Google Professional Machine Learning Engineer Exam  Dumps and Practice Test Questions Set 5 Q 81-100

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Question 81:

You are developing a deep learning model for classifying rare diseases in medical images. The dataset is highly imbalanced, with far more negative (healthy) images than positive (disease) images. Which approach is most appropriate to improve detection of rare diseases?

A) Use class-weighted loss or focal loss to emphasize the minority class.
B) Remove negative images to balance the dataset.
C) Train with standard cross-entropy loss without modification.
D) Reduce the model complexity to prevent overfitting.

Answer: A) Use class-weighted loss or focal loss to emphasize the minority class.

Explanation:

Medical datasets for rare diseases often exhibit extreme class imbalance, with positive cases being rare relative to negative cases. In this context, the model can achieve high overall accuracy by predicting the majority class (healthy), but this does not reflect the actual goal of detecting the rare disease, which is critical in medical applications. Proper handling of class imbalance is essential for improving recall and ensuring that positive cases are correctly identified.

A) Class-weighted loss assigns higher importance to minority class examples during training. This ensures that the network places more emphasis on learning features that distinguish rare disease cases from normal images. Focal loss goes further by down-weighting easy-to-classify majority examples and focusing on hard examples, which often correspond to the minority class. Both methods allow the network to learn effectively despite the imbalance, improving sensitivity (true positive rate) without sacrificing overall stability. Weighted loss functions are widely used in medical imaging tasks, including tumor detection and lesion segmentation, because they ensure that rare but critical cases are detected reliably.

B) Removing negative images to balance the dataset discards valuable information about healthy cases. Healthy images define the baseline of normal anatomy, and without sufficient negative examples, the model may misclassify normal images as diseased, increasing false positives. This approach reduces the model’s generalization ability and can compromise clinical safety.

C) Training with standard cross-entropy loss treats all samples equally, causing the model to prioritize the majority class. In extreme imbalance scenarios, the model may fail to detect positive cases entirely, yielding poor sensitivity and making the model practically useless for clinical diagnosis.

D) Reducing model complexity may prevent overfitting, but it does not address the underlying class imbalance. The network will still be biased toward the majority class, and detection of rare diseases will remain inadequate.

Using class-weighted or focal loss is the most effective strategy for handling extreme imbalance in medical imaging, ensuring that the model is sensitive to rare disease cases while maintaining overall robustness.

Question 82:

You are building a recommendation system for an online marketplace. Users have interacted with only a few products, and new items are constantly being added. Which approach is most effective to provide relevant recommendations under these conditions?

A) Use a hybrid system combining collaborative filtering and content-based filtering.
B) Remove new items from the recommendation pool.
C) Recommend only the most popular products.
D) Rely solely on collaborative filtering.

Answer: A) Use a hybrid system combining collaborative filtering and content-based filtering.

Explanation:

The scenario presents both cold-start problems: new users with sparse interactions and new items with no interaction history. Collaborative filtering relies on historical user-item interactions, which are limited or missing in cold-start scenarios. Content-based filtering leverages product metadata (e.g., category, description, price) and user profiles to generate recommendations independent of interaction history.

A) A hybrid recommendation system combines collaborative filtering and content-based filtering. Content-based filtering handles new items and new users by recommending items based on features and similarity to known user preferences. Collaborative filtering refines recommendations over time as more interactions accumulate, personalizing recommendations for individual users. For example, a newly added smartphone can be recommended to a user interested in electronics based on its specifications even if no one has purchased it yet. Hybrid systems achieve a balance between cold-start handling and long-term personalization, improving relevance, diversity, and user satisfaction.

B) Removing new items limits the recommendation pool and prevents users from discovering fresh products, reducing engagement and satisfaction.

C) Recommending only popular products maximizes short-term accuracy but fails to personalize recommendations. Users with unique preferences may find the suggestions irrelevant, leading to poor retention.

D) Relying solely on collaborative filtering fails for new users and new items because it requires sufficient interaction history. Cold-start scenarios would yield poor recommendations and low coverage.

Hybrid recommendation systems are the most practical solution in dynamic marketplaces, ensuring relevant, personalized, and timely recommendations while mitigating cold-start issues.

Question 83:

You are developing a time series forecasting model for electricity consumption. The series exhibits a long-term trend, daily cycles, and seasonal spikes. Which approach is most suitable?

A) Use a model capable of handling trend and multiple seasonalities, such as Prophet or TBATS.
B) Ignore seasonality and rely on standard ARIMA.
C) Train a linear regression on raw values.
D) Aggregate data to remove high-frequency fluctuations.

