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Question 121
A machine learning model needs to process streaming video data to detect objects in real-time with minimal latency. Which AWS service combination would be most effective for this use case?
A) Amazon Kinesis Video Streams with SageMaker real-time endpoints
B) Amazon S3 with SageMaker Batch Transform
C) Amazon RDS with Lambda functions
D) Amazon DynamoDB with SageMaker Asynchronous Inference
Answer: A
Explanation:
Amazon Kinesis Video Streams combined with SageMaker real-time endpoints provides the optimal architecture for real-time object detection in streaming video. Kinesis Video Streams ingests, processes, and stores video streams from connected devices with low latency. Video frames can be extracted and sent to SageMaker real-time inference endpoints that host object detection models, delivering predictions within milliseconds. This architecture supports continuous video processing with immediate object detection results, making it suitable for applications like security monitoring, autonomous vehicles, or quality inspection systems.
Option B is incorrect because Amazon S3 with Batch Transform is designed for offline batch processing of stored data, not real-time streaming video analysis. Batch Transform processes complete datasets at scheduled intervals, introducing significant latency between video capture and object detection. Videos would need to be uploaded to S3, accumulated, and then processed in batches, making this approach unsuitable for real-time applications requiring immediate detection and response.
Option C is incorrect because Amazon RDS is a relational database service for structured transactional data, not video stream processing. RDS cannot ingest or process video streams efficiently. While Lambda could potentially process individual frames, using RDS as the primary component for video streaming creates architectural mismatch. Lambda has execution time limits that make continuous video processing challenging, and RDS provides no video handling capabilities.
Option D is incorrect because Amazon DynamoDB is a NoSQL database for structured data storage, not video stream ingestion. SageMaker Asynchronous Inference is designed for requests with long processing times or large payloads, processing them asynchronously with queuing. This introduces latency unsuitable for real-time video object detection where immediate results are required. The combination doesn’t address video stream handling or provide the low-latency processing needed.
Question 122
A data scientist needs to identify anomalous patterns in multivariate time-series data from industrial equipment without labeled examples. Which SageMaker algorithm is most appropriate?
A) Linear Learner for classification
B) Random Cut Forest for anomaly detection
C) DeepAR for forecasting
D) BlazingText for text analysis
Answer: B
Explanation:
Random Cut Forest is the most appropriate SageMaker algorithm for identifying anomalous patterns in multivariate time-series data without labels. Random Cut Forest is an unsupervised algorithm specifically designed for anomaly detection that works effectively with time-series and high-dimensional data. It constructs multiple random decision trees that isolate anomalous data points, assigning anomaly scores based on how easily points are isolated. Random Cut Forest handles multivariate data naturally and doesn’t require labeled training examples, making it ideal for detecting unusual equipment behavior patterns.
Option A is incorrect because Linear Learner is a supervised learning algorithm requiring labeled training data for classification or regression tasks. The scenario explicitly states there are no labeled examples, making supervised learning impossible. Linear Learner needs examples of normal and anomalous patterns with labels to train, which aren’t available. Additionally, Linear Learner models linear relationships and may struggle with complex temporal patterns in time-series data.
Option C is incorrect because DeepAR is designed for time-series forecasting, predicting future values based on historical patterns. While DeepAR works with multivariate time-series, its purpose is prediction rather than anomaly detection. DeepAR learns typical patterns to forecast expected values but doesn’t explicitly identify anomalies or unusual deviations from normal behavior. Anomaly detection requires different algorithmic approaches focused on identifying outliers rather than forecasting.
Option D is incorrect because BlazingText is a natural language processing algorithm for text classification and generating word embeddings. BlazingText operates on text data, not numerical time-series data from industrial equipment. Applying a text processing algorithm to sensor readings and equipment metrics is conceptually inappropriate. BlazingText has no capabilities for handling temporal patterns or multivariate numerical data from machinery.
Question 123
A machine learning model shows high variance between different training runs with the same hyperparameters. Which technique would improve training stability and reproducibility?
A) Set random seeds for all random number generators
B) Increase the number of features significantly
C) Remove all regularization from the model
D) Use different data splits for each run
Answer: A
Explanation:
Setting random seeds for all random number generators improves training stability and ensures reproducibility across training runs. Machine learning training involves multiple sources of randomness including weight initialization, data shuffling, dropout layers, and data augmentation. Without fixed random seeds, each training run produces different results due to these random variations. Setting seeds for libraries like NumPy, TensorFlow, PyTorch, and SageMaker’s built-in algorithms ensures identical initialization and processing order, producing consistent results across runs with the same hyperparameters and data.
