Google Professional Machine Learning Engineer Exam  Dumps and Practice Test Questions Set 4 Q 61-80

Visit here for our full Google Professional Machine Learning Engineer exam dumps and practice test questions.

Question 61:

You are building a recommendation system for a video streaming platform. Many new items are being added daily, and users often have sparse interaction histories. Which approach is most effective for providing relevant recommendations in this scenario?

A) Use a hybrid recommendation system combining collaborative filtering and content-based filtering.
B) Remove new items from the recommendation pool.
C) Recommend only popular items to all users.
D) Rely solely on matrix factorization for collaborative filtering.

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

Explanation:

The scenario presents both a cold-start problem for new users and new items. Collaborative filtering relies on user-item interaction histories, which are sparse or missing for newcomers. Content-based filtering, on the other hand, leverages item metadata (e.g., genre, description, director) and user profiles (e.g., demographics or preferences) to generate recommendations without historical interaction data.

A) Hybrid systems integrate collaborative filtering and content-based filtering. For new users or items, content-based methods provide initial recommendations based on features and profiles. As interaction data accumulates, collaborative filtering refines recommendations using similarity patterns. This approach balances short-term cold-start handling with long-term personalization, improving user engagement and satisfaction. For example, a newly added movie can be recommended to a user based on its genre and actors, even before any ratings exist.

B) Removing new items ignores a significant portion of the content catalog. Users would miss fresh content, decreasing engagement and undermining the system’s goal of discovery.

C) Recommending only popular items maximizes short-term accuracy but fails to provide personalized suggestions. Users with niche interests may find the recommendations irrelevant, reducing overall satisfaction and retention.

D) Relying solely on collaborative filtering fails for new users and new items. Without sufficient interactions, the model cannot learn meaningful latent embeddings, resulting in poor recommendations and user experience.

Using a hybrid recommendation system effectively addresses both cold-start challenges and long-term personalization, making it the most practical solution in dynamic content environments.

Question 62:

 You are training a deep learning model for detecting rare diseases in medical images. The dataset is highly imbalanced, with very few positive samples. Which approach is most suitable to improve performance?

A) Use class-weighted loss or focal loss to emphasize minority class samples.
B) Remove negative samples to balance the dataset.
C) Apply standard cross-entropy loss without modification.
D) Reduce the network size to prevent overfitting.

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

Explanation:

 Medical datasets for rare diseases are often imbalanced, with far fewer positive examples than negatives. Standard training methods tend to bias models toward the majority class, resulting in poor recall for the minority class, which is critical in clinical applications.

A) Class-weighted loss increases the contribution of minority class examples to the overall loss, forcing the model to pay more attention to these rare cases. Focal loss further improves learning by down-weighting easy examples (mostly negative samples) and focusing updates on hard-to-classify examples, often corresponding to rare positive cases. These methods enhance sensitivity, ensuring that the model detects rare diseases while maintaining overall stability. Focal loss, in particular, is effective in handling extreme imbalance without requiring oversampling.

B) Removing negative samples balances the dataset but discards valuable information about normal cases. This reduces generalization and may cause the model to overfit positive samples, leading to high false positives.

C) Using standard cross-entropy treats all samples equally, causing the model to bias toward negative examples due to their overwhelming frequency. Rare diseases are underrepresented, and the model will likely fail to detect them reliably.

D) Reducing network size decreases capacity and may help prevent overfitting, but it does not address the fundamental class imbalance. The model may still underperform on minority classes.

Class-weighted or focal loss is the most effective approach for handling imbalanced medical datasets, ensuring accurate detection of rare but critical disease cases.

Question 63:

You are developing a reinforcement learning (RL) agent in an environment with sparse rewards. The agent rarely encounters positive feedback, causing slow learning. Which approach is most effective?

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

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

Explanation:

 Sparse rewards create a challenge in RL because the agent receives infrequent signals about the quality of its actions. Without feedback, learning is slow, and policy updates may not converge toward optimal behavior.

A) Reward shaping provides additional, intermediate rewards for partial progress toward the goal. For example, in a maze navigation task, the agent might receive rewards for moving closer to the exit, picking up intermediate objects, or achieving sub-goals. This increases the frequency and informativeness of feedback, guiding the agent toward the optimal policy more efficiently. Properly designed reward shaping accelerates convergence without altering the optimal policy, as long as it is consistent with the original reward function.

