Amazon AWS Certified Machine Learning Engineer — Associate MLA-C01 Exam Dumps and Practice Test Questions Set 14 Q196-210

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Question 196

A company wants to detect fraudulent transactions in real time for an online payment system. Which AWS service is most suitable?

A) Amazon SageMaker real-time endpoint
B) Amazon S3
C) Amazon Athena
D) AWS Glue

Answer: A

Explanation:

The first service, Amazon SageMaker real-time endpoint, is specifically designed for low-latency, real-time inference, which is essential for detecting fraudulent transactions as they occur. Immediate detection allows the system to block or flag suspicious payments, preventing financial loss and protecting customers. SageMaker real-time endpoints provide an HTTPS interface to send transaction data, and predictions are returned almost instantly. This ensures that fraudulent transactions can be intercepted before they are completed. SageMaker handles autoscaling, load balancing, logging, and monitoring, ensuring consistent performance even during peak transaction periods, such as holidays or sales events. Integration with AWS Lambda allows automated responses, such as triggering alerts, freezing accounts, or sending notifications to risk management teams. Deploying models on SageMaker endpoints eliminates the need to maintain custom inference infrastructure, providing a scalable, reliable, and fully managed solution.

The second service, Amazon S3, is primarily used for storing historical transaction data and training datasets. While essential for model training and storage, S3 cannot provide real-time detection or prevention of fraud. Using S3 alone would require additional infrastructure to perform inference, introducing latency incompatible with operational requirements.

The third service, Amazon Athena, is a serverless SQL engine for querying structured data in S3. Athena is suitable for batch reporting or analysis of historical fraud trends, but cannot provide low-latency, real-time predictions. Batch queries are too slow to prevent fraudulent transactions immediately.

The fourth service, AWS Glue, is a managed ETL service for cleaning, transforming, and preparing datasets. Glue is useful for preprocessing transaction logs or generating features for model training, but it does not provide real-time inference. Using Glue alone would leave a gap in operational fraud detection workflows.

The correct reasoning is that Amazon SageMaker real-time endpoints provide fully managed, low-latency inference necessary for detecting fraudulent transactions in real time. S3 provides storage, Athena supports batch queries, and Glue handles preprocessing, but none provide immediate predictions. SageMaker enables automated workflows, instant fraud detection, and operational efficiency. By detecting fraud as transactions occur, companies can protect customers, reduce financial losses, and maintain trust. Integration with other AWS services ensures scalability, reliability, and consistent performance under high transaction volumes.

Question 197

A machine learning engineer wants to reduce overfitting in a gradient boosting model trained on a small dataset of loan defaults. Which technique is most effective?

A) Apply regularization and limit tree depth
B) Increase the number of trees dramatically
C) Use raw, unnormalized features
D) Remove cross-validation

Answer: A

Explanation:

The first technique, applying regularization and limiting tree depth, is highly effective for preventing overfitting in gradient boosting models trained on small datasets. Overfitting occurs when the model memorizes training data, including noise, rather than learning generalizable patterns. Regularization techniques, such as shrinkage (learning rate adjustment) or L1/L2 penalties, constrain model weights, reducing the risk of overfitting. Limiting tree depth prevents individual trees from becoming overly complex, ensuring they do not memorize idiosyncratic patterns in the loan default dataset. Together, regularization and depth limitation improve generalization performance on unseen data, which is critical for small datasets where models are prone to memorization. Gradient boosting models with these constraints produce more stable predictions, reduce variance, and improve operational decision-making in loan approval or risk assessment.

The second technique, increasing the number of trees dramatically, can worsen overfitting in small datasets. More trees may increase model complexity without sufficient data to generalize, causing the model to memorize noise and reduce predictive performance.

The third technique, using raw unnormalized features, does not prevent overfitting. Gradient boosting models handle feature scaling internally and are insensitive to feature magnitudes, so normalization is not critical for overfitting prevention.

The fourth technique, removing cross-validation, eliminates a method to evaluate model performance on unseen data. Without cross-validation, overfitting may go undetected, leading to unreliable predictions in deployment.

The correct reasoning is that applying regularization and limiting tree depth directly constrain model complexity and reduce memorization of noise, ensuring gradient boosting models generalize effectively. Increasing trees, using raw features, or removing cross-validation either exacerbate overfitting or fail to detect it. These techniques ensure that gradient boosting models trained on small loan default datasets produce reliable, accurate predictions, support robust operational decisions, and minimize risk in deployment. By implementing regularization and tree depth limits, engineers can achieve stable and generalizable models for financial risk assessment.