Answer: A) Use a model capable of handling trend and multiple seasonalities, such as Prophet or TBATS.

Explanation:

 Electricity consumption data often exhibit complex temporal patterns, including long-term trends (e.g., increasing overall demand), daily cycles (e.g., peak usage in the evening), and seasonal spikes (e.g., summer or winter extremes). Accurately capturing these patterns is critical for operational planning, grid management, and energy procurement.

A) Prophet decomposes time series into trend, multiple seasonal components, and holiday effects, allowing flexible modeling of overlapping cycles. TBATS incorporates Fourier terms to handle multiple seasonalities, along with Box-Cox transformations, ARMA errors, and trend components. These models can handle non-linear trends, missing data, and irregular seasonal spikes. By explicitly modeling multiple seasonal patterns, forecasts are more accurate and actionable. For example, forecasting peak demand accurately prevents outages, allows efficient allocation of generation capacity, and supports demand-response programs.

B) Ignoring seasonality with standard ARIMA may capture short-term autocorrelation or linear trends but fails to account for multiple overlapping seasonal patterns. This results in systematic errors during peak periods, reducing forecast accuracy.

C) Linear regression on raw values is insufficient for capturing non-linear trends or multiple seasonalities. Predictions would fail to reflect daily or seasonal peaks, making the forecast unreliable for operational decisions.

D) Aggregating data removes high-frequency fluctuations and seasonal patterns, sacrificing granularity. While it reduces noise, it eliminates critical patterns necessary for grid management and energy planning.

Using models like Prophet or TBATS ensures that complex temporal patterns, including multiple seasonalities and trend dynamics, are accurately modeled, providing reliable forecasts for electricity demand management.

Question 84:

You are training a multi-label text classification model. Some labels are rare, leading to low recall. Which approach is most effective to improve performance on rare labels?

A) Use binary cross-entropy with class weighting.
B) Remove rare labels from the dataset.
C) Treat the task as multi-class classification using categorical cross-entropy.
D) Train only on examples with frequent labels.

Answer: A) Use binary cross-entropy with class weighting.

Explanation

 In multi-label classification, each instance can belong to multiple categories. Rare labels are underrepresented in the dataset, causing standard loss functions to underweight them, resulting in low recall. Improving model performance on these rare labels is critical, particularly in applications such as document tagging, medical diagnosis, or multi-topic classification.

A) Binary cross-entropy treats each label independently, making it suitable for multi-label problems. Applying class weights inversely proportional to label frequency ensures that rare categories contribute more to the loss function. This focuses training on underrepresented labels, improving recall and model coverage. Weighted binary cross-entropy is widely used in scenarios with highly imbalanced labels because it allows the model to learn meaningful representations for rare categories without sacrificing performance on common labels.

B) Removing rare labels simplifies the problem but eliminates the ability to predict these critical categories. In many applications, rare labels are highly informative and essential for the task’s objectives, so this approach is not acceptable.

C) Treating the task as multi-class classification assumes each instance has only one label. This violates the multi-label assumption and ignores multiple rare labels in a single instance, reducing overall predictive performance.

D) Training only on examples with frequent labels excludes rare categories from learning entirely, guaranteeing poor recall for these labels.

Weighted binary cross-entropy ensures balanced learning across all labels, addressing the challenge of rare labels while maintaining overall model performance and coverage.

Question 85:

You are training a convolutional neural network (CNN) for image classification. The model achieves high training accuracy but poor validation performance. Which approach is most effective to improve generalization?

A) Apply data augmentation techniques such as rotations, flips, and color jittering.
B) Increase the number of convolutional layers.
C) Reduce the number of filters in convolutional layers.
D) Train for fewer epochs to avoid overfitting.

Answer: A) Apply data augmentation techniques such as rotations, flips, and color jittering.

Explanation:

 The scenario indicates overfitting, where the network memorizes training examples but fails to generalize to unseen data. Overfitting is a common problem in CNNs, especially when the dataset lacks diversity or is limited in size. Addressing overfitting requires increasing data variability or introducing regularization.

A) Data augmentation artificially increases the effective size and diversity of the training dataset. Techniques such as rotations, flips, scaling, cropping, and color jittering encourage the network to learn invariant features rather than memorizing specific training examples. For example, an animal classifier should recognize a cat regardless of rotation, scale, or lighting conditions. Data augmentation reduces overfitting, improves generalization, and is widely used in computer vision tasks, including object detection, medical imaging, and wildlife classification.

