Google Professional Machine Learning Engineer Exam Dumps and Practice Test Questions Set 4 Q46-60
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Question 46
You are building a model to forecast demand for perishable goods in a retail chain. The data exhibits strong weekly seasonality and occasional promotional spikes. Which modeling approach is most appropriate?
A) Simple linear regression ignoring time
B) Seasonal ARIMA (SARIMA) or Prophet with handling of promotions as external regressors
C) Aggregate data weekly and use moving averages
D) k-means clustering on historical sales
Answer: B
Explanation:
Simple linear regression, ignoring time, is inadequate for forecasting perishable goods demand because it does not account for temporal dependencies, seasonality, or cyclical patterns in sales. Retail demand is influenced by predictable weekly cycles, holidays, and promotional events, which linear regression cannot capture if only raw time or basic features are included. Fitting a linear model without considering seasonality or promotional effects may lead to systematic errors and poor predictive performance. This approach oversimplifies the problem and does not provide the granularity needed for operational decision-making, such as inventory management for perishable items.
Seasonal ARIMA (SARIMA) or Prophet with external regressors for promotions is highly suitable. SARIMA explicitly models seasonal and trend components, incorporating autoregressive and moving average terms to capture temporal dependencies. Prophet, a model designed for business time series, can include external regressors such as promotions, holidays, and events, which impact demand. Both approaches can handle missing data and accommodate irregular spikes due to marketing campaigns or holidays. Incorporating promotions as external regressors allows the model to distinguish between normal seasonal patterns and abnormal surges, providing accurate forecasts for inventory planning and reducing spoilage. These models also produce interpretable components such as trend, seasonality, and events, which are valuable for decision-makers.
Aggregating data weekly and using moving averages oversimplifies the dynamics of perishable goods sales. While moving averages can smooth noise, they do not explicitly model seasonality, trends, or promotional effects. Important short-term variations, such as daily peaks and promotional spikes, may be lost in aggregation, resulting in inaccurate forecasts. Moving averages are more suitable for exploratory analysis but not for operational demand forecasting requiring precise inventory management.
Using k-means clustering on historical sales may identify patterns or group similar sales periods but does not provide a predictive framework. Clustering cannot forecast future demand, handle temporal dependencies, or model external effects like promotions. While useful for segmentation, k-means does not meet the requirements of actionable forecasting for perishable goods, where accuracy and responsiveness are critical.
SARIMA or Prophet with external regressors is the most appropriate approach because it models both seasonality and trend, handles external influences like promotions, and provides interpretable and actionable forecasts. This ensures accurate planning for perishable inventory, reduces waste, and optimizes operational efficiency, balancing statistical rigor with practical business needs.
Question 47
You are developing a natural language processing model to summarize long scientific articles. The summaries must capture key points while maintaining coherence. Which approach is most appropriate?
A) Extractive summarization using simple frequency-based methods
B) Transformer-based sequence-to-sequence model with attention mechanisms
C) Remove all stopwords and retain only nouns and verbs
D) Use k-means clustering on sentences to select representative ones
Answer: B
Explanation:
Extractive summarization using simple frequency-based methods selects sentences based on word occurrence frequency. While it can capture common terms, it often produces disjointed summaries that lack coherence or logical flow. Scientific articles contain complex terminology and nuanced arguments, and frequency-based extraction may miss critical context or subordinate ideas. This approach is limited to selecting sentences verbatim and cannot paraphrase or integrate information across multiple sentences, making it insufficient for high-quality summarization of technical content.
A transformer-based sequence-to-sequence model with attention mechanisms is highly suitable. Transformers such as BART or T5 are pretrained on large corpora and can be fine-tuned for abstractive summarization tasks. Sequence-to-sequence models generate summaries by learning to map input text to a condensed output, producing coherent and contextually accurate summaries. Attention mechanisms allow the model to focus on the most relevant sections of the article while maintaining relationships between sentences and paragraphs. This is critical for scientific articles, where understanding dependencies between concepts and logical flow is essential. Transformers can handle long sequences and complex language structures, enabling abstractive summarization that captures key points in a concise, readable form.