Option B is incorrect because significantly increasing the number of features would likely worsen variance and instability rather than improve it. More features increase model complexity and the risk of overfitting, potentially leading to greater variation between training runs. Additional features also increase the dimensionality of the optimization space, making training more sensitive to initialization and potentially less stable. Feature engineering should be driven by relevance, not by attempts to stabilize training.
Option C is incorrect because removing all regularization would increase model variance and overfitting rather than improving stability. Regularization techniques like L1, L2, dropout, or early stopping help control model complexity and reduce sensitivity to training data specifics. Without regularization, models fit training data more closely including noise, leading to higher variance between runs and worse generalization. Regularization actually improves stability by constraining the solution space.
Option D is incorrect because using different data splits for each run introduces additional variability rather than improving reproducibility. Different train-test splits expose the model to different samples, naturally causing performance variations. For reproducibility, the same data splits should be used across runs. Random data splitting is appropriate for cross-validation to assess generalization, but for reproducible comparison of model configurations, consistent data splits are essential.
Question 124
A company needs to train a model on sensitive financial data that must remain encrypted. Which SageMaker feature allows training on encrypted data?
A) SageMaker with S3 server-side encryption
B) SageMaker with client-side encryption only
C) Training without any encryption
D) Manual decryption before training
Answer: A
Explanation:
SageMaker with S3 server-side encryption enables training on encrypted data while maintaining security. When training data is stored in S3 with server-side encryption (SSE-S3, SSE-KMS, or SSE-C), SageMaker can directly access and decrypt the data during training using appropriate IAM permissions and encryption keys. The data remains encrypted at rest in S3 and during transit to training instances through TLS. SageMaker handles decryption transparently, allowing training on sensitive data without manual intervention while maintaining compliance with security requirements.
Option B is incorrect because relying solely on client-side encryption without SageMaker support would prevent training access to the data. Client-side encryption means data is encrypted before upload to S3, and AWS services cannot decrypt it without the client-provided keys. While client-side encryption provides strong security, SageMaker needs to decrypt data to perform training. Server-side encryption with proper key management provides security while enabling SageMaker to access encrypted data for training.
Option C is incorrect because training without encryption on sensitive financial data violates security best practices and likely regulatory compliance requirements. Financial data typically contains personally identifiable information and confidential business information that must be protected. Storing and processing unencrypted sensitive data creates security vulnerabilities and exposure risks. Encryption at rest and in transit is essential for protecting sensitive financial information.
Option D is incorrect because manual decryption before training creates security risks and operational overhead. Manually decrypting data and storing it unencrypted for training exposes sensitive information unnecessarily. This approach defeats the purpose of encryption and creates windows of vulnerability. SageMaker’s integrated encryption support eliminates the need for manual decryption while maintaining security throughout the training process.
Question 125
A model trained for sentiment analysis in English needs to be adapted for Spanish with limited Spanish training data. Which technique would be most effective?
A) Train from scratch on Spanish data only
B) Transfer learning from the English model
C) Use only machine translation to English
D) Apply random initialization for Spanish
Answer: B
Explanation:
Transfer learning from the English model is most effective for adapting sentiment analysis to Spanish with limited training data. Transfer learning leverages knowledge learned from the English model, where lower layers capture general linguistic features applicable across languages. Fine-tuning the pre-trained model on limited Spanish data allows it to adapt language-specific features while retaining general sentiment understanding. This approach requires significantly less Spanish training data than training from scratch and typically achieves better performance by building on existing knowledge.
Option A is incorrect because training from scratch on limited Spanish data would likely result in poor performance due to insufficient training examples. Sentiment analysis models require substantial data to learn robust language patterns and sentiment indicators. With limited Spanish data, a model trained from scratch would underfit and fail to capture the complexity of sentiment expression. Transfer learning overcomes data limitations by starting with pre-learned features.
Option C is incorrect because relying solely on machine translation to English introduces translation errors that degrade sentiment analysis accuracy. Translation can alter sentiment expressions, lose cultural nuances, and introduce artifacts. Idioms and sentiment indicators often don’t translate directly. Processing Spanish text natively with a Spanish-adapted model provides better accuracy than the translation-analysis pipeline, which compounds errors from both stages.