B) Increasing convolutional layers increases model capacity, which may exacerbate overfitting if the training dataset is limited. More layers alone do not address the lack of variability in the data.

C) Reducing the number of filters decreases model capacity, which can prevent overfitting in some cases, but risks underfitting, where the network cannot learn sufficient features to perform accurate classification.

D) Training for fewer epochs may mitigate overfitting but risks undertraining, preventing the network from learning essential patterns in the data.

Data augmentation is the most effective approach to improve generalization in CNNs by providing realistic variability, allowing the network to perform well on unseen validation or test datasets.

Question 86:

 You are developing a reinforcement learning (RL) agent to navigate a robotic arm. The environment provides rewards only when the arm reaches its target, making rewards extremely sparse. Which approach is most effective to accelerate learning?

A) Implement reward shaping to provide intermediate feedback.
B) Reduce the discount factor to prioritize immediate rewards.
C) Increase the replay buffer size.
D) Eliminate random exploration to focus on the current best policy.

Answer: A) Implement reward shaping to provide intermediate feedback.

Explanation:

 Sparse rewards pose a significant challenge in reinforcement learning because the agent receives infrequent signals about whether its actions are beneficial. Without consistent feedback, the agent struggles to learn the optimal policy, and learning converges extremely slowly or may fail altogether.

A) Reward shaping introduces additional feedback by providing intermediate rewards for partial progress toward the goal. For a robotic arm, this could mean giving small positive rewards for moving closer to the target or aligning correctly with the object. This increases the frequency of informative signals, guiding the agent toward successful strategies. Properly designed reward shaping preserves the optimal policy by providing consistent feedback proportional to progress, accelerating learning while avoiding unintended behaviors. Techniques like potential-based reward shaping mathematically ensure that the optimal policy under shaped rewards is the same as that under the original sparse rewards.

B) Reducing the discount factor emphasizes immediate rewards over long-term outcomes. In sparse reward settings, this can cause the agent to ignore actions that are critical to achieving the final goal because intermediate rewards are negligible, slowing or preventing effective learning.

C) Increasing the replay buffer allows the agent to store and reuse past experiences. While this can improve sample efficiency in dense-reward environments, it does not address the fundamental problem of sparse rewards. The replay buffer may primarily contain uninformative transitions, which do not help the agent learn meaningful policies.

D) Eliminating random exploration reduces the likelihood of discovering rewarding states. Exploration is crucial in sparse-reward environments to encounter positive feedback at all. Without exploration, the agent may never discover successful sequences of actions, preventing effective policy learning.

Reward shaping is the most effective approach for sparse-reward reinforcement learning tasks, as it provides consistent, informative feedback that accelerates convergence without compromising policy optimality. It is widely used in robotics, navigation tasks, and game-playing RL environments to overcome the inherent challenges of sparse rewards.

Question 87:

You are training a multi-class text classification model with thousands of categories. The training is slow due to the high output dimensionality. Which approach is most effective to reduce computation?

A) Use hierarchical softmax or sampled softmax.
B) Remove rare classes to reduce output size.
C) Train with very small batch sizes.
D) Apply L1 regularization to sparsify the model.

Answer: A) Use hierarchical softmax or sampled softmax.

Explanation:

 Large-scale multi-class classification poses a computational challenge because the softmax function requires calculating exponentials and normalizing over all classes, which becomes increasingly expensive as the number of categories grows. When there are thousands of classes, naive computation of the full softmax is costly in terms of both memory and computation.

A) Hierarchical softmax organizes classes into a tree structure. Computing the probability of a class involves traversing the tree from the root to the leaf node, reducing the computational complexity from O(n) to O(log n) per training example, where n is the number of classes. Sampled softmax approximates the full softmax by considering a subset of negative classes per training step, dramatically reducing computation while still providing unbiased gradient estimates. Both methods maintain model accuracy while improving efficiency. These techniques are particularly effective in natural language processing tasks, such as predicting words from large vocabularies or classifying documents into thousands of topics.

B) Removing rare classes reduces output dimensionality but eliminates potentially important categories, resulting in reduced model coverage and utility. In many applications, even rare classes are essential.

C) Training with small batch sizes reduces memory usage per batch but does not reduce the inherent computational cost of softmax over a large number of classes. It may also increase gradient variance, slowing convergence.

D) L1 regularization sparsifies the weights of the model but does not change the cost of computing softmax over all classes. The softmax computation remains a major bottleneck, and sparsifying weights alone is insufficient to improve training efficiency significantly.

Using hierarchical or sampled softmax is the most effective strategy for large-scale multi-class problems, allowing efficient training without sacrificing coverage or accuracy.