Removing all stopwords and retaining only nouns and verbs oversimplifies the text. While this reduces vocabulary size, it discards critical functional words and relationships necessary for coherent sentence construction. Key connections, negations, and modifiers are lost, resulting in fragmented, potentially misleading summaries. This approach does not produce meaningful summarization and ignores semantic and syntactic context critical for accurate scientific abstraction.
Using k-means clustering on sentences may select representative sentences, providing an extractive summary. However, clustering does not account for sentence order, logical flow, or coherence across paragraphs. Important sentences may be missed if they are semantically similar to others but contain critical details. Clustering cannot perform paraphrasing or integrate information, limiting the quality of summaries.
Transformer-based sequence-to-sequence models with attention are the most appropriate approach because they can generate coherent, context-aware, and concise summaries of complex scientific articles. They capture dependencies, retain meaning across long passages, and allow abstractive summarization, ensuring key points are highlighted accurately and presented in a readable form suitable for researchers and practitioners.
Question 48
You are designing a recommendation system for an online news platform. Users have diverse interests, and the system must suggest articles that maximize engagement over time while balancing novelty. Which approach is most appropriate?
A) Recommend only historically popular articles
B) Reinforcement learning to optimize long-term user engagement
C) Collaborative filtering, ignoring temporal aspects
D) Recommend random articles to increase exploration
Answer: B
Explanation:
Recommending only historically popular articles focuses on content that has been widely consumed but fails to account for individual user preferences or novelty. Users with niche interests may receive irrelevant suggestions, reducing engagement and satisfaction. Popularity-based recommendations do not adapt to changing interests or emerging content, leading to stagnation and reduced long-term engagement. This approach is simple but inadequate for personalized news delivery, where relevance, diversity, and novelty are critical.
Reinforcement learning to optimize long-term user engagement is highly suitable. RL treats recommendations as a sequential decision problem, where user interactions such as clicks, read duration, and sharing serve as reward signals. The model learns policies that balance exploitation of known preferences with exploration of new content, adapting dynamically to evolving interests. RL enables the system to optimize for long-term engagement, considering both immediate clicks and sustained satisfaction. Reward functions can incorporate diversity, novelty, and relevance, ensuring that users are exposed to new articles without sacrificing personalization. This approach allows continuous learning from user behavior and content trends, providing a responsive, engaging recommendation experience.
Collaborative filtering, ignoring the temporal aspect, captures general preferences based on historical interactions but fails to adapt to changing user behavior or content freshness. Seasonal trends, breaking news, and evolving user interests are not reflected, leading to outdated or irrelevant recommendations. While collaborative filtering is effective for static preferences, it does not optimize long-term engagement in dynamic news environments where temporal relevance and novelty are crucial.
Recommending random articles provides exploration but lacks personalization. Users may encounter new content but with low relevance, reducing satisfaction and engagement. Random recommendations are inefficient for maximizing engagement and may dilute trust in the platform. While some exploration is important, it must be balanced with relevance and user preference to ensure meaningful interactions.
Reinforcement learning is the most appropriate approach because it dynamically adapts to user behavior, balances relevance and novelty, and optimizes long-term engagement. By learning from sequential interactions and incorporating reward signals for satisfaction, diversity, and exploration, RL ensures personalized, engaging recommendations that maximize retention and overall user experience on the news platform.
Question 49
You are developing a predictive model for hospital readmissions. The dataset contains patient demographics, medical history, lab results, and treatments. Some lab values are missing for certain patients. Which approach is most appropriate?
A) Remove all records with missing lab values and train a standard classifier
B) Apply imputation techniques and train a tree-based model such as XGBoost
C) Use only demographic data to avoid missing values
D) Train a linear regression model on complete cases only
Answer: B
Explanation:
Removing all records with missing lab values reduces the dataset size and may introduce bias. In healthcare datasets, missing values are often not random; for example, certain lab tests may only be ordered for sicker patients. Dropping these cases risks eliminating critical information, resulting in a biased model that underrepresents high-risk patients. Additionally, smaller datasets reduce the model’s ability to learn complex patterns, particularly interactions between lab results, treatments, and outcomes. This approach is generally unsuitable for healthcare applications where patient safety and predictive accuracy are paramount.