B) Reducing the discount factor emphasizes immediate rewards but does not create new feedback. In sparse-reward settings, this may lead to myopic policies that fail to pursue long-term objectives.

C) Increasing the replay buffer stores more experiences but does not address the scarcity of positive signals. The agent may repeatedly replay uninformative transitions, resulting in slow learning.

D) Eliminating random exploration reduces the likelihood of discovering positive rewards. Exploration is critical in sparse-reward environments to find sequences of actions that lead to rewards. Removing it would hinder learning further.

Reward shaping is the most effective strategy for sparse-reward RL environments, providing more informative feedback and accelerating learning without compromising policy optimality.

Question 64:

 You are building a multi-class text classification model with thousands of categories. Training is slow and memory usage is high. Which approach is most effective?

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

Answer A) Use hierarchical softmax or sampled softmax to reduce computation.

Explanation:

 Large-scale multi-class classification poses computational and memory challenges because calculating the full softmax requires normalization over all classes. This becomes increasingly expensive with thousands of classes.

A) Hierarchical softmax organizes classes into a tree structure, reducing computational complexity from O(n) to O(log n) per example, 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 reduce memory usage and speed up training while maintaining prediction quality. They are widely used in NLP applications such as word prediction and document classification with very large vocabularies or label sets.

B) Removing rare classes reduces dimensionality but discards valuable information and decreases coverage. In many applications, rare classes are important for completeness or accuracy.

C) Training with small batch sizes reduces memory per batch but increases gradient variance, which may slow convergence. It does not address the fundamental computational cost of the softmax operation.

D) L1 regularization sparsifies weights, reducing memory usage indirectly, but it does not reduce the O(n) cost of computing softmax over thousands of classes.

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

Question 65:

 You are developing a time series forecasting model for retail demand. The data exhibits multiple seasonalities, such as weekly and yearly cycles. Which approach is most appropriate?

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

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

Explanation:

 Retail demand often contains complex seasonal patterns, including weekly shopping cycles and yearly holiday peaks. Standard time series models may struggle to capture overlapping patterns accurately.

A) Prophet decomposes the series into trend, multiple seasonal components, and holiday effects, enabling flexible modeling of overlapping cycles. TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend, Seasonal components) models multiple seasonalities using Fourier terms, capturing both short-term and long-term cycles. These models also handle missing values and non-linear trends. By explicitly modeling multiple seasonal patterns, forecasts become more accurate, supporting operational decisions like inventory management and staffing.

B) Ignoring seasonality with standard ARIMA may capture trends but cannot model complex overlapping cycles. This results in systematic errors during peaks and troughs, reducing forecast reliability.

C) Linear regression on raw values is insufficient to model non-linear or cyclic behavior. Predictions would miss seasonal spikes, leading to poor accuracy.

D) Aggregating data removes seasonal fluctuations but sacrifices granularity. Important patterns for decision-making are lost, reducing the forecast’s practical usefulness.

Using models designed for multiple seasonalities, such as Prophet or TBATS, ensures accurate and reliable forecasts by capturing overlapping cyclic patterns critical in retail demand prediction.

Question 66:

 You are building a neural network to classify images of wildlife. Some classes, like common animals, have thousands of images, while rare species have only a few dozen. Which approach is most suitable to improve classification performance for rare classes?

A) Use class-weighted loss or focal loss to emphasize rare classes.
B) Remove rare classes to simplify training.
C) Increase the number of convolutional layers to capture more features.
D) Train only on examples from the common classes.

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

Explanation:

 Class imbalance is a significant challenge in wildlife image classification because rare species contribute very few examples. Standard training treats all samples equally, causing the model to favor common classes and neglect rare species.

A) Class-weighted loss assigns higher importance to rare classes, ensuring that the network pays attention to these samples during gradient updates. Focal loss focuses on hard-to-classify examples, often corresponding to rare classes, by down-weighting easy examples from common classes. This combination improves recall for rare species without compromising performance on common species. In practice, these techniques are widely used in imbalanced image classification tasks, including wildlife monitoring, medical imaging, and fraud detection.