Question 198

A company wants to perform real-time object detection on a video feed from a security camera. Which AWS service is most suitable?

A) Amazon SageMaker real-time endpoint
B) Amazon S3
C) Amazon Athena
D) AWS Glue

Answer: A

Explanation:

The first service, Amazon SageMaker real-time endpoint, is designed for low-latency, real-time inference, making it ideal for performing object detection on live video feeds. Real-time detection allows the system to identify potential security threats or unusual activity immediately, enabling rapid intervention. SageMaker real-time endpoints provide an HTTPS interface where frames from the video feed are sent to the deployed model, and predictions are returned almost instantly. This ensures that object detection occurs in real time, which is crucial for surveillance systems that rely on immediate response. SageMaker manages autoscaling, load balancing, logging, and monitoring, ensuring consistent performance during periods of high activity or multiple camera feeds. Integration with AWS Lambda or SNS allows automated actions, such as triggering alarms, locking doors, or notifying security personnel. Deploying models on SageMaker endpoints eliminates the need to maintain custom inference infrastructure, providing a scalable, reliable, and fully managed solution.

The second service, Amazon S3, is primarily used for storing historical video footage or training datasets. While S3 is essential for storing and accessing data, it cannot perform real-time object detection. Using S3 alone would introduce significant latency, making it unsuitable for operational surveillance.

The third service, Amazon Athena, is a serverless SQL engine for querying structured data in S3. Athena is suitable for batch analysis of historical video metadata or generating reports, but cannot deliver low-latency predictions for live video streams. Batch queries are too slow to respond to real-time events.

The fourth service, AWS Glue, is a managed ETL service for cleaning, transforming, and preparing datasets. Glue can preprocess video metadata or generate features for model training, but does not provide inference capabilities. Using Glue alone would leave a gap in operational object detection workflows.

The correct reasoning is that Amazon SageMaker real-time endpoints provide fully managed, low-latency inference necessary for detecting objects in live video feeds. S3 provides storage, Athena supports batch analysis, and Glue handles preprocessing, but none provide real-time predictions. SageMaker enables automated workflows, immediate detection of potential threats, and operational efficiency. By delivering real-time object detection, security systems can respond rapidly, maintain safety, and prevent incidents. Integration with other AWS services ensures scalability, reliability, and consistent performance even under high video throughput or multiple camera feeds.

Question 199

A data science team wants to simplify the process of building, training, and deploying custom machine learning models without needing to manage infrastructure. They want a fully managed solution that supports experimentation through Jupyter notebooks and automated deployment capabilities. Which AWS service best meets this requirement?

A) Amazon SageMaker
B) Amazon Comprehend
C) AWS Glue
D) Amazon Athena

Answer: A

Explanation:

Amazon SageMaker is the best choice when a fully managed service is needed for building, training, and deploying custom machine learning models without managing underlying infrastructure. It provides end-to-end machine learning capabilities, including integrated development environments such as Jupyter notebooks, distributed training support, model tuning, deployment pipelines, and monitoring. This allows data science teams to focus on model development instead of infrastructure setup or maintenance. SageMaker also supports a wide range of algorithms, frameworks like TensorFlow and PyTorch, and automated scaling for inference endpoints. It integrates seamlessly with many AWS services such as Amazon S3 for data storage, AWS Lambda for inference workflows, and Amazon CloudWatch for monitoring performance metrics. The managed environment reduces operational burden and accelerates experimentation, enabling rapid iteration and deployment.

Amazon Comprehend is a natural language processing service used for extracting insights from text, including sentiment detection, entity recognition, topic modeling, and language detection. It is not designed to support custom end-to-end machine learning pipelines. While it helps with text analytics, it does not offer tools for general ML training and deployment. It is more suited for organizations wanting prebuilt language models rather than custom models.

AWS Glue is primarily an ETL and data integration service that simplifies data preparation pipelines for analytics and machine learning projects. It can catalog data, transform structured and semi-structured datasets, and prepare datasets for downstream workloads. However, Glue does not provide model-building or model-deployment capabilities. It is valuable when managing data movement and preprocessing, but it cannot replace a machine learning development environment.

Amazon Athena is a serverless SQL query service designed for interactive analytics on data stored in Amazon S3. It is very useful for querying large datasets without provisioning servers, and plays an important role in data exploration and analytics. However, Athena does not provide machine learning training or deployment capabilities. It cannot serve as a scalable ML experimentation and model inference platform.