Option D is incorrect because random initialization for Spanish training discards all valuable knowledge from the English model. Random initialization requires learning everything from scratch, which is inefficient and requires large amounts of training data. With limited Spanish data, randomly initialized models would significantly underperform compared to transfer learning. Random initialization makes sense only when target and source domains are completely unrelated.
Question 126
A machine learning pipeline needs to automatically retrain when model performance drops below 85% accuracy. Which SageMaker feature combination enables this automated retraining trigger?
A) SageMaker Model Monitor with CloudWatch alarms and Lambda
B) SageMaker Experiments with manual checks
C) SageMaker Debugger during initial training only
D) SageMaker Ground Truth for data collection
Answer: A
Explanation:
SageMaker Model Monitor combined with CloudWatch alarms and Lambda provides automated retraining triggers based on performance thresholds. Model Monitor continuously evaluates deployed model performance metrics and publishes them to CloudWatch. CloudWatch alarms can be configured to trigger when accuracy drops below 85%, invoking Lambda functions that automatically initiate SageMaker training jobs. This serverless architecture creates a closed-loop system where performance degradation automatically triggers model updates without manual intervention.
Option B is incorrect because SageMaker Experiments with manual checks requires human monitoring and intervention to detect performance drops and initiate retraining. Manual processes introduce delays, are prone to oversight, and don’t scale efficiently. Experiments helps organize training runs but doesn’t provide automated monitoring or triggering capabilities. For production systems requiring continuous performance assurance, automated monitoring and retraining are essential.
Option C is incorrect because SageMaker Debugger monitors training jobs during model development to identify issues like vanishing gradients or overfitting. Debugger operates during training, not after deployment in production. It doesn’t monitor deployed model performance or trigger retraining based on production accuracy metrics. Debugger helps optimize training but doesn’t provide the continuous production monitoring needed for automated retraining triggers.
Option D is incorrect because SageMaker Ground Truth is a data labeling service for creating training datasets through human annotation. While Ground Truth can support retraining by providing newly labeled data, it doesn’t monitor model performance or automatically trigger retraining workflows. Ground Truth focuses on data preparation, not production model monitoring or automated workflow orchestration.
Question 127
A dataset contains missing values that are not random but related to the value itself (e.g., high-income individuals not reporting income). Which type of missingness is this and how should it be handled?
A) Missing Completely At Random (MCAR); use mean imputation
B) Missing At Random (MAR); use simple deletion
C) Missing Not At Random (MNAR); use domain-informed imputation or modeling
D) No missing pattern; ignore the missingness
Answer: C
Explanation:
This scenario describes Missing Not At Random (MNAR), where the probability of missingness depends on the unobserved value itself. When high-income individuals systematically don’t report income because it’s high, the missingness is informative and related to the missing value. MNAR requires careful handling through domain-informed imputation methods, modeling the missingness mechanism explicitly, or using specialized algorithms that account for informative missingness. Ignoring MNAR or using simple imputation methods introduces significant bias because the missing data pattern carries information about the underlying values.
Option A is incorrect because Missing Completely At Random (MCAR) occurs when missingness is unrelated to any observed or unobserved data—essentially random luck. In the scenario described, missingness is systematically related to the income value itself, violating MCAR assumptions. Mean imputation for MNAR data introduces severe bias because it ignores the systematic relationship between missingness and value. MCAR is the simplest missingness pattern but rarely occurs in real-world data.
Option B is incorrect because Missing At Random (MAR) occurs when missingness depends on observed variables but not on the missing value itself. For example, if women were more likely to not report income regardless of actual income level, that would be MAR (depends on observed gender). The scenario describes missingness depending on the unobserved income value itself, which is MNAR. Simple deletion with MNAR creates bias by systematically removing high-income observations.
Option D is incorrect because the scenario explicitly describes a systematic missing pattern where missingness relates to the value itself. Ignoring this pattern and treating it as random would introduce substantial bias in analysis and model training. The missing data carries important information—knowing that income is missing suggests it’s likely high. Proper analysis must account for this informative missingness pattern.
Question 128
A neural network model for image classification needs to be deployed on edge devices with limited computational resources. Which SageMaker feature optimizes models for edge deployment?