Applying imputation techniques and training a tree-based model such as XGBoost is highly suitable. Imputation methods, such as mean, median, or model-based imputation, fill missing lab values while retaining all available information. Tree-based models like XGBoost can handle heterogeneous data types, model non-linear interactions, and are robust to outliers and missing values. These models automatically capture complex relationships between demographics, treatments, and lab results, improving prediction accuracy. XGBoost’s ability to learn feature interactions is particularly useful in healthcare, where patient outcomes depend on the interplay of multiple variables. Imputation preserves the dataset’s completeness, ensures all patient data contributes to model training, and reduces the risk of bias due to missingness.
Using only demographic data to avoid missing values limits the model’s predictive power. Lab results, medical history, and treatments provide critical information about patient risk. Ignoring these features oversimplifies the problem, reducing accuracy and potentially overlooking high-risk patients. While demographic features are useful, they are insufficient for accurate readmission prediction on their own. This approach sacrifices actionable insights for simplicity, which is not appropriate in healthcare.
Training a linear regression model on complete cases only has similar drawbacks to dropping missing records. Linear regression assumes linear relationships between inputs and outputs and cannot naturally capture complex non-linear interactions among medical variables. Limiting the training set to complete cases further reduces data availability and may introduce bias. This method is unlikely to achieve reliable performance, particularly for diverse patient populations with varying medical histories.
Imputation combined with tree-based models like XGBoost is the most appropriate approach because it retains all available data, handles missing values effectively, models complex interactions, and provides high predictive accuracy. This ensures reliable readmission predictions, supporting hospital decision-making and patient care interventions.
Question 50
You are building a machine learning model to detect fraudulent insurance claims. The dataset is highly imbalanced, with only 1% of claims being fraudulent. Which modeling approach is most effective?
A) Train a standard classifier without addressing class imbalance
B) Apply oversampling or class weighting and evaluate with precision, recall, and F1 score
C) Remove legitimate 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 because the model will be biased toward predicting legitimate claims. With only 1% of claims being fraudulent, the classifier may predict all claims as non-fraudulent, achieving high accuracy but failing entirely to identify fraudulent cases. Accuracy is misleading in highly imbalanced datasets, and the model would not serve its purpose of detecting rare but high-impact events. This approach does not prioritize the minority class, which is critical in fraud detection.
Applying oversampling or class weighting and evaluating with precision, recall, and F1 score is highly suitable. Oversampling fraudulent claims increases their representation in the dataset, while class weighting penalizes misclassification of fraud more heavily. Precision measures the proportion of correctly predicted fraudulent claims among all predicted frauds, recall measures the proportion of actual fraud detected, and F1 score balances these metrics, providing a comprehensive evaluation. This approach ensures the model effectively identifies rare fraudulent claims while controlling false positives, which is crucial for operational efficiency and financial protection. Resampling combined with appropriate metrics addresses both learning and evaluation challenges in imbalanced classification tasks.
Removing legitimate claims to balance the dataset reduces the amount of information about normal behavior. While it may artificially balance classes, it introduces bias and limits generalization. The model may fail to distinguish subtle differences between legitimate and fraudulent claims, reducing real-world effectiveness. This approach may inflate training metrics but results in a model that performs poorly on unseen data.
Using mean squared error as the evaluation metric is inappropriate because MSE is designed for regression problems. MSE measures numeric differences between predictions and actual values but does not reflect classification performance, particularly for minority classes. It does not account for the cost of false positives and false negatives, which is critical in fraud detection. Relying on MSE would provide misleading guidance for model optimization and deployment.
Oversampling or class weighting combined with precision, recall, and F1 score is the most effective modeling strategy for highly imbalanced fraud detection datasets. It ensures the model can learn from rare events, provides actionable performance metrics, and minimizes the financial and operational risk associated with misclassification.
Question 51
You are building a recommendation system for an online streaming platform. Users frequently consume new content, and engagement depends on relevance and novelty. Which approach is most appropriate?
A) Recommend only historically popular content
B) Reinforcement learning to optimize long-term engagement
C) Collaborative filtering ignoring temporal aspects
D) Recommend random content to increase exploration
Answer: B
Explanation:
Recommending only historically popular content focuses on items that have been widely consumed, but this approach fails to account for user-specific preferences or the need for novelty. Users with niche interests may receive irrelevant recommendations, and repeated suggestions of popular content can lead to stagnation and decreased engagement. Popularity-based approaches do not adapt to emerging content or changing user behavior, limiting their effectiveness for maximizing long-term engagement on dynamic platforms.
