Google Professional Machine Learning Engineer Exam Dumps and Practice Test Questions Set 3 Q31-45
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Question 31
You are designing a machine learning model to forecast electricity demand in a city. The data is hourly, shows strong seasonality, and occasionally has missing readings due to sensor outages. Which approach is most suitable?
A) Simple linear regression ignoring temporal dependencies
B) Seasonal ARIMA (SARIMA) with imputation for missing data
C) Drop all missing data and fit a moving average model
D) Use k-means clustering on raw readings
Answer: B
Explanation:
Simple linear regression, ignoring temporal dependence, is inadequate for forecasting electricity demand because it assumes a linear relationship between predictors and the target without accounting for time-based patterns. Electricity demand is strongly influenced by daily, weekly, and seasonal cycles. Ignoring these patterns would lead to systematic errors and poor predictive performance. Additionally, regression cannot easily handle gaps caused by missing sensor readings, which are common in real-world datasets. This approach does not capture autocorrelation or cyclic trends inherent in energy consumption data, making it unsuitable for accurate forecasting.
Seasonal ARIMA (SARIMA) with imputation for missing data is the most suitable approach. SARIMA models explicitly incorporate seasonal components alongside autoregressive and moving average terms, capturing both short-term trends and long-term periodic patterns. Missing data can be addressed using imputation techniques such as interpolation, forward-filling, or model-based imputation to prevent gaps from affecting model training. By modeling temporal structure and seasonality, SARIMA allows accurate forecasts of electricity demand, even when occasional sensor failures occur. Its parameters can be fine-tuned to reflect observed patterns, enabling robust short-term and long-term predictions. SARIMA also provides interpretable coefficients, which can help analysts understand demand drivers.
Dropping all missing data and fitting a moving average model reduces the dataset size and may introduce bias. Missing readings are not random; for example, sensor outages may coincide with peak usage periods or adverse weather events. Removing these observations could distort seasonality and trend estimates. Moving averages can smooth noise, but do not explicitly capture temporal dependencies or seasonality. As a result, this approach would produce forecasts that lag behind true patterns and fail to reflect real-world demand dynamics.
Using k-means clustering on raw readings is primarily an unsupervised method for grouping similar patterns. While it can identify typical usage profiles or cluster periods of high and low demand, it does not provide a predictive model for future electricity consumption. Clustering cannot account for temporal dependencies or handle missing values in a way that produces accurate forecasts. This approach may be useful for exploratory analysis but is insufficient for operational forecasting.
SARIMA with imputation is the most appropriate choice because it explicitly models both trend and seasonality, incorporates autocorrelation, and can handle missing data without discarding observations. It provides accurate, interpretable forecasts that are robust to gaps caused by sensor outages, making it suitable for operational decision-making in energy management. This approach balances statistical rigor, predictive power, and practical feasibility.
Question 32
You are building a recommendation system for an e-commerce platform where users often purchase seasonal products. You want the system to account for temporal patterns in user behavior. Which approach is most appropriate?
A) Standard collaborative filtering ignoring time
B) Time-aware collaborative filtering using sequence modeling
C) Recommend products randomly
D) Only recommend historically popular products
Answer: B
Explanation:
Standard collaborative filtering, ignoring time, relies solely on historical user-item interactions to infer similarities. While it can capture general preferences, it does not account for temporal dynamics such as seasonal trends, changing tastes, or evolving product availability. For instance, a user may buy holiday decorations only in December. Ignoring time would cause the system to recommend products year-round, resulting in irrelevant suggestions and reduced engagement. Standard collaborative filtering is insufficient when user behavior varies over time, as it treats all past interactions equally, regardless of recency or seasonal relevance.
Time-aware collaborative filtering using sequence modeling is highly suitable. This approach incorporates temporal information into the recommendation process, either by weighting recent interactions more heavily or by using sequence models like recurrent neural networks or temporal embeddings. Sequence modeling captures patterns such as recurring seasonal purchases, trends, or short-term interests. For example, it can recommend swimsuits in summer or winter coats in colder months based on observed purchasing sequences. By leveraging temporal context, the system provides more relevant, timely recommendations, improving user satisfaction and engagement while maintaining personalization.
Recommending products randomly provides exploration but lacks personalization and temporal relevance. While it may expose users to new items, the majority of recommendations will likely be irrelevant, particularly for seasonal products. Random recommendations do not capture patterns or trends and fail to optimize for engagement or sales, making this approach inefficient in practical e-commerce applications.