Question 88:

 You are building a convolutional neural network (CNN) for medical image segmentation. Some regions of interest (ROIs) are very small compared to the background. Which approach is most suitable to improve segmentation of small ROIs?

A) Use a loss function such as Dice loss or focal loss.
B) Increase convolutional kernel size.
C) Downsample images to reduce computational cost.
D) Use standard cross-entropy loss without modification.

Answer: A) Use a loss function such as Dice loss or focal loss.

Explanation

 Medical image segmentation often involves severe class imbalance, where the majority of pixels belong to the background, and ROIs occupy only a small fraction of the image. Standard cross-entropy loss treats all pixels equally, which causes the network to prioritize learning background features and ignore small, clinically important regions.

A) Dice loss directly measures the overlap between predicted masks and ground-truth masks, emphasizing correct segmentation of the foreground (ROI). It compensates for the imbalance by increasing the relative contribution of foreground pixels to the loss. Focal loss further improves learning by down-weighting easy-to-classify background pixels and focusing the network on hard examples, often corresponding to small ROIs. By combining these loss functions, the network can learn to segment small, critical structures accurately while maintaining overall mask quality. These approaches are widely adopted in medical imaging applications such as tumor segmentation, lesion detection, and organ delineation, where small regions are clinically significant.

B) Increasing convolutional kernel size captures a larger spatial context but does not address class imbalance. Small ROIs may still contribute minimally to the loss, leaving segmentation performance unchanged.

C) Downsampling images reduces computational cost but sacrifices fine details. Small ROIs may disappear entirely, making accurate segmentation impossible.

D) Standard cross-entropy biases the network toward the background, reducing sensitivity to small ROIs. Without modification, the network will underperform on clinically relevant regions.

Dice and focal loss effectively address the challenges of small ROIs, improving segmentation accuracy and clinical reliability.

Question 89:

 You are building a recommendation system for a video streaming platform. Many new movies are added daily, and most users have sparse interaction histories. Which approach is most effective?

A) Use a hybrid recommendation system combining collaborative filtering and content-based filtering.
B) Remove new movies from the recommendation pool.
C) Recommend only the most popular movies.
D) Rely solely on collaborative filtering.

Answer: A) Use a hybrid recommendation system combining collaborative filtering and content-based filtering.

Explanation:

 This scenario presents both cold-start problems: new users with limited interactions and new items with no interaction history. Collaborative filtering relies on historical interactions and fails for cold-start users or items, while content-based filtering uses item metadata (genre, description, actors) and user profiles to generate recommendations without historical interactions.

A) Hybrid recommendation systems combine collaborative filtering and content-based approaches. Content-based filtering handles cold-start scenarios by recommending items similar to known preferences, while collaborative filtering refines recommendations as more user-item interactions accumulate. For instance, a newly added documentary can be recommended to a user interested in similar genres based on metadata. Over time, collaborative filtering personalizes recommendations further by analyzing patterns among users. This approach improves coverage, relevance, and diversity of recommendations.

B) Removing new movies reduces the recommendation pool, preventing users from discovering fresh content and reducing engagement.

C) Recommending only popular movies maximizes short-term engagement but fails to provide personalized suggestions. Users with niche tastes may find recommendations irrelevant, decreasing satisfaction and retention.

D) Relying solely on collaborative filtering fails in cold-start scenarios, as the model cannot make meaningful predictions without sufficient interaction history.

Hybrid recommendation systems are the most practical solution for dynamic platforms, providing relevant recommendations even with sparse interactions and newly added items.

Question 90:

 You are developing a multi-label text classification model. Some labels are rare, resulting in low recall for these categories. Which approach is most effective?

A) Use binary cross-entropy with class weighting.
B) Remove rare labels from the dataset.
C) Treat the task as multi-class classification using categorical cross-entropy.
D) Train only on examples with frequent labels.

Answer: A) Use binary cross-entropy with class weighting.

Explanation:

 Multi-label classification allows each instance to belong to multiple categories. Rare labels are underrepresented, and standard loss functions underweight them, resulting in low recall and poor coverage.

A) Binary cross-entropy treats each label independently, making it ideal for multi-label tasks. Applying class weights inversely proportional to label frequency ensures rare labels contribute more to the loss function, encouraging the model to learn meaningful representations for these underrepresented categories. This approach improves recall for rare labels while maintaining performance on frequent labels. Weighted binary cross-entropy is widely used in multi-label problems, including document tagging, medical diagnosis, and multi-topic classification.

B) Removing rare labels simplifies the dataset but eliminates the ability to predict important categories, which may be critical for real-world applications.