B) Removing rare classes simplifies training but eliminates the ability to classify them, which is counterproductive for real-world monitoring applications. Rare species are often the ones of greatest interest for conservation.

C) Increasing convolutional layers enhances feature extraction but does not address class imbalance. Without weighting, the network will still prioritize common classes during learning.

D) Training only on common classes ignores rare species entirely, resulting in a model that cannot identify them, defeating the purpose of biodiversity monitoring.

Using class-weighted or focal loss directly addresses imbalance, improving classification for rare species while maintaining overall accuracy, making it the most effective approach.

Question 67:

 You are developing a reinforcement learning (RL) agent to play a video game with sparse rewards. The agent rarely receives positive feedback, causing slow learning. Which approach is most effective?

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

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

Explanation:

 Sparse rewards make it difficult for RL agents to learn because they receive little guidance on which actions lead to success. Without sufficient feedback, the agent struggles to update its policy effectively.

A) Reward shaping introduces intermediate rewards for partial progress toward the ultimate goal. For example, in a maze game, the agent might receive rewards for approaching the exit or collecting intermediate items. This increases the frequency of informative feedback, guiding the agent toward optimal strategies more efficiently. Properly designed reward shaping preserves the optimal policy while improving convergence speed. It is widely used in RL environments with sparse rewards, including robotics, navigation tasks, and games.

B) Reducing the discount factor emphasizes immediate rewards but does not introduce additional feedback. In sparse-reward environments, this can lead to myopic behavior where the agent ignores long-term objectives.

C) Increasing the replay buffer stores more past experiences but does not address the scarcity of positive feedback. The agent may repeatedly replay uninformative transitions, slowing learning.

D) Eliminating random exploration limits the agent to its current policy, reducing the chance of discovering rare reward states. Exploration is crucial in sparse-reward environments to identify successful strategies.

Reward shaping is the most effective approach, providing frequent and meaningful feedback that accelerates learning without compromising the optimal policy.

Question 68:

 You are building a time series model for forecasting sales data. The series exhibits multiple seasonal patterns, including weekly cycles and yearly holiday spikes. Which approach is most suitable?

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

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

Explanation:

 Sales data often contains overlapping seasonal patterns. Weekly cycles capture recurring shopping habits, while yearly spikes correspond to holidays or promotions. Standard models that ignore multiple seasonalities may fail to capture these patterns, leading to poor forecasts.

A) Prophet decomposes the time series into trend, multiple seasonal components, and holiday effects, allowing flexible modeling of overlapping cycles. TBATS uses Fourier terms to model complex seasonality, along with Box-Cox transformations, ARMA errors, and trend components. These models can handle non-linear trends, missing data, and irregular seasonal patterns. By explicitly modeling multiple seasonalities, they provide accurate forecasts crucial for inventory planning, staffing, and promotion scheduling.

B) Standard ARIMA may capture trends or short-term autocorrelation but cannot model multiple overlapping seasonalities, leading to systematic forecast errors during peak periods.

C) Linear regression on raw values cannot account for cyclical behavior or non-linear trends, resulting in biased predictions.

D) Aggregating data reduces variability and smooths out seasonality, sacrificing granularity. Important peaks and troughs are lost, reducing the forecast’s usefulness for operational planning.

Using models designed for multiple seasonalities ensures accurate forecasts and allows businesses to plan effectively for complex, cyclical sales patterns.

Question 69:

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

A) Use binary cross-entropy with class weighting to emphasize rare labels.
B) Remove rare labels from the dataset.
C) Treat the problem 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 to emphasize rare labels.

Explanation:

 In multi-label classification, each instance may belong to multiple categories. Rare labels often appear infrequently, and standard loss functions underweight them, resulting in low recall.

A) Binary cross-entropy treats each label independently, making it appropriate for multi-label tasks. Applying class weights inversely proportional to label frequency ensures that rare categories contribute more to the loss, encouraging the model to learn their patterns. This improves recall for rare labels while maintaining performance on frequent labels. Weighted binary cross-entropy is widely used in multi-label applications such as document tagging, medical diagnosis, and multi-topic classification.

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

C) Treating the task as multi-class classification assumes only one label per instance, violating the multi-label structure. Rare labels in multi-label instances would be ignored, reducing model effectiveness.