Therefore, the correct selection is Amazon SageMaker. It offers a fully managed, scalable environment designed specifically for building, training, tuning, and deploying custom ML models in production. It eliminates the need for infrastructure management and improves productivity through integrated, collaborative tools. SageMaker’s automation and deployment features directly align with the team’s requirements, while the other services serve different purposes in the AWS ecosystem.

Question 200

A company needs to monitor a production machine learning model in real time to detect data quality issues such as missing values, drift, and performance degradation. Which AWS service provides built-in tools for monitoring model behavior after deployment?

A) Amazon SageMaker Model Monitor
B) AWS Identity and Access Management (IAM)
C) Amazon QuickSight
D) Amazon CloudWatch Logs

Answer: A

Explanation:

Amazon SageMaker Model Monitor is designed specifically for observing and analyzing deployed machine learning models in real time. It detects differences between training data and live production inputs, ensuring models continue to perform as expected. It automatically identifies issues such as data drift, missing values, out-of-range values, bias, and performance degradation. Model Monitor compares live inference data with baseline statistics captured during training. If anomalies are detected, it generates detailed reports, logs insights into Amazon CloudWatch, and triggers alerts for prompt remediation. This ensures quality control throughout the model lifecycle and supports continuous model governance.

AWS Identity and Access Management (IAM) manages secure access control and permissions for AWS resources. While crucial for securing machine learning platforms, IAM does not analyze model behavior, detect drift, or monitor performance. It supports policies for who can access SageMaker, but does not evaluate model output quality.

Amazon QuickSight is a business intelligence and dashboarding service used to build interactive analytics visualizations. While it can display metrics related to model performance or drift if the data is streamed to it, it does not autonomously detect issues or perform statistical monitoring on inference data. QuickSight does not provide automated alerts or machine learning drift diagnostics.

Amazon CloudWatch Logs helps collect and monitor logs, such as model inference logs or system-level events from AWS resources. While CloudWatch can alert on predefined metrics and thresholds, it does not provide machine learning-specific monitoring capabilities such as baseline comparison, drift detection, or data quality checks. It can be used alongside Model Monitor for custom dashboards and alerting, but alone, it cannot provide the required ML performance insights.

Thus, Amazon SageMaker Model Monitor is the appropriate service because it delivers automated, continuous monitoring of deployed models. It ensures that production models remain reliable by applying statistical and data integrity checks. When issues are found, it provides interpretability insights and structured reports, enabling proactive maintenance and governance. The alternative services handle security, BI dashboards, or generic logs, but none include automated model behavior monitoring features necessary for a production ML system.

Question 201

A banking institution needs an automated solution to detect anomalies in credit card transaction trends, such as sudden spikes in high-risk purchases. They want to use a service that automatically identifies abnormal behavior in operational metrics without needing to build custom ML models. Which AWS service best supports this requirement?

A) Amazon Lookout for Metrics
B) Amazon S3
C) Amazon Redshift
D) Amazon Rekognition

Answer: A

Explanation:

Amazon Lookout for Metrics is specifically designed to automatically detect anomalies in time-series and operational metrics, making it ideal for monitoring financial transaction trends. It applies machine learning to learn normal patterns and detect deviations in real time, such as sudden increases in risky transactions, unusually large payments, or behavior inconsistent with historical customer activity. The service does not require users to build or train custom models; it automatically manages the underlying machine learning process. It pinpoints the source of anomalies, quantifies severity, and provides explanations such as which variables contributed most to the event. It triggers alerts and can integrate with AWS Lambda and Amazon SNS to initiate automated responses, such as temporarily freezing accounts or notifying fraud teams. This enables proactive fraud prevention and operational protection without requiring specialized ML development.

Amazon S3 is a scalable object storage service for storing data, including transaction history and logs. Although it is often used as a data source for anomaly detection systems, it cannot detect anomalies itself. It would require external analytics or ML tools, resulting in a delayed response for a real-time fraud environment.

Amazon Redshift is a data warehousing service optimized for analytics and reporting using SQL queries. While it can analyze large transaction datasets and historical performance patterns, it operates in a batch or scheduled analytical manner and does not provide real-time, automated anomaly detection. It also does not generate alerts based on behavioral deviations.

Amazon Rekognition is a computer vision service used for analyzing images and videos, such as facial recognition or object detection. It does not apply to analyzing behavioral anomalies in financial transaction metrics and therefore does not meet the institution’s requirements.