C) Treating the problem as multi-class classification assumes each instance has only one label. This violates the multi-label assumption and ignores multiple rare labels in a single instance, reducing predictive performance.

D) Training only on frequent labels excludes rare categories entirely, guaranteeing low recall for these labels and reducing overall coverage.

Weighted binary cross-entropy is the most effective solution for handling rare labels in multi-label tasks, ensuring balanced learning and improved recall across all categories.

Question 91:

 You are developing a neural network to classify medical images for multiple diseases. Some diseases are very rare, leading to a highly imbalanced dataset. Which approach is most effective to improve performance for rare diseases?

A) Use class-weighted loss or focal loss to emphasize rare classes.
B) Remove rare disease classes from the dataset.
C) Train with standard cross-entropy loss without modification.
D) Reduce the number of convolutional layers to prevent overfitting.

Answer: A) Use class-weighted loss or focal loss to emphasize rare classes.

Explanation:

 Medical imaging datasets often have extreme class imbalances, with common conditions represented in thousands of images while rare diseases have very few examples. Training a neural network on such data with standard cross-entropy loss typically biases the model toward the majority classes, resulting in poor recall for rare diseases. High recall is crucial in medical applications, as missing a rare condition could have severe consequences for patient outcomes.

A) Class-weighted loss assigns higher importance to rare classes during training, ensuring that gradient updates are influenced more by rare disease examples. This compensates for the imbalance and allows the network to learn discriminative features for these underrepresented classes. Focal loss further improves performance by down-weighting easy-to-classify examples (commonly normal or majority class cases) and focusing learning on hard examples, which often correspond to rare diseases. Both techniques have been extensively used in medical image classification, such as for tumor detection, rare genetic disorders, or abnormal lesion identification, where class imbalance is severe. By emphasizing rare classes, the network achieves higher sensitivity without compromising accuracy for common classes.

B) Removing rare disease classes simplifies training but eliminates the ability to detect these critical conditions. In a clinical setting, this is unacceptable, as rare disease detection is often the primary goal.

C) Training with standard cross-entropy loss treats all samples equally, causing the network to prioritize the majority classes. While overall accuracy may appear high, recall for rare diseases will be extremely low, making the model ineffective in practice.

D) Reducing the number of convolutional layers may prevent overfitting but does not address class imbalance. The network will still underperform on rare classes due to lack of emphasis in the loss function.

Using class-weighted loss or focal loss is the most effective method to improve detection of rare diseases, ensuring that the network learns features for both common and rare classes while maintaining overall model robustness and clinical relevance.

Question 92:

You are building a recommendation system for an e-commerce platform. Most users have interacted with only a few products, and new items are constantly added. Which approach is most effective for generating relevant recommendations?

A) Use a hybrid recommendation system combining collaborative filtering and content-based filtering.
B) Remove new items from the recommendation pool.
C) Recommend only the most popular products.
D) Rely solely on collaborative filtering.

Answer: A) Use a hybrid recommendation system combining collaborative filtering and content-based filtering.

Explanation:

 In dynamic marketplaces, cold-start problems are common: new users have limited interaction history, and new items have no interactions. Collaborative filtering depends on historical user-item interactions and cannot provide meaningful recommendations in cold-start scenarios. Content-based filtering relies on item attributes (e.g., product category, description, specifications) and user profiles, allowing recommendations even when interaction history is sparse.

A) Hybrid recommendation systems combine the strengths of both approaches. Content-based filtering addresses cold-start scenarios by recommending items similar to those a user has already interacted with or shown interest in. Collaborative filtering personalizes recommendations over time as more interaction data accumulates. For example, a new smartphone can be recommended to a user interested in electronics based on specifications and category even before any purchases or ratings exist. Hybrid systems increase coverage, relevance, and diversity of recommendations, providing both immediate value and long-term personalization. They are widely used in large-scale e-commerce and streaming platforms to maintain user engagement and satisfaction.

B) Removing new items from the recommendation pool limits discovery of fresh products, reducing engagement and negatively impacting user experience.

C) Recommending only the most popular products maximizes short-term engagement but fails to provide personalization. Users with niche interests may find recommendations irrelevant, decreasing satisfaction and retention.

D) Relying solely on collaborative filtering fails in cold-start scenarios because new items or users have insufficient interaction history for meaningful predictions, leading to poor coverage and relevance.

Hybrid recommendation systems effectively balance cold-start handling and long-term personalization, making them the most practical solution for dynamic platforms with sparse interactions.