A) SageMaker Neo for model compilation
B) SageMaker Autopilot for model selection
C) SageMaker Clarify for model analysis
D) SageMaker Feature Store for data management
Answer: A
Explanation:
SageMaker Neo optimizes machine learning models for deployment on edge devices with limited computational resources. Neo compiles trained models to run up to twice as fast with reduced memory footprint by optimizing the model for specific hardware platforms (ARM, Intel, NVIDIA). Neo converts models from various frameworks (TensorFlow, PyTorch, MXNet) into optimized executables tailored for target devices. This compilation process includes operator fusion, memory optimization, and hardware-specific optimizations that enable efficient inference on resource-constrained edge devices.
Option B is incorrect because SageMaker Autopilot automates the machine learning workflow by automatically trying different algorithms and hyperparameters to find the best model. Autopilot focuses on model development and selection during training, not optimization for edge deployment. While Autopilot can identify effective models, it doesn’t perform the hardware-specific compilation and optimization that Neo provides for edge devices with computational constraints.
Option C is incorrect because SageMaker Clarify detects bias in models and provides explainability for predictions through feature attribution. Clarify helps ensure fairness and interpretability but doesn’t optimize models for computational efficiency or edge deployment. Clarify analyzes model behavior for governance purposes rather than transforming models for efficient execution on resource-constrained hardware.
Option D is incorrect because SageMaker Feature Store manages feature data for training and inference, providing centralized feature storage and serving. Feature Store addresses data management challenges but doesn’t optimize model execution for edge devices. While Feature Store can support edge applications by providing features, it doesn’t perform model compilation or optimization for hardware-constrained environments.
Question 129
A company wants to build a search system that retrieves similar product images based on visual content rather than metadata. Which approach is most appropriate?
A) Keyword-based search on product descriptions
B) Content-based image retrieval using embedding vectors
C) Collaborative filtering based on user behavior
D) Rule-based matching on product categories
Answer: B
Explanation:
Content-based image retrieval using embedding vectors is the most appropriate approach for finding visually similar product images. This method uses deep learning models (typically CNNs) to extract high-dimensional feature vectors (embeddings) that capture visual characteristics like colors, textures, shapes, and patterns. Similar images produce similar embeddings, enabling similarity search by computing distances between embedding vectors. This approach directly analyzes visual content rather than relying on textual metadata, making it effective for finding products that look similar regardless of their textual descriptions.
Option A is incorrect because keyword-based search on product descriptions relies on textual metadata rather than visual content. This approach cannot identify visually similar products unless they share similar text descriptions. Products that look alike but have different descriptions would not be retrieved. Keyword search also suffers when descriptions are missing, incomplete, or inconsistent. The requirement specifically asks for visual content-based retrieval, not text-based search.
Option C is incorrect because collaborative filtering recommends items based on user behavior patterns and preferences, not visual similarity. Collaborative filtering identifies products that users with similar preferences tend to like, which may or may not be visually similar. For example, users who buy red dresses might also buy blue shoes, but these items aren’t visually similar. Collaborative filtering addresses recommendation based on behavior, not content similarity.
Option D is incorrect because rule-based matching on product categories uses predefined categorical metadata rather than analyzing visual content. Products in the same category might look completely different, while visually similar products might be in different categories. Rule-based approaches lack the flexibility and nuance of content-based methods that directly analyze visual features to determine similarity.
Question 130
A data scientist notices that adding more training data improves validation performance significantly. Which diagnosis does this indicate and what action should be taken?
A) Overfitting; reduce model complexity
B) High bias; collect more diverse data and increase model capacity
C) Correct behavior; continue adding more data
D) Data leakage; remove features
Answer: C
Explanation:
When adding more training data significantly improves validation performance, this indicates correct learning behavior where the model benefits from additional examples. This situation suggests the model has sufficient capacity but was previously limited by training data quantity. The appropriate action is to continue collecting more training data as it demonstrably improves generalization. This pattern indicates healthy learning where more examples help the model learn more robust patterns and better generalize to unseen data.
Option A is incorrect because overfitting manifests as good training performance but poor validation performance, with the gap widening as training progresses. If the model were overfitting, adding more training data would have minimal effect on validation performance or might even worsen it if the model simply memorizes more examples. The scenario describes validation performance improving with more data, which is the opposite of overfitting behavior.