The correct choice is Amazon Lookout for Metrics because it fulfills the need for automated, ML-powered anomaly detection without requiring custom model development. It enables immediate recognition of unusual transaction behavior, giving institutions the ability to respond quickly to fraud risk and maintain customer trust. The other services support storage, analytics, or image processing, but do not specialize in time-series anomaly detection for financial data.

Question 202

A data science team wants to automate hyperparameter tuning for a deep learning model trained on Amazon SageMaker. The model training jobs are expensive, and the team wants the tuning process to converge as efficiently as possible. Which approach should they choose to maximize optimization efficiency with minimal cost?

A) Use Grid Search across all possible hyperparameter combinations
B) Use SageMaker Automatic Model Tuning with Bayesian Optimization strategy
C) Use random search and manually terminate low-performing trials
D) Increase instance types to larger GPU machines for every job

Answer: B

Explanation:

Hyperparameter tuning is one of the most resource-intensive activities in developing an optimal machine learning model. The first choice to examine is Grid Search, which evaluates every possible combination of hyperparameters explicitly. While exhaustive, its primary weakness is inefficiency. Exploring every permutation becomes computationally expensive, especially for deep learning models that utilize multiple parameters such as learning rate, dropout rate, batch size, and depth. Grid Search scales poorly and wastes compute resources by evaluating many low-value combinations. This results in high cost and slow convergence, making it impractical for teams who want efficient optimization in the cloud.

The second choice utilizes SageMaker Automatic Model Tuning with Bayesian Optimization. Bayesian Optimization is a probabilistic technique designed for optimizing expensive processes. Rather than blindly exploring space, it builds a model of the objective function as results are observed. By using prior performance knowledge, Bayesian Optimization intelligently selects better hyperparameter candidates in future training rounds, minimizing wasted compute time and speeding convergence. SageMaker’s Automatic Model Tuning distributes multiple training jobs in parallel and evaluates them using a defined objective metric such as accuracy or loss. The optimization engine uses performance feedback to continuously improve selection. This technique reduces cost by stopping evaluation of obviously inferior parameters early and redirecting effort toward promising regions of the search space. For expensive deep learning models, Bayesian Optimization strikes the ideal balance between exploration and exploitation. It is compute-efficient, cost-aware, and integrated seamlessly within SageMaker architecture, making it the best choice for this scenario.

The third choice, random search with manual termination, does not embody automation and introduces the burden of human oversight. Although random search can outperform Grid Search in some high-dimensional spaces, it still lacks the intelligence to prefer better samples. Manually terminating jobs requires constant monitoring and is prone to human bias or delay. This approach neither guarantees efficiency nor ensures systematic improvement of hyperparameter selection.

The fourth choice recommends switching instance types to larger GPU machines. While larger compute may reduce training runtime per job, it does not reduce the number of required experiments and instead increases hourly cost. Without optimization guidance, higher compute leads to higher expense. Faster individual training execution does not inherently translate into efficient tuning, particularly when doing so multiplies cost across dozens of training runs. Hardware changes must support an algorithmic strategy, and here the strategy is insufficient.

Therefore, the correct reasoning is that SageMaker’s Automatic Model Tuning with Bayesian Optimization is fundamentally designed to optimize cost, performance, and number of experiments. It learns from previous attempts, prioritizes parameter values with the highest probability of improving model evaluation metrics, and avoids wasteful exploration. The integration with managed infrastructure makes it scalable, fault-tolerant, and aligned with real enterprise deep learning needs. For organizations working on expensive models, this service ensures a much more efficient path to hyperparameter convergence. Accordingly, SageMaker with Bayesian Optimization is clearly the best solution among the presented options.

Question 203

A company wants to automatically label a large dataset of product review text and images to reduce manual labeling time. The dataset contains millions of entries, and the team wants a scalable labeling strategy. Which AWS service should they use?

A) Amazon Textract
B) Amazon SageMaker Ground Truth
C) Amazon Translate
D) AWS Glue

Answer: B

Explanation:

Large-scale labeling is a prerequisite for most supervised machine learning projects. The first possibility, Amazon Textract, is a service used for extracting text and data from scanned documents. Although Textract can detect structure and text contained within forms or tables, it does not perform dataset labeling tasks such as categorizing reviews or identifying product relevance. Textract does not support workflows that send data to human labelers and does not provide quality assurance mechanisms.