Reinforcement learning to optimize long-term engagement is highly suitable. Reinforcement learning models treat recommendations as a sequential decision-making problem, where user interactions such as watch time, clicks, likes, or skips serve as reward signals. The system learns policies that balance exploitation of known preferences with exploration of new content, adapting dynamically to evolving user behavior. By optimizing for cumulative rewards, reinforcement learning ensures recommendations maximize long-term engagement, considering both immediate satisfaction and sustained retention. Reward functions can incorporate diversity, novelty, and relevance, providing a comprehensive approach to personalized recommendations on streaming platforms.
Collaborative filtering ignoring temporal aspects captures user preferences based on historical interactions but fails to adapt to changes in user behavior or new content. Seasonal trends, newly released shows, and evolving interests are not reflected, leading to outdated or irrelevant recommendations. While collaborative filtering captures similarity between users or items, it does not optimize for long-term engagement in a dynamic environment.
Recommending random content provides exploration but lacks personalization. Users may be exposed to new items but with low relevance, reducing satisfaction and overall engagement. Random recommendations do not systematically optimize for retention or relevance, making them inefficient for commercial streaming platforms where user engagement is critical.
Reinforcement learning is the most appropriate approach because it dynamically adapts to user preferences, balances relevance and novelty, and optimizes long-term engagement. It allows the system to continuously learn from interactions, introduce new content effectively, and maintain high user satisfaction over time.
Question 52
You are building a computer vision system to detect defects in high-resolution industrial components. The defects are small and irregularly shaped. Which approach is most suitable?
A) Use a standard CNN classifier on downsampled images
B) Apply a region-based object detection model such as Faster R-CNN
C) Train a fully connected network on raw pixels
D) Use k-means clustering to identify defect regions
Answer: B
Explanation:
Using a standard CNN classifier on downsampled images is insufficient for detecting small and irregularly shaped defects. Downsampling reduces the resolution of critical regions, potentially eliminating the defects entirely. Standard CNNs perform classification on the entire image and do not provide spatial localization. While they may detect if an image contains defects, they cannot identify the specific location or shape of small anomalies, which is critical for quality control in industrial manufacturing. This approach sacrifices both detection granularity and actionable insights.
Applying a region-based object detection model such as Faster R-CNN is highly suitable. Faster R-CNN combines region proposal networks with convolutional feature extraction to detect objects within specific regions of the image. It can localize small defects, providing bounding boxes or masks, which are essential for identifying and documenting defects in industrial components. The model can handle high-resolution images without downsampling, preserving spatial detail. Faster R-CNN also allows the detection of multiple defects within a single image, and its hierarchical feature extraction is capable of capturing irregular shapes. This approach balances detection accuracy with practical utility in quality assurance processes.
Training a fully connected network on raw pixels is impractical for high-resolution images. Fully connected networks require an enormous number of parameters, treat each pixel independently, and ignore spatial relationships. They are computationally intensive and prone to overfitting, especially when defects are small and rare. This approach is not feasible for real-world deployment in high-resolution defect detection tasks.
Using k-means clustering to identify defect regions may highlight unusual patterns but does not provide classification or localization. Clustering can group pixels with similar characteristics, but it is sensitive to noise, lighting variations, and initialization. It cannot accurately detect irregular shapes or provide structured outputs like bounding boxes. Clustering might be useful for exploratory analysis but is insufficient for operational defect detection.
Region-based object detection models like Faster R-CNN are the most appropriate approach because they combine localization and classification, preserve spatial detail, and detect small, irregular defects accurately. This ensures actionable quality control insights and supports automated industrial inspection systems.
Question 53
You are developing a natural language processing model to extract named entities from legal documents. The documents include complex terminology, long sentences, and rare entities. Which approach is most appropriate?
A) Bag-of-words model with logistic regression
B) Transformer-based model, such as LegalBERT or a domain-specific BERT variant
C) Apply k-means clustering to group tokens
D) Train a standard RNN without pretrained embeddings
Answer: B
Explanation:
A bag-of-words model with logistic regression ignores word order, context, and syntactic structure. Legal documents often contain multi-word entities, references, and complex relationships that are lost in a bag-of-words representation. Logistic regression cannot model dependencies between terms or understand rare entities, which are common in legal text. This approach may identify frequently occurring terms, but it is insufficient for accurate named entity recognition (NER) in legal documents.