Option B is incorrect because while the description matches high bias symptoms (improving with more data) initially, the recommendation to increase model capacity is premature. High bias means the model is too simple to capture data patterns, but if validation performance is improving significantly with more data, the current model capacity may be adequate—it simply needs more examples. Increasing complexity without first maximizing data utilization could lead to overfitting.
Option D is incorrect because data leakage occurs when training data contains information about the target variable that wouldn’t be available at prediction time, causing artificially high performance. Data leakage doesn’t improve with more data—it maintains artificially inflated performance regardless of dataset size. The scenario describes legitimate performance improvement from additional examples, not the consistently unrealistic performance characteristic of data leakage.
Question 131
A machine learning model needs to predict customer lifetime value based on purchase history, demographics, and behavior patterns. Which type of learning problem is this?
A) Binary classification
B) Multi-class classification
C) Regression
D) Clustering
Answer: C
Explanation:
Predicting customer lifetime value is a regression problem because it involves predicting a continuous numerical value (expected future revenue from a customer). Regression models output numerical predictions on a continuous scale rather than discrete categories. Customer lifetime value typically represents monetary amounts that can take any value within a range, making regression the appropriate approach. Common regression algorithms for this problem include linear regression, gradient boosting, or neural networks configured for regression output.
Option A is incorrect because binary classification predicts one of two discrete categories (yes/no, true/false, 0/1). Customer lifetime value is not a binary outcome but a continuous monetary value. Binary classification would only be appropriate if the problem were reformulated as something like “will customer lifetime value exceed $1000?” which reduces the rich numerical information to a simple yes/no decision.
Option B is incorrect because multi-class classification predicts one category from three or more discrete options (like product category, customer segment, or risk level). Customer lifetime value is a continuous numerical prediction, not a categorical assignment. While you could discretize lifetime value into categories (low/medium/high), this would lose valuable information and granularity that regression preserves.
Option D is incorrect because clustering is an unsupervised learning technique that groups similar data points without predicting specific outcomes. Clustering would identify customer segments based on similarities but wouldn’t predict the specific lifetime value for individual customers. Clustering provides descriptive groupings rather than predictive numerical outputs, making it unsuitable for directly predicting continuous values.
Question 132
A training job fails repeatedly with “ResourceLimitExceeded” errors. The dataset is 2 TB and the instance type is ml.m5.large. What is the most appropriate solution?
A) Use a larger instance type with more memory and compute
B) Reduce the learning rate
C) Add more regularization
D) Change the activation function
Answer: A
Explanation:
Using a larger instance type with more memory and compute resources is the appropriate solution for ResourceLimitExceeded errors with large datasets. The ml.m5.large instance has limited memory (8 GB) insufficient for processing 2 TB of data. Upgrading to instances like ml.m5.4xlarge, ml.m5.12xlarge, or compute-optimized instances provides more RAM and CPU to handle large-scale data processing and model training. Alternatively, using Pipe mode or Fast File mode can stream data without loading everything into memory, but increasing instance capacity directly addresses the resource limitation.
Option B is incorrect because reducing the learning rate affects optimization dynamics and convergence speed but doesn’t address resource limitations. Learning rate controls how much model weights are updated during training but has no impact on memory requirements or compute capacity. ResourceLimitExceeded errors indicate insufficient hardware resources, not optimization hyperparameter issues. Adjusting learning rate would not resolve the fundamental resource constraint.
Option C is incorrect because adding more regularization helps prevent overfitting by constraining model complexity but doesn’t reduce memory or compute requirements during training. Regularization adds penalty terms to the loss function but doesn’t change the fundamental resource needs for processing large datasets. ResourceLimitExceeded errors stem from hardware limitations, not model complexity issues that regularization addresses.
Option D is incorrect because changing activation functions affects how neurons compute outputs and can impact training dynamics, but activation functions don’t significantly change memory or compute requirements. Different activations (ReLU, sigmoid, tanh) have similar computational costs. ResourceLimitExceeded errors indicate the instance cannot handle the data volume and processing requirements, which activation function changes cannot resolve.
Question 133
A model trained on data from urban areas performs poorly in rural areas. Which technique would help the model generalize better across different geographic regions?