Only recommending historically popular products ignores individual user preferences and temporal dynamics. Popularity-based recommendations may work for commonly desired items but fail to capture seasonal trends or niche interests. Users may receive irrelevant suggestions if products are out of season or no longer aligned with their needs. This approach does not optimize personalization or adapt to changing user behavior over time.
Time-aware collaborative filtering using sequence modeling is the most appropriate approach because it captures temporal patterns in user behavior while maintaining personalization. By considering the timing and sequence of past purchases, the system can recommend products at the right time, increasing relevance, engagement, and overall user satisfaction. This approach balances historical preference with evolving seasonal trends effectively.
Question 33
You are building a model to detect fraudulent insurance claims. The dataset is extremely imbalanced, with only 1% of claims being fraudulent. Which modeling strategy is most suitable?
A) Train a standard classifier without addressing class imbalance
B) Apply resampling techniques and evaluate with precision, recall, and F1 score
C) Remove non-fraudulent claims to balance the dataset
D) Use mean squared error as the evaluation metric
Answer: B
Explanation:
Training a standard classifier without addressing class imbalance is ineffective in this scenario. With only 1% of claims being fraudulent, the classifier will likely predict all claims as non-fraudulent, achieving high accuracy but failing to detect any fraudulent activity. Accuracy is misleading in such imbalanced settings and does not reflect the model’s ability to identify minority-class events. Without mechanisms to handle imbalance, the model cannot learn patterns specific to fraud, rendering it ineffective for practical deployment where detecting fraudulent claims is critical.
Applying resampling techniques and evaluating with precision, recall, and F1 score is highly suitable. Resampling can involve oversampling fraudulent claims to increase their representation or undersampling non-fraudulent claims to balance class distribution. Synthetic data generation methods, such as SMOTE, can also be used to create realistic fraudulent examples. Precision measures the correctness of predicted fraud cases, recall captures the proportion of actual frauds detected, and F1 score balances these metrics, providing a comprehensive evaluation for rare-event detection. This strategy ensures the model learns to identify fraud effectively while providing interpretable performance metrics.
Removing non-fraudulent claims to balance the dataset reduces the available data and may discard important patterns of legitimate claims. This approach introduces bias and may prevent the model from learning realistic relationships between features and outcomes. Additionally, it does not fully resolve evaluation challenges because standard metrics like accuracy remain misleading. While it artificially balances classes, it sacrifices valuable information, reducing model generalization and reliability.
Using mean squared error (MSE) as the evaluation metric is inappropriate because MSE is designed for regression problems, not classification. It does not capture performance on minority classes, nor does it differentiate between false positives and false negatives. MSE cannot effectively guide model selection or tuning in imbalanced classification scenarios like fraud detection, making it unsuitable for evaluating the system.
Resampling combined with precision, recall, and F1 score evaluation is the most appropriate strategy for detecting fraudulent insurance claims. It addresses the class imbalance, ensures the model learns relevant patterns for rare events, and provides metrics that accurately reflect performance on minority-class cases. This approach maximizes the likelihood of detecting fraud while maintaining reliability and interpretability.
Question 34
You are building a computer vision model to identify defects in industrial products. The dataset contains high-resolution images, but defects occupy very small regions, making them difficult to detect. Which approach is most appropriate?
A) Use a standard convolutional neural network (CNN) on downsampled images
B) Apply a region-based CNN (R-CNN) or Faster R-CNN object detection framework
C) Train a fully connected network on raw pixel values
D) Use k-means clustering to identify defective regions
Answer: B
Explanation:
Using a standard convolutional neural network on downsampled images simplifies computation but can lead to loss of critical information. Small defects may disappear or become blurred during downsampling, making them harder for the network to detect. CNNs trained on low-resolution images can capture general patterns but are ineffective when the target signal is small relative to the image size. The model may overlook subtle defects, resulting in poor accuracy and missed detections in an industrial context where precision is critical.
Applying a region-based CNN (R-CNN) or Faster R-CNN object detection framework is highly suitable for this task. These models are specifically designed to locate and classify small objects within larger images. R-CNN first generates region proposals and then classifies each region, while Faster R-CNN integrates region proposal networks for faster processing. This approach enables the network to focus on specific areas likely to contain defects, preserving spatial details and improving detection accuracy. High-resolution images can be processed without downsampling, maintaining the visibility of small defects. Object detection frameworks also provide bounding boxes, which support actionable decisions in industrial quality control, such as marking defective items for removal or further inspection.