The second choice, Amazon SageMaker Ground Truth, is precisely the service designed for automated dataset labeling at scale. It employs active learning, integrating machine learning with human-in-the-loop validation. Ground Truth allows organizations to build labeling workflows for visual recognition tasks, such as object detection, and for natural language tasks such as sentiment classification. It supports automatic labeling where the model learns progressively by making predictions on unlabeled samples and forwards uncertain cases to human workers through Amazon Mechanical Turk or private workforces. This significantly lowers cost, because the machine assumes more responsibility as it improves. Ground Truth also tracks performance, ensuring label consistency through annotation consolidation. This makes it ideal when faced with millions of product reviews, where manual labeling would otherwise require huge staffing and time.

The third choice, Amazon Translate, is used exclusively for translating text between languages. Although useful for cross-lingual processing, it does not support dataset annotation processes or active learning workflows. Translation is not equivalent to labeling tasks and does not reduce the cost or burden associated with classification or detection.

The fourth service, AWS Glue, acts as a managed ETL platform to prepare and transform data. Glue is extremely important for organizing large datasets, creating metadata catalogs, and converting formats like JSON to parquet. Yet, it does not apply classification labeling or provide supervised learning assistance, meaning it cannot solve the problem independently.

The correct reasoning shows that SageMaker Ground Truth is built to optimize and automate labeling at a large scale. Organizations gain cost reduction through progressive automation, minimized manual involvement, and model-assisted workflows. The need to label millions of reviews efficiently aligns directly with the purpose of Ground Truth’s design, validating it as the best fit.

Question 204

A retail company must run batch inference nightly to update personalized product recommendations for millions of users. The team wants a cost-effective, fully managed solution that scales automatically without maintaining servers. What should they use?

A) SageMaker Batch Transform
B) SageMaker Real-time Inference Endpoint
C) Amazon Comprehend
D) AWS Lambda only

Answer: A

Explanation:

Some models require repeated bulk predictions, especially in retail personalization, where recommendations are updated using the latest browsing behavior, inventory, or pricing data. The first option, SageMaker Batch Transform, is specifically designed for batch inference workloads. It automatically loads an artifact, processes large datasets stored in services such as Amazon S3, scales as needed during execution, then deallocates compute resources upon completion. This reduces operational costs because compute exists only during execution. Batch Transform also supports distributed processing across multiple instances, ideal for millions of predictions. It is fully managed, meaning version control, logging, and security are integrated into the workflow.

Second, SageMaker Real-time Endpoints serve inference for applications requiring millisecond latency, like chatbots or fraud detection. However, they remain operational constantly, billing for uptime regardless of traffic volume. For nightly jobs, maintaining persistent endpoints wastes money and does not match usage patterns.

Third, Amazon Comprehend is used for NLP tasks, including sentiment and entity extraction. It does not serve arbitrary machine learning models nor manage large-scale recommendation inference.

Fourth, AWS Lambda is serverless but is limited by runtime and payload constraints. Running models requiring GPUs or long execution durations is impractical. Lambda cannot efficiently process millions of recommendations in batch form.

Batch inference demands managed distributed capabilities and cost efficiency, characteristics uniquely provided by SageMaker Batch Transform.

Question 205

A company wants to deploy a machine learning model that predicts equipment failure in real time based on sensor readings from industrial machines. The solution must support low-latency inference and scale with fluctuating sensor input volume. Which AWS service should the team use?

A) Amazon SageMaker real-time endpoint
B) Amazon S3
C) Amazon Athena
D) AWS Glue

Answer: A

Explanation:

Real-time predictions in industrial applications require low-latency inference because delays can result in missed detection of critical failures, which may cause costly downtime or safety hazards. Amazon SageMaker real-time endpoints are designed to address this requirement by providing a fully managed, low-latency inference service that allows real-time scoring of data. The deployed model receives incoming sensor readings via an HTTPS API call and returns predictions almost immediately. SageMaker endpoints automatically handle scaling, so when input volume fluctuates due to increased activity or sensor data bursts, the service adjusts compute resources to maintain performance and reliability. The endpoint can be configured with autoscaling policies to meet peak demand, while still minimizing costs during low-traffic periods. SageMaker also integrates with other AWS services like Amazon CloudWatch for logging, monitoring, and triggering automated alerts, and AWS Lambda for event-driven responses to failure predictions. For industrial environments, this ensures that any predicted equipment anomalies can be acted upon immediately, reducing risk and enabling proactive maintenance scheduling. Deploying models with SageMaker endpoints eliminates the need to manage custom infrastructure or low-level server resources, allowing engineers to focus on improving model accuracy and operational efficiency.