A) Train only on urban data and ignore rural
B) Collect rural data and retrain with combined dataset
C) Increase model complexity significantly
D) Remove all geographic features
Answer: B
Explanation:
Collecting rural data and retraining with a combined dataset that includes both urban and rural examples enables the model to learn patterns from both environments. This addresses the distribution shift between urban and rural areas by exposing the model to the full diversity of conditions it will encounter in production. The combined dataset allows the model to learn features and patterns that generalize across geographic contexts, improving performance in both settings rather than specializing on one.
Option A is incorrect because training only on urban data and ignoring rural areas perpetuates the poor rural performance problem. This approach accepts geographic bias and limits the model’s applicability. If the model needs to serve rural areas, deliberately excluding rural training data ensures continued failure in those regions. This creates a model that only works for a subset of the target population.
Option C is incorrect because simply increasing model complexity doesn’t address the fundamental problem of training data not representing rural conditions. A more complex model trained only on urban data would still fail in rural areas because it has never seen examples of rural patterns, regardless of capacity. Complexity without relevant training data leads to overfitting on urban examples rather than generalizing across geographies.
Option D is incorrect because removing all geographic features eliminates potentially valuable information about how patterns vary by location. While removing explicit geography might reduce overfitting to specific locations, it doesn’t address the underlying distribution shift. The model would still be trained on urban-biased data and lack exposure to rural patterns. Better solutions involve diversifying training data rather than removing informative features.
Question 134
A company wants to perform hyperparameter tuning for a model but has a limited budget. Which hyperparameter optimization strategy would be most cost-effective?
A) Grid search covering all possible combinations
B) Random search with limited iterations
C) Bayesian optimization with SageMaker Automatic Model Tuning
D) Manual tuning without automation
Answer: C
Explanation:
Bayesian optimization with SageMaker Automatic Model Tuning is the most cost-effective strategy for budget-constrained hyperparameter tuning. Bayesian optimization intelligently selects hyperparameter combinations to try based on previous results, focusing computational resources on promising regions of the hyperparameter space. This approach typically finds good solutions with fewer training jobs than grid or random search. SageMaker’s implementation uses sophisticated optimization algorithms to maximize performance while minimizing the number of expensive training runs required.
Option A is incorrect because grid search exhaustively evaluates all combinations of predefined hyperparameter values, which is extremely expensive for high-dimensional hyperparameter spaces. With even moderate numbers of hyperparameters and values, grid search requires hundreds or thousands of training jobs. For budget-constrained scenarios, grid search wastes resources evaluating unpromising hyperparameter combinations that Bayesian methods would intelligently avoid.
Option B is incorrect because while random search is more efficient than grid search and requires fewer iterations, it still samples the hyperparameter space randomly without learning from previous results. Random search might accidentally miss good configurations or waste trials on poor regions. Although random search with limited iterations fits budget constraints better than grid search, Bayesian optimization achieves better results with the same number of trials by learning from each experiment.
Option D is incorrect because manual tuning without automation is time-consuming, inefficient, and unlikely to find optimal configurations. Manual tuning requires expert intuition and many trial-and-error iterations. Humans cannot efficiently explore high-dimensional spaces or identify subtle hyperparameter interactions. Manual approaches consume data scientist time (expensive resource) and typically achieve worse results than automated methods that systematically explore the hyperparameter space.
Question 135
A dataset contains 1 million samples with 10,000 features, most of which are irrelevant. Which technique would identify the most informative features before training?
A) Train on all features without selection
B) Feature selection using mutual information or correlation
C) Randomly remove 90% of features
D) Convert all features to categorical
Answer: B
Explanation:
Feature selection using mutual information or correlation identifies the most informative features by measuring their relationship with the target variable. Mutual information quantifies how much knowing a feature’s value reduces uncertainty about the target, while correlation measures linear relationships. These methods rank features by relevance, allowing removal of uninformative features before training. This reduces dimensionality, improves model training speed, reduces overfitting risk, and often improves performance by eliminating noise from irrelevant features.
Option A is incorrect because training on all 10,000 features when most are irrelevant introduces computational expense, overfitting risk, and potential performance degradation. Irrelevant features add noise that obscures important patterns, making learning harder. High dimensionality increases training time and memory requirements substantially. Including irrelevant features violates the principle of parsimony and reduces model interpretability without providing value.
Option C is incorrect because randomly removing 90% of features risks discarding truly informative features while retaining irrelevant ones. Random selection makes no distinction between valuable signal and useless noise. This approach might accidentally improve performance if most features are truly irrelevant, but it’s unreliable and provides no systematic basis for feature retention decisions. Principled feature selection methods based on information content vastly outperform random selection.