Training a fully connected network on raw pixel values is impractical for high-resolution images. Fully connected networks treat each pixel as independent, requiring a massive number of parameters and computational resources. They also ignore spatial relationships, which are crucial for detecting localized defects. As a result, this approach is inefficient, prone to overfitting, and unlikely to achieve reliable defect detection.
Using k-means clustering to identify defective regions may provide exploratory insights, but it does not directly perform object detection. Clustering can group similar pixel patterns, potentially highlighting unusual areas, but it cannot classify or precisely locate defects. Additionally, k-means is sensitive to noise and cannot learn hierarchical features or context, making it insufficient for industrial defect detection.
Region-based CNNs or Faster R-CNN are the most appropriate approach because they combine localization and classification, maintain high-resolution detail, and effectively detect small defects in large images. By focusing on regions of interest, these frameworks ensure high accuracy, provide interpretable results, and are suitable for automated quality control in industrial settings.
Question 35
You are building a machine learning model for predicting customer churn in a subscription service. The dataset is highly imbalanced, with very few churned customers. Which approach is most effective?
A) Train a standard classifier without addressing imbalance
B) Apply oversampling or class weighting and evaluate with precision, recall, and F1 score
C) Remove non-churned customers to balance the dataset
D) Use mean squared error as the evaluation metric
Answer: B
Explanation:
Training a standard classifier without addressing imbalance is ineffective because the model will be biased toward the majority class. In a highly imbalanced churn dataset, predicting all customers as non-churned may result in high accuracy but zero ability to detect actual churn. Standard classifiers without adjustment for imbalance fail to learn patterns associated with the minority class, which is the primary concern in churn prediction. Relying on accuracy alone is misleading and provides a false sense of model performance.
Applying oversampling or class weighting and evaluating with precision, recall, and F1 score is highly suitable. Oversampling increases the representation of churned customers, while class weighting penalizes misclassification of the minority class more heavily. These methods ensure the model focuses on identifying churn events while maintaining performance on the majority class. Precision measures the proportion of correctly predicted churns among all predicted churns, recall measures the proportion of actual churns correctly identified, and the F1 score balances both metrics. This combination allows a comprehensive evaluation of the model’s effectiveness in detecting rare events, which is crucial for business decisions aimed at customer retention.
Removing non-churned customers to balance the dataset reduces available data and may discard important patterns. While it balances class proportions, it introduces bias and limits generalization. Essential information about normal customer behavior is lost, which can affect the model’s ability to distinguish between churned and non-churned users accurately. This approach may artificially improve performance metrics but decrease real-world applicability.
Using mean squared error (MSE) as the evaluation metric is inappropriate for classification problems. MSE is designed for regression tasks, measuring the average squared difference between predicted and actual numeric values. It does not capture the ability to detect minority classes, nor does it account for false positives or false negatives. Evaluating churn prediction with MSE would fail to reflect model performance in identifying rare churn events.
Oversampling or class weighting combined with precision, recall, and F1 score evaluation is the most effective strategy because it addresses class imbalance, improves the model’s ability to detect churn, and provides meaningful performance metrics. This approach ensures actionable insights for customer retention and supports reliable model deployment in real-world subscription services.
Question 36
You are designing a natural language processing model for named entity recognition (NER) in biomedical text. The dataset contains complex terminology and rare entities. Which approach is most appropriate?
A) Use a simple bag-of-words model with logistic regression
B) Use a transformer-based model such as BioBERT or SciBERT
C) Apply k-means clustering to tokenize entities
D) Use a standard RNN without pretrained embeddings
Answer: B
Explanation:
Using a simple bag-of-words model with logistic regression ignores context and sequential information. Bag-of-words represents text as unordered term frequencies, which is insufficient for NER tasks where the position of words, the surrounding context, and relationships between terms are crucial. In biomedical text, entities may be multi-word expressions with complex structures, and a bag-of-words model cannot capture these patterns. Logistic regression may classify frequent entities correctly but struggles with rare terms or ambiguous names, making it inadequate for biomedical NER.
Using a transformer-based model such as BioBERT or SciBERT is highly suitable. These models are pretrained on large-scale biomedical or scientific corpora, enabling them to capture domain-specific language, terminology, and contextual relationships. Transformers leverage attention mechanisms, allowing the model to focus on relevant words and their context for accurate entity recognition. Fine-tuning these models on a labeled biomedical NER dataset enables robust detection of rare entities, disambiguation of complex terms, and recognition of multi-word expressions. Pretrained embeddings from domain-specific models significantly improve performance compared to generic language models.