Amazon S3, by contrast, is primarily a storage service. While S3 can store historical sensor data for training or batch analysis, it cannot perform real-time inference. Using S3 alone would require the addition of compute infrastructure and custom scheduling to simulate low-latency predictions, which is inefficient and error-prone. S3 cannot scale dynamically for live sensor streams, and any solution built solely with S3 would experience significant delays in failure detection.

Amazon Athena is a serverless query service optimized for interactive analytics on structured data stored in S3. It is well-suited for batch querying and historical analysis, but does not provide real-time inference capabilities. Using Athena for predictive maintenance would only allow periodic reporting of past trends, which is insufficient for preventing immediate equipment failures.

AWS Glue is an ETL and data transformation service. It excels at cleaning, normalizing, and preparing datasets for analysis or model training. While Glue is valuable for preprocessing sensor data or consolidating historical metrics, it does not perform real-time predictions or low-latency scoring. Using Glue alone would leave a critical gap in the real-time prediction workflow.

The correct reasoning is that Amazon SageMaker real-time endpoints directly address the requirement for immediate, scalable, and managed inference. S3 provides storage, Athena supports batch analytics, and Glue handles data transformation, but none can perform low-latency predictions for fluctuating real-time sensor input. SageMaker endpoints enable continuous monitoring of industrial machinery, proactive maintenance, and immediate operational decision-making. Their integration with monitoring, alerting, and automation services ensures reliability and scalability, making them the most suitable solution for predicting equipment failure in real time.

Question 206

A retail company wants to automatically detect anomalies in daily sales data across multiple stores, accounting for trends, seasonality, and promotions. The team wants a solution that requires minimal machine learning expertise. Which AWS service should they use?

A) Amazon Lookout for Metrics
B) Amazon S3
C) Amazon Athena
D) AWS Glue

Answer: A

Explanation:

Detecting anomalies in time-series sales data across multiple stores involves identifying deviations from normal patterns while accounting for trends, seasonality, and external influences such as promotions or holidays. Amazon Lookout for Metrics is specifically designed for this type of automated anomaly detection. It applies machine learning algorithms that automatically learn normal behavior from historical metrics and detect anomalies without requiring users to manually build or tune models. This makes it ideal for organizations with minimal machine learning expertise. Lookout for Metrics supports multiple dimensions, enabling the company to analyze sales by store, product category, region, or other business-specific metrics. The service continuously ingests data from sources like Amazon S3, Redshift, or RDS, and monitors metrics in real time. When anomalies are detected, alerts are automatically generated through Amazon SNS or integrated workflows, allowing operational teams to respond promptly. Dashboards provide insights into which stores, products, or time periods contributed most to the detected anomalies, facilitating root cause analysis and strategic decision-making.

Amazon S3 serves primarily as a storage service for historical sales data. While it is necessary to store the raw metrics, S3 does not provide anomaly detection or generate alerts. Using S3 alone would require building custom ML pipelines or query scripts, which increases complexity and requires significant expertise.

Amazon Athena is a serverless SQL query engine suitable for ad hoc querying and batch analysis of structured sales data stored in S3. While Athena can be used to generate historical reports or perform exploratory analysis, it does not perform real-time anomaly detection or account for trends, seasonality, and promotions automatically. Batch analysis is insufficient for operational anomaly detection where timely alerts are critical.

AWS Glue is a managed ETL service for data preparation, cleaning, and transformation. Glue is useful for structuring sales data and integrating multiple sources, but it does not perform anomaly detection or automated monitoring. Using Glue alone would require additional ML services to detect unusual patterns in sales metrics.

The correct reasoning is that Amazon Lookout for Metrics provides automated, low-effort anomaly detection for complex sales data. S3 provides storage, Athena supports batch queries, and Glue handles preprocessing, but only Lookout for Metrics applies machine learning in a managed, real-time manner. By detecting anomalies efficiently, businesses can identify unusual trends, prevent revenue loss, and respond to operational changes quickly, without requiring deep ML expertise. Look out for Metrics’ automation, dashboarding, and alerting capabilities make it the optimal solution for anomaly detection in multi-store sales environments.

Question 207

A machine learning engineer is training a convolutional neural network on a limited dataset of medical images. The model achieves near-perfect accuracy on training data but performs poorly on validation data. Which technique should the engineer apply to reduce overfitting?

