage patterns.

Thus, SageMaker Serverless Inference is the correct choice because it provides automated scaling to zero, removes infrastructure overhead, and optimizes costs for irregular traffic workloads.

Question 38

A robotics company needs to deploy reinforcement learning policies to edge devices and manage updates. Which service is best?

A)  SageMaker Edge Manager
B) SageMaker Processing
C) AWS Batch
D) AWS Glue

Answer: A

Explanation:  

SageMaker Edge Manager is purpose-built for deploying, monitoring, and managing machine learning models on edge devices. For robotics companies that need to update reinforcement learning policies on distributed edge devices, Edge Manager simplifies packaging, model versioning, and secure deployment. It ensures that devices always have the latest policy updates and can monitor inference performance in real time, providing actionable insights to improve decision-making at the edge.

SageMaker Processing is designed for preprocessing, postprocessing, and batch data transformations rather than deploying models to edge devices. AWS Batch executes compute workloads in the cloud but does not provide ML-specific deployment or version control for edge devices. Similarly, AWS Glue focuses on ETL tasks and has no functionality for deploying or managing ML models.

Edge Manager also supports monitoring deployed models to detect drift or performance degradation, enabling organizations to trigger model refreshes automatically. It integrates with other SageMaker services, allowing seamless workflows for training, packaging, deployment, and monitoring without manual intervention on each edge device.

Therefore, SageMaker Edge Manager is the optimal solution for deploying reinforcement learning policies at the edge, managing updates, and monitoring device-level performance efficiently.

Question 39

A team needs to run training jobs on Spot Instances with automatic interruption handling and checkpointing. Which SageMaker feature helps?

A)  Managed Spot Training
B) SageMaker Pipelines
C) SageMaker Studio
D) SageMaker Debugger

Answer: A

Explanation:  

SageMaker Managed Spot Training allows organizations to take advantage of EC2 Spot Instances for training jobs, significantly reducing compute costs. Spot Instances can be interrupted with short notice, so Managed Spot Training includes built-in checkpointing to save intermediate progress. If an instance is interrupted, the job can resume from the last checkpoint rather than starting over, ensuring efficient resource usage and minimizing training delays.

SageMaker Pipelines is an orchestration tool that manages ML workflows but does not provide native Spot Instance handling or checkpointing. SageMaker Studio is an IDE for model development and experimentation and does not manage job execution or instance interruptions. SageMaker Debugger is focused on monitoring and profiling training jobs to detect bottlenecks or anomalies but does not handle Spot interruptions or automatic checkpointing.

Managed Spot Training seamlessly integrates with SageMaker’s training APIs, allowing users to configure checkpoints, retry policies, and automatic resource scaling. This is particularly useful for large-scale training jobs that need cost optimization without sacrificing reliability. Organizations can train models efficiently, taking advantage of discounted Spot capacity while ensuring that progress is preserved during interruptions.

Thus, Managed Spot Training is the correct solution for running training jobs on Spot Instances with automatic interruption handling and checkpoint management.

Question 40

A company wants to build custom NLP models using billions of tokens and requires distributed training with minimal infrastructure work. Which should they use?

A)  SageMaker Distributed Training
B) Lambda
C) Glue
D) Rekognition

Answer: A

Explanation:  

SageMaker Distributed Training enables organizations to train very large NLP models using billions of tokens across multiple GPUs or instances without requiring extensive infrastructure management. It abstracts the complexities of distributed computing, such as data parallelism, model parallelism, gradient synchronization, and fault tolerance. Users can focus on model architecture and training strategy rather than cluster setup and optimization.

AWS Lambda is unsuitable because it has execution time and memory limitations, making it impossible to handle massive datasets or large neural network models. AWS Glue is designed for ETL workloads, not model training, and Amazon Rekognition is a managed computer vision service, irrelevant for custom NLP model training. Distributed Training in SageMaker provides built-in optimizations such as mixed-precision training, Horovod integration, and parameter server management, enabling faster convergence and efficient utilization of compute resources.

Additionally, Distributed Training supports seamless integration with other SageMaker features like Experiment Tracking, Debugger, and Profiler. This ensures that large-scale NLP training is not only efficient but also observable, auditable, and reproducible. Users can scale to hundreds of GPUs across multiple instances with minimal operational overhead, making it the preferred approach for handling massive NLP workloads.

Thus, SageMaker Distributed Training is the correct choice for building custom NLP models using extremely large datasets while minimizing infrastructure complexity and maximizing training efficiency.