Applying k-means clustering to tokenize entities is inappropriate. Clustering can group similar tokens or embeddings, but does not provide sequential labeling or explicit entity recognition. It cannot identify entity boundaries, classify entity types, or handle ambiguous terms. Clustering is more suitable for exploratory analysis or embedding organization rather than supervised NER tasks, making it ineffective for biomedical text.
Using a standard RNN without pretrained embeddings is limited in handling complex biomedical language. While RNNs can model sequences, training from scratch without domain-specific embeddings requires vast amounts of labeled data. Rare entities are unlikely to be represented sufficiently in the dataset, resulting in poor generalization. RNNs also struggle with long-range dependencies and multi-word entities compared to transformer architectures.
Transformer-based models like BioBERT or SciBERT are the most appropriate approach because they combine pretrained domain-specific embeddings with attention mechanisms to accurately identify entities in biomedical text. They handle complex terminology, rare entities, and multi-word expressions effectively, providing state-of-the-art performance for NER in specialized domains.
Question 37
You are building a predictive maintenance system for industrial machinery using sensor data. Sensor readings are collected every second and include temperature, vibration, and pressure. The system must predict failures before they occur. Which modeling approach is most suitable?
A) Use a feedforward neural network on raw sensor readings
B) Apply a recurrent neural network (RNN) or LSTM to capture temporal dependencies
C) Aggregate sensor readings into daily averages and use linear regression
D) Use k-means clustering to group sensor readings
Answer: B
Explanation:
Using a feedforward neural network on raw sensor readings captures relationships between features but does not account for temporal dependencies. Predictive maintenance requires recognizing patterns over time, such as gradual changes in vibration or temperature that precede failure. Feedforward networks treat each input independently, ignoring sequential information and temporal correlations that are critical for early failure detection. While they may detect correlations at a single time point, they are unlikely to capture complex temporal dynamics that indicate impending failures.
Applying a recurrent neural network (RNN) or long short-term memory (LSTM) network is highly suitable. RNNs are designed to process sequential data and maintain a memory of past states, which allows them to model temporal dependencies in sensor readings. LSTMs, a variant of RNNs, include gating mechanisms that prevent vanishing or exploding gradients and allow learning of long-term dependencies. This is essential for predictive maintenance because failure patterns may develop gradually over extended periods. By analyzing sequences of sensor readings, LSTMs can detect subtle anomalies and trends indicative of potential failures, enabling early warning and preventive action.
Aggregating sensor readings into daily averages and using linear regression simplifies the problem but discards critical temporal information. Daily averaging may smooth over anomalies or rapid changes in sensor values that precede failures. Linear regression assumes a linear relationship between input features and the target, which is often insufficient for capturing complex interactions in machinery data. As a result, this approach may miss important signals, leading to delayed or inaccurate predictions.
Using k-means clustering to group sensor readings is primarily an unsupervised technique for pattern discovery. While clustering may reveal typical operating states or anomalies, it does not provide predictive capability for failures. Randomly grouping sensor readings cannot model sequential dependencies or provide actionable forecasts, making it unsuitable for predictive maintenance applications.
Recurrent neural networks or LSTMs are the most appropriate approach because they explicitly model temporal dependencies, maintain memory of past events, and can detect subtle patterns indicative of machinery failure. This allows the system to provide accurate, early warnings and supports proactive maintenance strategies, reducing downtime and operational costs.
Question 38
You are developing a computer vision system for autonomous vehicles. The model must detect pedestrians in real-time while maintaining high accuracy. Which approach is most appropriate?
A) Use a standard deep CNN for image classification
B) Apply a real-time object detection model such as YOLO or SSD
C) Downsample images and use k-means clustering to identify pedestrians
D) Train a fully connected network on raw pixels
Answer: B
Explanation:
Using a standard deep CNN for image classification identifies the presence of pedestrians in an image, but does not provide localization. Classification networks output a single label per image and cannot generate bounding boxes for detected pedestrians. Autonomous vehicles require precise spatial information to make safe decisions, and standard CNNs cannot provide real-time localization of multiple objects. Additionally, deep CNNs optimized for classification are computationally intensive, making real-time inference challenging, especially when high-resolution images are needed.