A transformer-based model, such as LegalBERT or a domain-specific BERT variant, is highly suitable. These models are pretrained on large corpora of legal text, providing contextual embeddings tailored to the legal domain. Transformers use self-attention to capture relationships between words, even in long sentences, and can identify rare or multi-word entities accurately. Fine-tuning these models on labeled NER datasets allows them to adapt to specific tasks, such as recognizing legal parties, case numbers, or statutes. Pretrained transformers reduce the need for large annotated datasets and handle domain-specific terminology, abbreviations, and complex sentence structures effectively.
Applying k-means clustering to group tokens is limited. While clustering can identify groups of similar terms, it does not provide sequential labeling or classify entities. Multi-word expressions and context-dependent terms cannot be reliably captured, making clustering insufficient for legal NER tasks. It may help explore the structure of text, but it cannot produce actionable entity recognition.
Training a standard RNN without pretrained embeddings is constrained by the need for large labeled datasets. Rare legal entities may be underrepresented, causing poor generalization. RNNs also struggle with long sentences, long-range dependencies, and complex structures common in legal documents. Pretrained transformers outperform RNNs in capturing contextual relationships, domain-specific terminology, and rare entities.
Transformer-based models like LegalBERT are the most appropriate approach because they can understand context, handle complex legal terminology, and identify multi-word and rare entities accurately. They provide state-of-the-art NER performance for legal documents, supporting document analysis, automated contract review, and knowledge extraction.
Question 54
You are building a recommendation system for an e-commerce platform where users frequently 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 or temporal embeddings
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 consider temporal dynamics, such as seasonal trends or changing user interests. For example, users may buy winter clothing only in certain months. Ignoring temporal information can result in recommendations that are irrelevant at certain times of the year. Collaborative filtering without time also struggles with evolving user preferences, reducing engagement and purchase rates.
Time-aware collaborative filtering using sequence modeling or temporal embeddings is highly suitable. This approach incorporates temporal information, weighting recent interactions more heavily or modeling purchase sequences over time. Sequence models such as recurrent neural networks or transformer-based embeddings can capture seasonal patterns, recurring purchases, and short-term trends. This ensures that recommendations align with current user needs and temporal context. For example, it can suggest swimsuits in summer and jackets in winter. Temporal modeling also improves personalization, increases conversion rates, and enhances user satisfaction by providing timely, relevant recommendations.
Recommending products randomly provides exploration but lacks personalization. Users may discover new items, but the majority of recommendations are likely irrelevant, leading to low engagement and satisfaction. Random suggestions do not optimize for seasonal relevance or user preferences, making this approach inefficient for e-commerce applications.
Only recommending historically popular products ignores temporal trends and individual user preferences. While it may work for items consistently in demand, it fails to capture seasonal or emerging product needs. Users may receive irrelevant suggestions outside of peak demand periods, reducing engagement and potentially missing sales opportunities.
Time-aware collaborative filtering using sequence modeling is the most appropriate approach because it captures temporal patterns, maintains personalization, and adapts to evolving user behavior. It balances historical preference with seasonal trends, ensuring recommendations are relevant, timely, and more likely to result in purchases.
Question 55
You are building a predictive maintenance system for an industrial plant. Sensor data is collected continuously from machines, including vibration, temperature, and pressure readings. Failures are rare and often preceded by subtle changes in patterns. Which approach is most suitable?
A) Train a standard classifier on raw sensor readings
B) Use unsupervised anomaly detection methods such as autoencoders or isolation forests
C) Aggregate data into daily averages and use linear regression
D) Apply k-means clustering to detect abnormal readings
Answer: B
Explanation:
Training a standard classifier on raw sensor readings is limited due to the rarity of failures. Supervised models require sufficient examples of both normal and failure events to learn patterns effectively. In industrial settings, failures are rare, meaning the classifier would predominantly see normal operation data. This leads to models that predict “no failure” most of the time, achieving high accuracy but failing to detect critical anomalies. Moreover, subtle precursors to failure may not be learned effectively, reducing the ability to provide early warnings.