Question 93:

You are building a time series forecasting model for retail sales. The series exhibits trends, weekly cycles, seasonal spikes, and holiday effects. Which approach is most suitable?

A) Use a model capable of handling multiple seasonalities and trend, such as Prophet or TBATS.
B) Ignore seasonality and rely on standard ARIMA.
C) Train a linear regression on raw sales values.
D) Aggregate data to remove high-frequency fluctuations.

Answer: A) Use a model capable of handling multiple seasonalities and trend, such as Prophet or TBATS.

Explanation:

 Retail sales data are characterized by complex patterns: trends reflect long-term growth, weekly cycles capture recurring shopping behavior, seasonal spikes correspond to holiday shopping, and promotional effects create short-term variations. Accurately modeling these overlapping patterns is critical for inventory management, staffing, and supply chain planning.

A) Prophet decomposes time series into trend, multiple seasonal components, and holiday effects. It handles missing data, non-linear trends, and irregular seasonal spikes, making it highly suitable for retail sales forecasting. TBATS uses Fourier terms to model multiple seasonalities, along with Box-Cox transformations, ARMA errors, and trend components. Both approaches explicitly account for overlapping cycles and external effects, producing accurate forecasts that reflect daily, weekly, and yearly patterns, as well as holiday spikes. Accurate forecasts ensure operational efficiency, reduce stockouts or overstocks, and improve planning for marketing campaigns.

B) Standard ARIMA may capture trends and short-term autocorrelations but cannot model multiple overlapping seasonalities, resulting in systematic forecast errors during high-demand periods.

C) Linear regression on raw sales values is inadequate for capturing non-linear trends or multiple seasonalities. Predictions would fail to account for cyclic peaks and holiday effects, reducing forecast reliability.

D) Aggregating data smooths high-frequency fluctuations but eliminates critical patterns such as weekly cycles and holiday spikes, making forecasts less actionable.

Models designed for multiple seasonalities and trend decomposition provide the most accurate and operationally useful forecasts for complex retail sales data.

Question 94:

 You are training a multi-label text classification model. Some labels are rare, resulting in low recall for these categories. Which approach is most effective?

A) Use binary cross-entropy with class weighting.
B) Remove rare labels from the dataset.
C) Treat the task as multi-class classification using categorical cross-entropy.
D) Train only on examples with frequent labels.

Answer: A) Use binary cross-entropy with class weighting.

Explanation:

 Multi-label classification allows each instance to belong to multiple categories. Rare labels are underrepresented, causing standard loss functions to underweight them and resulting in low recall. Improving recall for rare labels is crucial in applications such as document tagging, medical diagnosis, and multi-topic classification.

A) Binary cross-entropy treats each label independently, making it suitable for multi-label problems. Applying class weights inversely proportional to label frequency ensures rare labels contribute more to the loss function, focusing learning on these underrepresented categories. Weighted binary cross-entropy improves recall for rare labels while maintaining performance on frequent labels. This technique is widely used in scenarios with highly imbalanced label distributions.

B) Removing rare labels simplifies the dataset but eliminates the ability to predict important categories, which may be critical in real-world applications.

C) Treating the problem as multi-class classification assumes each instance has only one label. This violates the multi-label assumption and ignores multiple rare labels in a single instance, reducing overall predictive performance.

D) Training only on frequent labels excludes rare categories entirely, guaranteeing poor recall and limiting coverage.

Weighted binary cross-entropy is the most effective approach for handling rare labels, ensuring balanced learning and improved recall across all categories.

Question 95:

You are training a convolutional neural network (CNN) for image classification. The model performs well on training data but poorly on validation data. Which approach is most effective to improve generalization?

A) Apply data augmentation techniques such as rotations, flips, and color jittering.
B) Increase the number of convolutional layers.
C) Reduce the number of filters in convolutional layers.
D) Train for fewer epochs to avoid overfitting.

Answer: A) Apply data augmentation techniques such as rotations, flips, and color jittering.

Explanation:

 Poor validation performance despite high training accuracy indicates overfitting. The network memorizes training data but fails to generalize to unseen data. Overfitting is common in CNNs trained on limited or low-diversity datasets.

A) Data augmentation artificially increases dataset diversity by applying transformations such as rotations, flips, scaling, cropping, and color jittering. This encourages the network to learn invariant features rather than memorizing specific examples. For instance, an object classifier should recognize a cat regardless of rotation or partial occlusion. Data augmentation reduces overfitting, improves generalization, and is widely used in computer vision tasks including object detection, medical imaging, and wildlife classification.