Applying a real-time object detection model such as YOLO (You Only Look Once) or SSD (Single Shot Detector) is highly suitable. These models combine localization and classification in a single forward pass, enabling real-time detection of multiple objects, including pedestrians, within high-resolution images. YOLO divides the image into a grid and predicts bounding boxes and class probabilities for each grid cell, while SSD detects objects at multiple scales with a single pass. Both approaches are optimized for speed and accuracy, maintaining low latency for real-time applications. Object detection frameworks provide bounding boxes and confidence scores, which are critical for autonomous vehicles to navigate safely and avoid collisions.
Downsampling images and using k-means clustering to identify pedestrians reduces spatial resolution and may obscure small or distant objects. It clusters groups of similar pixels, but cannot classify or locate pedestrians with precision. This approach is unsuitable for real-time autonomous navigation, where both accuracy and localization are critical. Clustering may identify regions of interest, but does not provide actionable predictions.
Training a fully connected network on raw pixels is computationally infeasible for high-resolution images. Fully connected networks treat every pixel independently, requiring an enormous number of parameters and ignoring spatial correlations essential for object detection. This approach is slow, memory-intensive, and unsuitable for real-time pedestrian detection in dynamic environments.
Real-time object detection models such as YOLO or SSD are the most appropriate because they provide accurate localization and classification of pedestrians, maintain low latency, and can process high-resolution images efficiently. These models balance speed and precision, making them ideal for autonomous vehicle applications where safety and responsiveness are paramount.
Question 39
You are building a natural language processing model to classify customer support tickets into categories. Tickets contain domain-specific terminology and abbreviations. Which approach is most appropriate?
A) Use a bag-of-words model with TF-IDF features
B) Use a transformer-based model with domain-specific pretraining
C) Ignore abbreviations and use standard word embeddings
D) Train a standard RNN on raw tokenized text without pretrained embeddings
Answer: B
Explanation:
Using a bag-of-words model with TF-IDF features captures word frequency but ignores context, word order, and relationships between terms. In customer support tickets, the meaning often depends on the sequence of words and domain-specific terminology. TF-IDF also struggles with abbreviations and rare terms, which may lead to misclassification. While bag-of-words models are simple and computationally efficient, they are inadequate for capturing the nuanced language and context present in specialized support tickets.
Using a transformer-based model with domain-specific pretraining is highly suitable. Models such as BERT, RoBERTa, or domain-specific variants pretrained on customer support or technical corpora can understand contextual relationships and specialized vocabulary. Transformers use self-attention to weigh relevant terms in the sequence, enabling accurate classification even when abbreviations or rare domain-specific words are present. Fine-tuning these models on labeled support ticket data allows them to capture subtle patterns and relationships, resulting in higher classification accuracy compared to traditional approaches. Pretrained embeddings reduce the amount of labeled data required and improve generalization.
Ignoring abbreviations and using standard word embeddings may result in lost information. Standard embeddings trained on general corpora may not include specialized terminology or abbreviations common in customer support tickets. This can reduce the model’s ability to understand the intended meaning, leading to misclassification and poor performance. Ignoring abbreviations also ignores the fact that they often carry critical semantic information necessary for accurate categorization.
Training a standard RNN on raw tokenized text without pretrained embeddings requires large amounts of labeled data to learn representations effectively. Rare terms, domain-specific vocabulary, and abbreviations may be underrepresented, causing the model to generalize poorly. RNNs alone cannot leverage prior knowledge from large corpora, making them less effective for domain-specific NLP tasks compared to pretrained transformer models.
Transformer-based models with domain-specific pretraining are the most appropriate approach because they capture context, handle rare and domain-specific terms, and leverage prior knowledge. They provide robust, high-accuracy classification for support tickets, ensuring that specialized terminology and abbreviations are correctly interpreted, improving operational efficiency and customer satisfaction.
Question 40
You are building a credit card fraud detection system. Fraudulent transactions are extremely rare, and misclassification of fraud has a high financial cost. Which evaluation strategy is most appropriate?
A) Use standard accuracy as the evaluation metric
B) Evaluate with precision, recall, and F1 score while applying class weighting or resampling
C) Remove legitimate transactions to balance the dataset
D) Use mean squared error as the evaluation metric
Answer: B
Explanation:
Using standard accuracy as the evaluation metric is misleading in highly imbalanced datasets like credit card fraud detection. If fraudulent transactions constitute only 1% of the data, a model that predicts all transactions as legitimate achieves 99% accuracy but fails at detecting actual fraud. Accuracy does not differentiate between classes and does not reflect the model’s performance on the minority class, which is the primary concern. High accuracy in such contexts can give a false sense of model reliability, leading to missed fraud detection and significant financial losses.