Unsupervised anomaly detection methods such as autoencoders or isolation forests are highly suitable. Autoencoders learn to reconstruct normal operational patterns; deviations from expected reconstruction indicate potential failures. Isolation forests isolate rare points in high-dimensional feature space, flagging unusual sensor readings. These methods do not require labeled failure data and can detect previously unseen failure modes. They capture subtle temporal or multi-sensor interactions that precede failures, enabling early alerts. Additionally, unsupervised techniques can be continuously updated with new data, adapting to changing operational conditions and improving detection performance over time.
Aggregating data into daily averages and using linear regression oversimplifies the problem. Averaging smooths out transient changes and subtle anomalies that may precede machine failures. Linear regression assumes a linear relationship between input features and failure probability, which is unlikely in complex machinery systems. This approach may miss early warning signals, delaying detection and increasing the risk of costly downtime.
Applying k-means clustering to detect abnormal readings is limited. While clustering may group normal operating states, it does not inherently provide a predictive or sequential framework for failure detection. Outliers might be detected, but clusters are sensitive to initialization, number of clusters, and noise. K-means cannot effectively capture complex temporal dependencies or subtle deviations across multiple sensor readings, making it unreliable for proactive maintenance.
Unsupervised anomaly detection using autoencoders or isolation forests is the most appropriate approach because it handles rare events, detects subtle anomalies, and adapts to high-dimensional sensor data. This approach enables early detection of machine failures, reduces downtime, and improves operational safety and efficiency.
Question 56
You are developing a model to predict customer churn for a subscription service. The dataset is highly imbalanced, with a small fraction of users churning each month. Which approach is most effective?
A) Train a standard classifier without addressing imbalance
B) Apply oversampling, undersampling, or class weighting and evaluate using precision, recall, and F1 score
C) Remove non-churned users 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 predicting non-churn. With a small proportion of churned users, a classifier can achieve high overall accuracy by predicting all users as active, but this provides no insight into actual churn. Ignoring imbalance results in poor recall for the minority class, which is the most important metric in retention analysis. Such a model cannot guide business interventions effectively.
Applying oversampling, undersampling, or class weighting and evaluating using precision, recall, and F1 score is highly suitable. Oversampling churned users balances the training dataset, while undersampling non-churned users reduces bias toward the majority class. Class weighting penalizes misclassification of churn more heavily, encouraging the model to focus on the minority class. Precision measures the proportion of correctly predicted churns among all predicted churns, recall measures the proportion of actual churn identified, and F1 score balances these metrics. This ensures the model can accurately identify churn while controlling false positives, providing actionable insights for retention strategies. These techniques are widely used in imbalanced classification problems where the minority class is of primary interest.
Removing non-churned users to balance the dataset reduces available data, potentially discarding patterns that distinguish churn from normal behavior. While this may create a balanced dataset, it introduces bias and limits generalization. The model may overfit to the reduced dataset and fail on unseen data, making it unsuitable for practical deployment.
Using mean squared error (MSE) is inappropriate because MSE is designed for regression tasks and does not reflect classification performance. It does not distinguish between false positives and false negatives, which are critical in churn detection. MSE would not provide meaningful guidance for model selection or optimization in this imbalanced classification scenario.
Oversampling, undersampling, or class weighting combined with precision, recall, and F1 score is the most effective approach because it addresses class imbalance, improves detection of churn, and provides meaningful evaluation metrics. This approach enables proactive retention strategies and maximizes the impact of intervention campaigns.
Question 57
You are building a natural language processing system to classify customer support tickets into categories. Tickets contain domain-specific terminology, abbreviations, and multi-word expressions. Which approach is most appropriate?
A) Bag-of-words model with TF-IDF features
B) 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:
A bag-of-words model with TF-IDF features ignores word order and context. In customer support tickets, meaning often depends on the sequence of words and relationships between terms. Bag-of-words cannot handle multi-word expressions or abbreviations effectively. While TF-IDF captures the importance of terms, it is insufficient for domain-specific terminology and may misrepresent rare or ambiguous expressions. This approach is limited in accurately classifying tickets with complex or specialized language.