D) Training only on frequent labels excludes rare labels entirely from the learning process, ensuring poor recall for these categories.

Weighted binary cross-entropy is the most effective method for handling rare labels, ensuring that the model learns meaningful representations for all categories.

Question 70:

You are building a CNN for image classification. The model performs well on the training set 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 to improve feature extraction.
C) Reduce the number of filters to simplify the model.
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 model memorizes training examples but fails to generalize to unseen data.

A) Data augmentation artificially increases the diversity of the training dataset by applying transformations such as rotations, flips, scaling, cropping, and color jitter. This forces the network to learn features invariant to these changes, improving robustness. For instance, an animal recognition model should identify a cat whether it is rotated or partially occluded. Data augmentation reduces overfitting, improves generalization, and is widely used in computer vision tasks.

B) Increasing convolutional layers increases capacity but may worsen overfitting. More layers do not address the fundamental issue of insufficient variability in the training set.

C) Reducing filters reduces capacity, which may prevent overfitting in some cases, but can lead to underfitting, failing to capture sufficient feature representations.

D) Training for fewer epochs may reduce overfitting but risks undertraining the model. If the model does not learn sufficient patterns, performance on both training and validation sets suffers.

Data augmentation is the most effective and widely used method to improve generalization in CNNs by providing realistic variability without altering model architecture or dataset size.

Question 71:

 You are building a deep learning model for predicting patient outcomes from electronic health records (EHRs). The dataset contains missing values in key lab test features. Which preprocessing approach is most appropriate?

A) Impute missing values using median or domain-specific defaults.
B) Remove all records with missing values.
C) Replace missing values with zero without consideration of context.
D) Use only the features without missing values.

Answer: A) Impute missing values using median or domain-specific defaults.

Explanation:

EHR datasets are notorious for missing data due to irregular testing schedules, human error, or data entry inconsistencies. Handling missing values correctly is crucial because discarding or poorly imputing data can significantly degrade model performance and bias predictions.

A) Imputation using the median of a feature or domain-specific defaults (e.g., normal reference ranges) maintains the dataset’s size while providing meaningful values for missing entries. Median imputation is robust to outliers and preserves feature distribution, reducing the risk of skewing the data. Domain-specific imputation allows healthcare knowledge to guide substitutions—for example, using age-specific normal ranges for lab tests. This approach enables models to utilize all records while reducing bias introduced by arbitrary or naive filling methods. Additionally, for time-dependent features, forward or backward filling based on previous patient measurements can capture temporal continuity.

B) Removing all records with missing values (complete-case analysis) reduces dataset size, potentially eliminating important subpopulations. For medical datasets, missingness is often non-random—patients with severe conditions may have more frequent lab tests, so discarding incomplete records can introduce significant bias.

C) Replacing missing values with zero assumes a neutral or baseline meaning for missing entries. In medical datasets, zero may not represent the physiological norm and can lead to nonsensical values, confusing the model and creating artifacts in predictions.

D) Using only features without missing values may discard highly predictive variables, reducing the richness of the input data and model performance. Many important lab results or clinical metrics are partially missing but crucial for outcome prediction.

Imputation using median or domain-specific defaults is the most appropriate strategy, preserving dataset size, maintaining feature distributions, and incorporating domain knowledge to produce meaningful values for missing entries, which enhances model reliability in predicting patient outcomes.

Question 72:

You are building a recommendation system for an e-commerce platform. Users have sparse interaction histories, and new products are added frequently. Which approach is most suitable to provide relevant recommendations?

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

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

Explanation:

 Sparse interactions and frequent addition of new items present both cold-start problems for users and items. Collaborative filtering relies on historical interactions, which are limited for new users or products, while content-based filtering leverages item attributes such as category, description, or metadata.

A) Hybrid systems combine collaborative filtering and content-based methods. Content-based filtering can recommend new products based on features, while collaborative filtering refines suggestions as more user interactions accumulate. This approach balances cold-start handling and long-term personalization. For example, a newly added camera can be recommended to a user interested in photography based on its specifications, even before any reviews exist. Over time, collaborative filtering improves recommendations as interactions accumulate.