Evaluating with precision, recall, and F1 score while applying class weighting or resampling is highly suitable. Class weighting penalizes misclassification of fraudulent transactions more heavily, encouraging the model to focus on the minority class. Resampling techniques, such as oversampling fraud cases or undersampling legitimate transactions, balance class distributions for better learning. Precision measures the correctness of fraud predictions, recall measures the proportion of actual fraud detected, and the F1 score balances both, providing a comprehensive assessment of model performance. These metrics directly address the cost-sensitive nature of fraud detection, where false negatives (missed fraud) are often more critical than false positives. This approach ensures that the model effectively identifies fraudulent transactions while maintaining manageable false alarm rates.
Removing legitimate transactions to balance the dataset reduces the available data and discards valuable information about typical transaction patterns. While it artificially balances classes, this strategy introduces bias and limits generalization. The model may fail to differentiate subtle patterns between legitimate and fraudulent behavior because it has fewer examples of normal transactions to learn from. This approach may inflate metrics during training but reduces real-world applicability and reliability.
Using mean squared error (MSE) as the evaluation metric is inappropriate because MSE is designed for regression problems. It measures the average squared difference between predicted and actual numeric values, which does not reflect classification performance. MSE does not differentiate between types of errors, such as false positives and false negatives, which is critical in fraud detection. Using MSE would fail to provide actionable guidance for model optimization in a class-imbalanced classification scenario.
Evaluating the model with precision, recall, and F1 score while applying class weighting or resampling is the most appropriate strategy. It ensures effective learning of minority-class patterns, provides meaningful performance metrics for rare events, and accounts for the financial and operational costs of misclassification. This approach is widely used in practical fraud detection systems to balance detection accuracy with false alarm rates.
Question 41
You are building a machine learning model for real-time anomaly detection in IoT sensor networks. Sensors report multiple metrics per second, and anomalies are rare and unpredictable. Which approach is most suitable?
A) Train a standard classifier using historical labeled data
B) Use an unsupervised anomaly detection method such as autoencoders or isolation forests
C) Ignore anomalies and use statistical averages of sensor data
D) Apply k-means clustering on raw sensor readings
Answer: B
Explanation:
Training a standard classifier using historical labeled data is impractical because labeled anomalies are extremely rare. Supervised learning requires a sufficient number of examples from all classes to learn effectively. In IoT networks, anomalies are unpredictable and often unseen during training, making it impossible to gather a representative labeled dataset. Classifiers trained on limited anomaly examples would generalize poorly, failing to detect novel or subtle anomalies and producing unreliable alerts.
Using an unsupervised anomaly detection method, such as autoencoders or isolation forests, is highly suitable. Autoencoders learn to reconstruct normal sensor data patterns and flag deviations in reconstruction error as anomalies. Isolation forests detect anomalies by partitioning data points in feature space and identifying points that require fewer splits to isolate. Both methods do not rely on labeled anomalies and can detect rare or previously unseen events. They are effective in real-time environments where anomalies are unpredictable and rare, providing timely alerts while maintaining low false positive rates. Unsupervised methods scale well with high-dimensional sensor data and adapt to evolving patterns without extensive retraining.
Ignoring anomalies and using statistical averages of sensor data is inadequate. While averaging reduces noise, it also eliminates important transient signals that indicate anomalies. Subtle or short-lived abnormal events would be lost in aggregation, preventing timely detection. This approach may suffice for long-term trend analysis but is unsuitable for real-time anomaly detection in safety-critical or performance-sensitive IoT systems.
Applying k-means clustering on raw sensor readings is limited. Clustering can identify groups of similar sensor states, but does not provide explicit anomaly detection. Points that do not fit clusters may be flagged as anomalies, but this approach is sensitive to cluster initialization, number of clusters, and noise. Clustering lacks temporal context and cannot adaptively learn evolving normal patterns, making it unreliable for real-time anomaly detection.
Unsupervised anomaly detection using autoencoders or isolation forests is the most appropriate approach because it can detect rare, unpredictable anomalies without requiring extensive labeled data. These methods leverage normal behavior patterns, are robust to high-dimensional sensor streams, and provide real-time alerts, ensuring operational reliability and timely response in IoT networks.
Question 42
You are designing a recommendation system for a video streaming platform. Users frequently consume new content, and engagement depends on relevance and novelty. Which approach is most appropriate?