A transformer-based model with domain-specific pretraining is highly suitable. Transformers such as BERT or RoBERTa pretrained on domain-specific corpora capture contextual relationships, understand abbreviations, and handle multi-word expressions. Self-attention mechanisms allow the model to focus on relevant words and context, ensuring accurate classification of tickets. Fine-tuning on labeled ticket data enables the model to adapt to specific categories and vocabulary. Pretrained embeddings reduce the need for large labeled datasets, improve generalization, and handle rare terminology effectively. This approach is state-of-the-art for text classification tasks with complex domain-specific language.
Ignoring abbreviations and using standard word embeddings limits performance. General embeddings may not cover domain-specific terms or abbreviations, causing misinterpretation. Important context is lost, reducing classification accuracy and potentially misrouting tickets. While simple embeddings are computationally cheap, they fail to capture critical semantic information in domain-specific support tickets.
Training a standard RNN on raw tokenized text without pretrained embeddings requires large datasets and extensive training to learn effective representations. Rare terms and abbreviations may be underrepresented, limiting the model’s ability to generalize. RNNs alone struggle with long-range dependencies and multi-word expressions, resulting in suboptimal performance compared to pretrained transformer-based approaches.
Transformer-based models with domain-specific pretraining are the most appropriate approach because they accurately capture context, handle specialized terminology, and generalize well. They ensure reliable categorization of support tickets, improving operational efficiency and customer satisfaction.
Question 58
You are building a predictive model to forecast energy consumption for a smart grid. Energy usage shows strong daily and weekly seasonality, as well as occasional spikes during extreme weather events. Which modeling approach is most appropriate?
A) Simple linear regression on raw energy data
B) Seasonal ARIMA (SARIMA) or Prophet with external regressors for weather events
C) Use moving averages to smooth the data and predict future values
D) k-means clustering on historical energy readings
Answer: B
Explanation:
Simple linear regression on raw energy data is inadequate because it cannot capture complex temporal patterns. Energy consumption exhibits strong seasonal variations, including daily and weekly cycles, and can be influenced by external factors such as temperature, humidity, and extreme weather events. Linear regression assumes a fixed linear relationship between inputs and outputs and ignores temporal dependencies and non-linear trends. Using this approach would fail to account for recurring patterns and spikes, leading to inaccurate forecasts and potentially costly errors in grid management and resource allocation.
Seasonal ARIMA (SARIMA) or Prophet with external regressors for weather events is highly suitable. SARIMA models extend ARIMA to include seasonal components, capturing daily and weekly patterns in energy consumption. Prophet, designed for business and time series forecasting, allows inclusion of holidays, events, and exogenous variables such as temperature or precipitation as external regressors. Both methods can handle missing data and irregular spikes, which are common in energy datasets. Including weather variables as regressors helps the model understand anomalous consumption patterns during extreme events, improving forecast accuracy. These approaches provide interpretable components—trend, seasonality, and external effects—which are critical for decision-makers managing energy supply and demand.
Using moving averages to smooth data and predict future values oversimplifies the problem. While moving averages can reduce noise, they also eliminate important short-term variations and spikes. Energy consumption is highly dynamic, and smoothing may hide peak load events, resulting in inaccurate predictions. This method is more suitable for visualizing trends rather than generating actionable forecasts for operational energy management.
Applying k-means clustering on historical energy readings is limited. Clustering may reveal typical usage patterns or group similar days, but it does not provide a predictive framework for forecasting future consumption. Cluster centroids represent averages of past patterns and cannot account for temporal dependencies, external factors, or anomalies. Clustering is useful for exploratory data analysis but is insufficient for precise, actionable forecasting in a smart grid context.
SARIMA or Prophet with external regressors is the most appropriate approach because it captures both seasonal and trend components, incorporates exogenous variables, and adapts to irregular events. This ensures accurate, interpretable forecasts that support efficient energy distribution, demand response planning, and grid stability.
Question 59
You are building a recommendation system for an online retail platform where users frequently purchase seasonal and trending products. Engagement depends on relevance, novelty, and personalization. Which approach is most appropriate?