B) Increasing convolutional layers increases model capacity, which may exacerbate overfitting if training data remains limited. More layers alone do not address insufficient data diversity.

C) Reducing the number of filters decreases model capacity, which can prevent overfitting in some cases, but risks underfitting, where the network cannot learn sufficient features for accurate classification.

D) Training for fewer epochs may reduce overfitting but risks undertraining, preventing the network from learning essential patterns.

Data augmentation is the most effective approach for improving generalization in CNNs by providing realistic variability, allowing the network to perform well on unseen data without sacrificing accuracy.

Question 96:

You are developing a reinforcement learning agent to play a video game. The agent receives rewards only when reaching the final goal, making rewards extremely sparse. Which approach is most effective to accelerate learning?

A) Implement reward shaping to provide intermediate feedback.
B) Reduce the discount factor to prioritize immediate rewards.
C) Increase the replay buffer size.
D) Eliminate random exploration to focus on the current best policy.

Answer: A) Implement reward shaping to provide intermediate feedback.

Explanation:

 Sparse rewards in reinforcement learning make it extremely difficult for the agent to learn an effective policy because positive feedback is rare. Without consistent signals, the agent’s policy may converge very slowly or not at all. In a video game, reaching the final goal might require hundreds of sequential actions, and without intermediate rewards, the agent has no guidance on which sequences lead to success.

A) Reward shaping introduces additional feedback for partial progress toward the goal. For example, in a maze, the agent could receive small positive rewards for moving closer to the exit. Properly designed reward shaping maintains the original optimal policy while providing frequent, informative signals. Potential-based reward shaping guarantees that the shaped rewards do not alter the optimal policy while improving learning efficiency. This approach is widely used in robotic control, navigation, and video game AI to accelerate learning under sparse reward conditions.

B) Reducing the discount factor emphasizes immediate rewards over long-term outcomes. In sparse reward settings, this can prevent the agent from learning policies that achieve distant goals because intermediate rewards are minimal or absent, slowing or preventing effective learning.

C) Increasing the replay buffer allows the agent to store and reuse past experiences, improving sample efficiency. However, in sparse reward environments, the replay buffer primarily contains uninformative transitions, which do not help the agent learn useful strategies.

D) Eliminating random exploration reduces the likelihood of encountering positive rewards in the first place. Exploration is crucial in sparse-reward settings to discover sequences of actions that lead to success. Without exploration, the agent may never experience rewarding states, preventing policy improvement.

Reward shaping is the most effective approach for sparse-reward reinforcement learning because it provides consistent, informative feedback that accelerates convergence and ensures learning of the optimal policy.

Question 97:

 You are building a multi-class text classification model with thousands of categories. Training is slow due to the high output dimensionality. Which approach is most effective to reduce computation?

A) Use hierarchical softmax or sampled softmax.
B) Remove rare classes to reduce output size.
C) Train with very small batch sizes.
D) Apply L1 regularization to sparsify the model.

Answer: A) Use hierarchical softmax or sampled softmax.

Explanation:

 Large-scale multi-class classification is computationally expensive because computing the softmax requires calculating exponentials and normalizing over all classes. With thousands of classes, this computation becomes a significant bottleneck in training neural networks.

A) Hierarchical softmax organizes classes into a tree structure. To compute the probability of a class, the model traverses from the root to the leaf, reducing computational complexity from O(n) to O(log n), where n is the number of classes. Sampled softmax approximates the full softmax by considering only a subset of negative classes per training step. Both methods dramatically reduce computation while maintaining model accuracy. Hierarchical and sampled softmax are widely used in NLP tasks such as language modeling, word prediction, and document classification with large vocabularies.

B) Removing rare classes reduces output dimensionality but eliminates coverage for important categories. In many applications, even rare classes are critical and cannot be ignored.

C) Training with very small batch sizes reduces memory usage per batch but does not reduce the inherent computational cost of softmax over thousands of classes. Small batches may also increase gradient variance, slowing convergence.

D) L1 regularization sparsifies the model weights but does not affect the cost of computing softmax. The computational bottleneck remains, and sparsifying weights alone is insufficient to improve efficiency significantly.

Hierarchical or sampled softmax provides the most effective solution for large-scale multi-class problems, enabling efficient training without sacrificing accuracy or coverage.

Question 98:

 You are training a convolutional neural network (CNN) for medical image segmentation. Some regions of interest (ROIs) are very small compared to the background. Which approach is most effective?

A) Use a loss function such as Dice loss or focal loss.
B) Increase convolutional kernel size.
C) Downsample images to reduce computational cost.
D) Use standard cross-entropy loss without modification.