A) Pure collaborative filtering using only historical interactions
B) Reinforcement learning to optimize long-term user engagement
C) Recommend random videos to encourage discovery
D) Recommend only the most popular videos
Answer: B
Explanation:
Pure collaborative filtering using only historical interactions is effective for predicting items similar to past preferences but does not account for novelty or changing user interests. Collaborative filtering may recommend familiar content repetitively, limiting exposure to new videos. It also struggles with new users or new content (cold-start problem), which are common in dynamic video platforms. Reliance solely on past interactions may reduce engagement over time and fail to optimize for long-term satisfaction.
Reinforcement learning (RL) to optimize long-term user engagement is highly suitable. RL models treat the recommendation process as a sequential decision problem, where user interactions (watch time, likes, skips) serve as reward signals. The system learns policies to maximize cumulative rewards over time, balancing exploitation of known user preferences with exploration of new content. This approach accounts for novelty, relevance, and long-term engagement metrics. RL can dynamically adapt recommendations to evolving user behavior and content additions, ensuring users remain engaged and discover diverse videos. Constraints such as diversity, fairness, and novelty can be incorporated into the reward function to meet platform objectives.
Recommending random videos provides exploration but lacks personalization and relevance. While it may expose users to new content, most recommendations will be irrelevant, reducing satisfaction and engagement. Random recommendations do not optimize for long-term engagement or retention, making them impractical for a commercial video streaming service.
Recommending only the most popular videos biases recommendations toward trending content. Popularity-based approaches ignore individual user preferences and novelty, which may lead to repetitive suggestions and reduced engagement over time. This method also fails to introduce new content effectively, limiting discovery and potentially decreasing overall satisfaction for users seeking personalized recommendations.
Reinforcement learning is the most appropriate approach because it dynamically adapts to user behavior, balances relevance and novelty, and optimizes long-term engagement. It enables the system to learn from sequential interactions, incorporate exploration of new content, and provide personalized recommendations that maximize user satisfaction over time.
Question 43
You are building a predictive model for detecting equipment failures in a manufacturing plant. Sensor readings are collected every second, and failures are rare but critical to detect. Which approach is most suitable?
A) Train a standard classifier on all data without addressing class imbalance
B) Apply anomaly detection techniques such as autoencoders or isolation forests
C) Aggregate sensor readings into hourly averages and train a linear regression
D) Use k-means clustering to group normal and abnormal readings
Answer: B
Explanation:
Training a standard classifier on all data without addressing class imbalance is ineffective for rare-event detection. With very few failure instances relative to normal operation, the model will likely predict normal operation most of the time, achieving high accuracy but failing to detect actual failures. Supervised learning in highly imbalanced scenarios without adjustment can result in poor generalization to the minority class. This approach does not provide reliable detection of critical failures, which is essential in a manufacturing context where undetected failures can lead to costly downtime or safety risks.
Applying anomaly detection techniques such as autoencoders or isolation forests is highly suitable. Autoencoders are trained to reconstruct normal operating data and flag deviations in reconstruction error as anomalies. Isolation forests detect points that require fewer splits to isolate, identifying rare abnormal readings. Both methods do not rely on labeled failures, which are limited, and can detect previously unseen failure patterns. They capture subtle deviations in sensor streams, enabling early detection of equipment malfunctions. These techniques are robust to high-dimensional data, operate in real time, and provide actionable alerts for maintenance personnel, reducing downtime and mitigating operational risk.
Aggregating sensor readings into hourly averages and training a linear regression model oversimplifies the problem. Aggregation smooths out critical variations, masking transient anomalies that indicate imminent failure. Linear regression assumes a linear relationship between features and the target, which is unlikely to capture complex interactions among multiple sensors, leading to failure. This approach is inadequate for early detection, as it may miss critical signals embedded in high-frequency sensor data.
Using k-means clustering to group normal and abnormal readings is limited. Clustering can identify groups of similar sensor patterns, but it does not provide a predictive framework for rare events. Anomalous readings may not form distinct clusters or may be incorrectly assigned to normal clusters due to noise. Clustering is sensitive to initialization, the number of clusters, and outliers, making it unreliable for real-time detection of critical failures in operational environments.
Anomaly detection methods such as autoencoders or isolation forests are the most appropriate approach because they effectively handle rare-event detection, capture deviations from normal patterns, and operate in high-dimensional sensor environments. These methods provide timely alerts, support preventive maintenance, and improve reliability in manufacturing operations.