A) Recommend only historically popular products
B) Reinforcement learning to optimize long-term engagement
C) Collaborative filtering ignoring temporal patterns
D) Recommend random products for exploration
Answer: B
Explanation:
Recommending only historically popular products prioritizes items that have been widely consumed but fails to capture individual user preferences or seasonal relevance. Users with niche interests may receive irrelevant suggestions, and repetitive recommendations reduce engagement over time. Popularity-based approaches do not adapt to changing trends, seasonal product demand, or emerging user behavior. This limits the effectiveness of the system in maximizing long-term engagement and conversion.
Reinforcement learning to optimize long-term engagement is highly suitable. Reinforcement learning treats the recommendation process as a sequential decision-making problem. User interactions such as clicks, purchases, or dwell time serve as reward signals. The system learns policies that balance exploitation of known preferences with exploration of new or trending products. This approach enables dynamic adaptation to evolving user behavior, seasonal trends, and product availability. Reward functions can incorporate relevance, novelty, and diversity, ensuring that recommendations remain personalized while exposing users to new products. Reinforcement learning continuously improves with interaction data, optimizing for long-term engagement rather than immediate clicks alone.
Collaborative filtering, ignoring temporal patterns, captures general similarities between users or items but fails to adapt to changes in user behavior or seasonal trends. For instance, a user may prefer winter apparel during one season and summer apparel during another, which static collaborative filtering cannot account for. While collaborative filtering is effective for static user preferences, it does not address dynamic content or changing engagement patterns over time, reducing overall recommendation quality.
Recommending random products provides exploration but lacks personalization. Users may discover new items, but many recommendations will be irrelevant, leading to decreased engagement and satisfaction. Random recommendations cannot systematically optimize for relevance, novelty, or user retention, making this approach inefficient for commercial e-commerce applications.
Reinforcement learning is the most appropriate approach because it dynamically adapts to user behavior, optimizes long-term engagement, balances relevance and novelty, and accounts for temporal trends. It ensures personalized, context-aware recommendations that maximize retention and satisfaction on the retail platform.
Question 60
You are building a natural language processing model to classify medical research articles such as cardiology, oncology, or neurology. Articles contain domain-specific terminology, abbreviations, and complex sentence structures. Which approach is most appropriate?
A) Bag-of-words model with TF-IDF features
B) Transformer-based model pretrained on biomedical text, such as BioBERT
C) Ignore abbreviations and use standard word embeddings
D) Train a standard RNN on tokenized text without pretrained embeddings
Answer: B
Explanation:
A bag-of-words model with TF-IDF features ignores context, word order, and sentence structure. Medical research articles often contain multi-word terms, domain-specific abbreviations, and nuanced relationships between concepts. Bag-of-words cannot differentiate between subtle differences in meaning, such as “no evidence of disease” versus “evidence of disease,” which is critical for accurate categorization. Logistic regression on TF-IDF features may capture common terms but fails to handle complex domain-specific language, limiting predictive performance.
A transformer-based model pretrained on biomedical text, such as BioBET, is highly suitable. BioBERT has been pretrained on large-scale biomedical corpora, enabling it to capture domain-specific terminology, context, and multi-word expressions. Transformers leverage self-attention mechanisms to focus on relevant portions of the text, maintaining the semantic relationships necessary for accurate classification. Fine-tuning BioBERT on labeled article datasets allows the model to adapt to specific classification tasks, providing high accuracy and robustness, even with complex sentence structures. Pretrained embeddings reduce the need for large labeled datasets, improving generalization and handling rare terminology effectively.
Ignoring abbreviations and using standard word embeddings is insufficient. General embeddings may not include specialized biomedical terms or abbreviations, causing misinterpretation. Important semantic information is lost, reducing classification accuracy. This approach oversimplifies the problem and fails to leverage prior knowledge from biomedical literature.
Training a standard RNN on tokenized text without pretrained embeddings requires large labeled datasets to learn meaningful representations. Rare terms and complex multi-word expressions may be underrepresented, limiting the model’s ability to generalize. RNNs struggle with long sentences and long-range dependencies, which are common in medical research articles. Pretrained transformer-based models outperform RNNs in capturing context, domain-specific terminology, and semantic relationships.
Transformer-based models pretrained on biomedical text like BioBERT are the most appropriate approach because they understand context, domain-specific terminology, and multi-word expressions. They provide high accuracy for classifying medical research articles into specialized categories, supporting literature organization, retrieval, and knowledge discovery in biomedical research.