Option D is incorrect because converting all features to categorical doesn’t identify informative features or reduce dimensionality—it may actually increase dimensionality through one-hot encoding. Converting numerical features to categorical loses information about magnitude and ordering. This transformation doesn’t address the core problem of identifying which features matter for prediction. Feature type conversion is a preprocessing decision separate from feature selection.
Question 136
A model deployed in production receives prediction requests with features in different units than training data (meters vs. kilometers). What preprocessing step should be applied consistently?
A) Feature scaling/normalization
B) One-hot encoding
C) Data augmentation
D) Dimensionality reduction
Answer: A
Explanation:
Feature scaling or normalization should be applied consistently to ensure features are in the same units and ranges as training data. When training data uses meters but production data arrives in kilometers, scaling transforms the values to the expected range. The same scaling parameters (mean, standard deviation, min, max) used during training must be applied to production data to maintain consistency. This preprocessing ensures the model receives inputs in the distribution it was trained on, preventing prediction errors from unit mismatches.
Option B is incorrect because one-hot encoding transforms categorical variables into binary vectors but doesn’t address numerical feature scaling or unit conversion. One-hot encoding is used when features represent discrete categories without ordinal relationships, not for handling numerical features with different units. The scenario describes a units problem with numerical measurements, not categorical encoding needs.
Option C is incorrect because data augmentation creates synthetic training examples by applying transformations like rotations, crops, or noise to increase dataset diversity. Augmentation is applied during training to improve model robustness, not during inference on production data. Applying augmentation to production inputs would alter the actual data being predicted on, producing incorrect results. The scenario requires standardization, not diversification.
Option D is incorrect because dimensionality reduction techniques like PCA transform features into a lower-dimensional space but don’t address unit conversion problems. While dimensionality reduction could be part of the preprocessing pipeline, it doesn’t solve the fundamental issue of production data having different units than training data. The problem requires unit standardization through scaling, not dimension reduction.
Question 137
A company needs to process customer support tickets and automatically route them to appropriate departments. Which machine learning approach is most suitable?
A) Regression to predict routing scores
B) Multi-class text classification
C) Clustering without labels
D) Anomaly detection
Answer: B
Explanation:
Multi-class text classification is the most suitable approach for automatically routing customer support tickets to departments. This supervised learning task classifies text into one of multiple predefined categories (departments like billing, technical support, sales, etc.). The model learns from historical tickets with known department assignments to predict the appropriate department for new tickets. Text classification handles the natural language processing required to understand ticket content and assign them to the correct destination.
Option A is incorrect because regression predicts continuous numerical values rather than categorical department assignments. While you could theoretically assign numerical scores to departments and use regression, this creates artificial ordering relationships between departments that don’t exist. Department assignment is inherently categorical—tickets belong to specific departments, not points on a continuous scale. Classification is the natural formulation for categorical assignments.
Option C is incorrect because clustering is unsupervised and groups similar tickets without predicting specific department assignments. Clustering might identify natural ticket groupings but doesn’t map them to known departments. Without labeled training data, clustering can’t learn which groups correspond to which departments. Automated routing requires predicting specific department labels, which supervised classification provides but unsupervised clustering doesn’t.
Option D is incorrect because anomaly detection identifies unusual or outlying examples rather than categorizing normal tickets into departments. Anomaly detection might identify strange or suspicious tickets but doesn’t perform the routing function of assigning tickets to appropriate departments. The goal is categorization of typical tickets, not identification of unusual ones. Routing requires multi-class classification, not outlier detection.
Question 138
A machine learning model shows identical training and validation performance with very high error rates. What problem does this indicate and what solution is appropriate?
A) Overfitting; apply regularization
B) Underfitting; increase model complexity or improve features
C) Perfect model; no changes needed
D) Data leakage; remove leaked features
Answer: B
Explanation:
Identical training and validation performance with high error rates indicates underfitting, where the model is too simple to capture underlying data patterns. When both training and validation errors are high and similar, the model lacks capacity to learn complex relationships in the data. Solutions include increasing model complexity (more layers, more neurons, higher polynomial degree), engineering better features, or trying more sophisticated algorithms. The goal is to give the model sufficient capacity to learn the patterns present in the data.