Question 44
You are developing a natural language processing system for extracting medical entities from electronic health records. The text contains complex terminology, abbreviations, and long sentences. Which approach is most suitable?
A) Use a bag-of-words model with logistic regression
B) Use a transformer-based model such as BioBERT or ClinicalBERT
C) Apply k-means clustering on token embeddings
D) Train a standard RNN without pretrained embeddings
Answer: B
Explanation:
Using a bag-of-words model with logistic regression ignores word order and context. In medical texts, entities are often multi-word terms, and meaning depends on surrounding words and sentence structure. Bag-of-words cannot differentiate between “no evidence of disease” and “evidence of disease,” which is critical in clinical interpretation. Logistic regression can handle simple classification, but struggles with rare or domain-specific terminology. This approach is insufficient for extracting complex medical entities accurately.
Using a transformer-based model such as BioBERT or ClinicalBERT is highly suitable. These models are pretrained on biomedical and clinical corpora, providing contextual embeddings tailored to medical language. Transformers use self-attention mechanisms to weigh relevant words, allowing the model to understand relationships between terms, abbreviations, and complex phrases. Fine-tuning these models on labeled medical entity datasets enables accurate extraction of entities, including rare and multi-word expressions. Pretrained embeddings significantly improve performance, reduce the need for large labeled datasets, and capture domain-specific nuances, which are essential in healthcare applications.
Applying k-means clustering on token embeddings is unsuitable for named entity recognition. Clustering can group similar tokens, but it does not provide sequential labeling or classify entity types. Clustering cannot handle multi-word expressions or context-dependent meanings, limiting its utility for medical entity extraction. It may provide exploratory insight, but it cannot serve as a robust, production-ready solution.
Training a standard RNN without pretrained embeddings is limited because learning high-quality embeddings from scratch requires large labeled datasets. Rare entities and domain-specific abbreviations may be underrepresented, resulting in poor generalization. RNNs also struggle with long sentences and long-range dependencies, which are common in medical notes. Pretrained transformers outperform RNNs in capturing context, dependencies, and domain-specific terminology.
Transformer-based models like BioBERT or ClinicalBERT are the most appropriate because they capture context, understand domain-specific terminology, and handle complex sentence structures. They provide accurate, scalable, and interpretable medical entity extraction, ensuring reliability for clinical decision support and research applications.
Question 45
You are building a real-time object detection system for drone monitoring of agricultural fields. The system must detect small pests and disease spots in high-resolution images. Which approach is most suitable?
A) Use a standard CNN classifier on downsampled images
B) Apply a single-stage object detection model like YOLOv5 or SSD
C) Train a fully connected network on raw pixels
D) Use unsupervised clustering to identify pest regions
Answer: B
Explanation:
Using a standard CNN classifier on downsampled images is ineffective for detecting small objects like pests or disease spots. Downsampling reduces image resolution, causing small targets to become indistinguishable. Standard CNNs perform classification over the entire image and do not provide localization, which is essential for identifying specific areas of infestation or damage in agricultural fields. This approach sacrifices both spatial detail and detection capability, making it unsuitable for precision agriculture.
Applying a single-stage object detection model like YOLOv5 or SSD is highly suitable. These models detect and localize multiple objects in a single forward pass, making them efficient for real-time applications. YOLOv5 divides the image into grids and predicts bounding boxes and classes simultaneously, while SSD detects objects at multiple scales, preserving small-object information. Both approaches maintain high accuracy for small targets in high-resolution images and provide bounding boxes for actionable decisions. They are optimized for speed, enabling real-time deployment on drones, which is critical for continuous field monitoring.
Training a fully connected network on raw pixels is computationally infeasible for high-resolution images. Fully connected layers treat each pixel independently, requiring an enormous number of parameters and ignoring spatial correlations essential for object detection. This approach is slow, memory-intensive, and unable to locate multiple small objects in complex images, making it unsuitable for agricultural monitoring.
Using unsupervised clustering to identify pest regions is limited. Clustering can group similar pixel patterns, but it cannot classify or precisely localize pests or disease spots. Clustering is sensitive to noise and lighting variations in outdoor images. It may highlight abnormal regions, but it does not provide structured outputs like bounding boxes or labels, which are necessary for automated monitoring and intervention.
Single-stage object detection models like YOLOv5 or SSD are the most appropriate approach because they efficiently detect small targets, provide localization, and operate in real-time on high-resolution images. This ensures accurate monitoring of pests and diseases, supporting timely interventions and improved crop management.