A) Apply data augmentation and dropout
B) Increase the number of epochs
C) Use raw, unnormalized pixel values
D) Remove early stopping

Answer: A

Explanation:

Overfitting occurs when a model memorizes the training data instead of learning generalizable features. In convolutional neural networks (CNNs) trained on small datasets, this is a common problem, as the model achieves high training accuracy but fails on unseen validation or test data. The first technique, applying data augmentation and dropout, directly addresses overfitting. Data augmentation increases the diversity of training images by applying random transformations such as rotation, flipping, cropping, or color adjustments. This exposes the model to more variations, encouraging it to learn robust, generalizable features rather than memorizing the original limited dataset. Dropout randomly deactivates a proportion of neurons during training, forcing the network to learn distributed representations instead of relying on specific nodes. Together, these techniques reduce model complexity, prevent memorization, and improve validation performance. In medical imaging, where datasets are often small, these strategies are essential for producing reliable predictions on unseen images.

Increasing the number of epochs is counterproductive. Training for more iterations allows the network to memorize noise and spurious patterns in the training data, exacerbating overfitting and reducing generalization performance.

Using RA, with unnormalized pixel values, does not address overfitting. Normalization can help with convergence and stability, but the main issue is memorization of small datasets, which normalization alone cannot resolve.

Removing early stopping eliminates a safeguard that halts training when validation performance ceases to improve. Without early stopping, the model continues learning training-specific patterns, worsening overfitting.

The correct reasoning is that data augmentation and dropout directly combat overfitting by increasing training diversity and reducing reliance on specific neurons. Other techniques, like more epochs, unnormalized input, or disabling early stopping, either worsen overfitting or fail to mitigate it. For CNNs trained on limited medical image datasets, these techniques improve model generalization, reduce errors on unseen data, and enhance reliability in real-world applications.

Question 208

A company wants to train a text classification model to categorize customer support tickets into multiple issue types. They have a large unlabeled dataset and want to reduce manual labeling costs. Which AWS service is most suitable for this task?

A) Amazon SageMaker Ground Truth
B) Amazon Comprehend
C) Amazon Textract
D) AWS Glue

Answer: A

Explanation:

When a company has a large unlabeled dataset and needs to classify textual data, the challenge lies in obtaining accurate labeled data for supervised learning. Amazon SageMaker Ground Truth is specifically designed to address this challenge by providing a managed data labeling solution that combines machine learning with human annotation. Ground Truth supports text classification tasks, including multi-class or multi-label categorization, enabling users to build high-quality labeled datasets efficiently. It uses active learning to reduce human labeling requirements: the system begins by having humans label a small subset of data, trains a model on these initial labels, and then predicts labels for the remaining data. Only uncertain or low-confidence predictions are sent back to human annotators, minimizing manual effort and accelerating the labeling process. This reduces overall labeling costs while maintaining high accuracy, which is particularly important for customer support ticket classification, where misclassification could lead to improper issue handling. Ground Truth also integrates with Amazon Mechanical Turk, private workforce, or vendor-managed workforce to provide flexible annotation options. Additionally, it automatically tracks label quality and consistency, ensuring reliable training data.

Amazon Comprehend is a managed NLP service that can automatically detect sentiment, entities, and topics within text. While it provides pre-trained models for analysis, it does not offer a framework for building custom labeled datasets or training supervised models for company-specific ticket categories. Comprehend is better suited for insights extraction rather than supervised classification using domain-specific labels.

Amazon Textract extracts text and data from scanned documents, forms, and tables. While useful for digitizing legacy ticket data or extracting text from PDFs, it does not provide classification or labeling workflows. Textract is focused on data extraction rather than supervised ML labeling, and using it alone would not reduce labeling costs.

AWS Glue is an ETL service that can clean, transform, and prepare datasets. It is valuable for pre-processing tickets, merging data sources, or converting formats, but it does not facilitate labeling or supervised model creation. Glue alone cannot address the need for scalable annotation and dataset creation.

The correct reasoning is that SageMaker Ground Truth is purpose-built for automated labeling using active learning combined with human review, which minimizes labeling costs while generating high-quality datasets. Comprehend, Textract, and Glue support NLP analysis, text extraction, or data preprocessing, but they do not provide scalable, efficient labeling for supervised model training. By leveraging Ground Truth, organizations can build robust text classification models for customer support tickets efficiently and reliably.