Option A is incorrect because overfitting occurs when training performance is much better than validation performance, with low training error but high validation error. The scenario describes both errors being high and similar, which is the opposite pattern from overfitting. Applying regularization to an underfitting model would further constrain it, worsening performance by making an already too-simple model even simpler.
Option C is incorrect because identical performance with very high error rates indicates a problematic model that fails to learn useful patterns. A perfect model would show low error rates on both training and validation sets, demonstrating effective learning and good generalization. High error rates mean the model cannot adequately perform its task and requires improvement through increased capacity or better feature engineering.
Option D is incorrect because data leakage causes artificially high performance (unrealistically low error) on both training and validation sets when validation data inadvertently contains information about training targets. The scenario describes high error rates, which is opposite to data leakage symptoms. Data leakage creates suspiciously good results, not poor performance. High errors on both sets indicate the model genuinely struggles to learn patterns, not that it has inappropriate access to target information.
Question 139
A machine learning team needs to compare the performance of 10 different model architectures. Which SageMaker feature helps organize and compare these experiments efficiently?
A) SageMaker Experiments for tracking and comparison
B) SageMaker Ground Truth for labeling
C) SageMaker Neo for optimization
D) SageMaker Clarify for bias detection
Answer: A
Explanation:
SageMaker Experiments provides comprehensive capabilities for organizing, tracking, and comparing multiple model architectures efficiently. Experiments automatically captures training parameters, metrics, artifacts, and metadata for each training run, organizing them into experiments and trials. The service provides visualization and comparison tools that allow teams to analyze performance differences across the 10 architectures side-by-side. This makes it easy to identify the best-performing model and understand how different architectural choices impact performance metrics.
Option B is incorrect because SageMaker Ground Truth is a data labeling service for creating training datasets through human annotation and active learning. Ground Truth addresses data preparation needs but doesn’t provide experiment tracking or model comparison capabilities. While high-quality labeled data is important for training, Ground Truth doesn’t help organize or compare results from different model architectures during experimentation.
Option C is incorrect because SageMaker Neo compiles and optimizes trained models for deployment on specific hardware platforms. Neo focuses on inference optimization after model training is complete, not on organizing and comparing experiments during model development. While Neo can optimize any of the 10 models for deployment, it doesn’t provide the experiment tracking and comparison functionality needed to evaluate which architecture performs best.
Option D is incorrect because SageMaker Clarify detects bias and provides explainability for individual models but doesn’t organize or compare multiple experiments systematically. Clarify helps understand model behavior for fairness and interpretability but doesn’t provide the experiment management infrastructure needed to track parameters and metrics across 10 different architectures. Clarify analyzes models individually rather than facilitating comparison across multiple candidates.
Question 140
A company wants to detect when their deployed model’s predictions become unreliable due to changes in input data distribution over time. Which combination of metrics should be monitored?
A) Only training loss values
B) Data drift metrics and prediction quality metrics
C) Instance CPU utilization only
D) Number of API calls per minute
Answer: B
Explanation:
Monitoring both data drift metrics and prediction quality metrics provides comprehensive detection of when model predictions become unreliable due to distribution changes. Data drift metrics detect when input feature distributions shift from training baseline, indicating the model encounters data it wasn’t designed for. Prediction quality metrics (accuracy, precision, recall) track actual model performance. Together, these metrics provide early warning when input patterns change and confirm whether those changes impact prediction reliability. SageMaker Model Monitor can track both metric types automatically.
Option A is incorrect because training loss values are historical metrics from model development that don’t change once training completes. Training loss measures how well the model fit training data but provides no information about production model performance or whether current input data matches training distribution. Monitoring only training loss gives no insight into production model behavior or data drift issues.
Option C is incorrect because instance CPU utilization measures computational resource usage but doesn’t indicate prediction reliability or data quality. High CPU utilization might indicate heavy request load but says nothing about whether predictions are accurate or whether input data has shifted from training distribution. Infrastructure metrics like CPU are important for operational health but don’t detect data drift or model degradation.
Option D is incorrect because the number of API calls per minute measures request volume and system load but provides no information about prediction quality or data distribution changes. Call volume indicates usage patterns but doesn’t reveal whether the model produces reliable predictions or whether input characteristics have shifted. A model could receive many API calls while producing poor predictions on drifted data.