Answer: A) Use a loss function such as Dice loss or focal loss.

Explanation:

 Medical image segmentation often involves extreme class imbalance, with most pixels belonging to the background and small ROIs representing critical regions. Standard cross-entropy loss treats all pixels equally, causing the network to prioritize learning background features and ignore small, clinically important regions.

A) Dice loss directly measures the overlap between predicted masks and ground-truth masks, emphasizing correct segmentation of the foreground (ROI). Focal loss down-weights easy-to-classify background pixels and focuses on hard examples, which often correspond to small ROIs. Combining these losses allows the network to learn accurate segmentation for small structures while maintaining overall mask quality. Dice and focal loss are widely used in medical imaging applications, including tumor segmentation, lesion detection, and organ delineation, where small ROIs are clinically significant.

B) Increasing convolutional kernel size captures a larger spatial context but does not address class imbalance. Small ROIs may still contribute minimally to the loss, leaving segmentation performance unchanged.

C) Downsampling images reduces computational cost but sacrifices fine details, making small ROIs harder or impossible to detect.

D) Standard cross-entropy biases the network toward background pixels, reducing sensitivity to small ROIs. Without modification, the network will underperform on clinically important regions.

Dice and focal loss directly address the challenges of small ROIs, improving segmentation accuracy and clinical reliability.

Question 99

You are building a recommendation system for a video streaming platform. Many new movies are added daily, and most users have sparse interaction histories. Which approach is most effective?

A) Use a hybrid recommendation system combining collaborative filtering and content-based filtering.
B) Remove new movies from the recommendation pool.
C) Recommend only the most popular movies.
D) Rely solely on collaborative filtering.

Answer: A) Use a hybrid recommendation system combining collaborative filtering and content-based filtering.

Explanation:

This scenario presents both cold-start problems: new users with sparse interaction histories and new items with no interaction data. Collaborative filtering relies on user-item interactions and cannot make meaningful recommendations when history is limited. Content-based filtering uses metadata (genre, description, actors, keywords) to generate recommendations without requiring historical interactions.

A) Hybrid systems combine collaborative filtering and content-based filtering. Content-based filtering handles cold-start users and items by recommending items similar to those a user has interacted with or expressed interest in. Collaborative filtering refines recommendations over time as more interaction data accumulates. For example, a newly released sci-fi movie can be recommended to a user who watches similar genres based on metadata, even without interaction data. Hybrid systems improve coverage, relevance, and personalization while mitigating cold-start challenges.

B) Removing new movies reduces the recommendation pool and prevents users from discovering new content, reducing engagement.

C) Recommending only popular movies maximizes short-term engagement but lacks personalization, making recommendations irrelevant for users with niche interests.

D) Relying solely on collaborative filtering fails in cold-start scenarios because new users and items lack interaction data, resulting in poor coverage and relevance.

Hybrid recommendation systems are the most effective solution for dynamic streaming platforms, ensuring personalized and relevant recommendations despite sparse interactions and newly added content.

Question 100:

You are training a multi-label text classification model. Some labels are rare, resulting in low recall. Which approach is most effective?

A) Use binary cross-entropy with class weighting.
B) Remove rare labels from the dataset.
C) Treat the task as multi-class classification using categorical cross-entropy.
D) Train only on examples with frequent labels.

Answer: A) Use binary cross-entropy with class weighting.

Explanation:

In multi-label classification, each instance can belong to multiple categories. Rare labels are underrepresented, and standard loss functions often underweight them, resulting in low recall. Improving recall for rare labels is critical in applications such as medical coding, document tagging, or multi-topic classification.

A) Binary cross-entropy treats each label independently, making it suitable for multi-label tasks. Applying class weights inversely proportional to label frequency ensures rare labels contribute more to the loss function, forcing the model to learn meaningful representations for these underrepresented categories. This improves recall for rare labels while maintaining performance on frequent labels. Weighted binary cross-entropy is widely adopted in scenarios with highly imbalanced label distributions to ensure coverage and accuracy across all categories.

B) Removing rare labels simplifies training but eliminates the ability to predict important categories, which may be essential in practice.

C) Treating the problem as multi-class classification assumes each instance has only one label, which violates the multi-label structure and ignores multiple rare labels in a single instance, reducing predictive performance.

D) Training only on frequent labels ignores rare categories entirely, guaranteeing low recall for these labels and limiting overall coverage.

Weighted binary cross-entropy is the most effective solution to improve performance on rare labels, ensuring balanced learning and high recall across all categories while maintaining overall predictive accuracy.