Question 209

A financial institution needs to predict potential loan defaults using tabular customer data. The dataset contains hundreds of features, some of which are redundant. Which approach should the data scientist take to improve model performance and reduce overfitting?

A) Apply feature selection and regularization
B) Increase the number of training epochs
C) Use raw, unprocessed features
D) Remove cross-validation

Answer: A

Explanation:

Predicting loan defaults using tabular data requires models that generalize well to unseen customers. Overfitting occurs when the model memorizes irrelevant or redundant patterns in the training data. The first approach, applying feature selection and regularization, directly addresses these issues. Feature selection identifies the most informative predictors and removes redundant or noisy variables. This simplifies the model, reduces complexity, and improves generalization. Regularization techniques such as L1 (Lasso) and L2 (Ridge) penalize large weights, discouraging the model from relying too heavily on any single feature. This combination helps the model focus on meaningful patterns, prevents memorization of idiosyncrasies in the dataset, and improves predictive performance on new loan applicants. In financial applications, ensuring robust generalization is critical for minimizing risk and making sound lending decisions.

Increasing the number of training epochs allows the model to continue fitting the training data, which can exacerbate overfitting, particularly in datasets with limited variability. Extended training leads the model to memorize noise, decreasing performance on validation or production data.

Using raw, unprocessed features ignores the importance of scaling, encoding categorical variables, and removing irrelevant or highly correlated variables. This increases the likelihood of overfitting, introduces instability, and may hinder convergence of the learning algorithm.

Removing cross-validation eliminates the ability to evaluate the model’s generalization performance reliably. Cross-validation provides estimates of predictive accuracy on unseen data. Without it, the model may appear to perform well during training while failing in production, which is unacceptable in financial applications.

The correct reasoning is that feature selection and regularization are foundational techniques for preventing overfitting in tabular datasets with high-dimensional or redundant features. Other approaches, such as increasing epochs, using raw features, or removing cross-validation, neither worsen overfitting nor reduce reliability. By focusing on the most informative predictors and controlling model complexity, the institution can achieve robust, interpretable predictions for loan defaults and ensure effective risk management.

Question 210

A company wants to generate personalized product recommendations in real time for users visiting their e-commerce website. They require low-latency predictions and automatic scaling to handle traffic spikes during sales events. Which AWS service is most appropriate?

A) Amazon SageMaker real-time endpoint
B) Amazon S3
C) Amazon Athena
D) AWS Glue

Answer: A

Explanation:

Real-time recommendation engines require low-latency inference because predictions must be served immediately as users interact with the website. The first service, Amazon SageMaker real-time endpoints, provides a fully managed solution for deploying trained machine learning models and serving predictions with low latency. Users send input data such as user behavior, preferences, or browsing history to the endpoint via HTTPS API calls, and the endpoint returns personalized recommendations instantly. SageMaker endpoints automatically scale with traffic, accommodating spikes during sales events without manual infrastructure adjustments. Autoscaling policies can be configured based on request volume, ensuring both high availability and cost efficiency. The service also integrates with AWS monitoring and alerting tools such as CloudWatch and SNS, allowing operational teams to track endpoint performance and detect anomalies. By leveraging managed endpoints, companies eliminate the overhead of provisioning servers or managing load balancers while ensuring consistent performance under varying traffic conditions. This makes SageMaker real-time endpoints highly suitable for e-commerce personalization scenarios where response time and scalability are critical for user engagement and conversion.

Amazon S3 is primarily a storage service. While it can store user interaction data, model artifacts, and historical recommendation datasets, it does not provide real-time inference capabilities. Using S3 alone would require additional compute infrastructure, leading to latency and operational complexity.

Amazon Athena is a serverless query engine for analyzing structured data stored in S3. Athena is optimized for batch analytics and ad hoc queries, not for serving low-latency predictions. Using Athena for real-time recommendations would result in slow response times and a poor user experience.

AWS Glue is a managed ETL service for preparing and transforming data. Glue can preprocess features, aggregate logs, and prepare datasets for model training, but it cannot perform real-time inference. It is valuable in the data pipeline, but cannot replace a recommendation engine.

The correct reasoning is that Amazon SageMaker real-time endpoints provide low-latency, fully managed, scalable inference for real-time recommendation applications. S3 provides storage, Athena supports batch queries, and Glue handles preprocessing, but none offer real-time, scalable predictions. By using SageMaker endpoints, the company can deliver personalized recommendations instantly, improve engagement, and handle fluctuating traffic efficiently.