B) Removing new products limits content discovery and reduces engagement, particularly when users expect to see the latest items.

C) Recommending only popular items maximizes short-term accuracy but fails to provide personalized recommendations, reducing user satisfaction for niche preferences.

D) Relying solely on collaborative filtering fails in cold-start scenarios because the model cannot recommend new products with no interaction history, leading to poor coverage and engagement.

Hybrid systems effectively address the challenges of sparse interactions and dynamic catalogs, providing both relevant and personalized recommendations.

Question 73:

 You are training a convolutional neural network (CNN) for medical image segmentation. Some regions of interest (ROIs) are very small, leading to poor detection. 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, where the majority of pixels correspond to the background, and ROIs occupy small portions of the image. Standard cross-entropy treats each pixel equally, causing the model to bias toward the background.

A) Dice loss measures the overlap between predicted and true masks, emphasizing small structures. It effectively balances foreground and background contributions in the loss, improving sensitivity to small ROIs. Focal loss emphasizes hard-to-classify pixels, typically corresponding to small or rare structures. Combining these approaches allows the network to learn accurate segmentation for clinically relevant regions while maintaining overall mask quality. They are widely used in medical imaging, including tumor detection and anatomical segmentation.

B) Increasing kernel size captures larger context but does not address pixel imbalance. Small ROIs may still contribute minimally to the loss and remain poorly segmented.

C) Downsampling reduces computational cost but loses fine details, making small ROIs even harder to detect.

D) Standard cross-entropy biases the network toward background pixels, reducing sensitivity to small ROIs.

Using Dice or focal loss directly addresses class imbalance at the pixel level, enhancing segmentation performance for small but critical structures.

Question 74:

You are training a multi-label text classification model. Some labels are rare, causing poor recall. Which approach is most appropriate?

A) Use binary cross-entropy with class weighting.
B) Remove rare labels.
C) Treat the problem as multi-class classification.
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 appear infrequently, and standard loss functions underweight them, leading to low recall.

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

B) Removing rare labels simplifies training but eliminates the ability to predict them, which may be unacceptable in real-world applications where rare labels carry important information.

C) Treating the task as multi-class classification assumes each instance has only one label. Rare labels would be ignored in multi-label instances, reducing model effectiveness.

D) Training only on frequent labels ignores rare labels entirely, ensuring they remain undetected, reducing recall.

Weighted binary cross-entropy is the most effective approach to handle rare labels in multi-label classification, ensuring improved recall and balanced learning.

Question 75:

You are building a time series forecasting model for electricity demand. The series exhibits 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 demand exhibits complex patterns including long-term trends, daily cycles, and seasonal spikes due to holidays or weather. Accurate forecasting requires models that can handle overlapping patterns.

A) Prophet decomposes the time series into trend, multiple seasonal components, and holiday effects. TBATS models multiple seasonalities using Fourier terms, Box-Cox transformation, ARMA errors, and trend components. These models handle missing data and non-linear trends, providing accurate forecasts for operational planning, grid management, and energy procurement.

B) Standard ARIMA may capture trends or short-term autocorrelation but cannot handle multiple seasonal patterns, leading to systematic errors during peaks and troughs.

C) Linear regression on raw values cannot capture cyclic or non-linear behavior, resulting in biased forecasts.

D) Aggregating data smooths high-frequency patterns, sacrificing granularity. Important daily or seasonal peaks are lost, reducing operational usefulness.

Models designed for multiple seasonalities, such as Prophet or TBATS, ensure accurate forecasts and reliable planning for electricity consumption.

Question 76:

You are training a deep learning model for detecting anomalies in network traffic. The dataset contains a vast number of normal traffic examples and very few anomalies. Which approach is most effective to improve detection of anomalies?

A) Use anomaly detection techniques or class-weighted loss.
B) Remove normal traffic examples to balance the dataset.
C) Train with standard cross-entropy loss without adjustment.
D) Reduce model complexity to prevent overfitting.

Answer: A) Use anomaly detection techniques or class-weighted loss.

Explanation:

 Anomaly detection in network traffic is a classic case of highly imbalanced datasets, where normal traffic dominates and anomalies (intrusions, attacks, or unusual patterns) are rare. Detecting these anomalies is critical for cybersecurity. The challenge is that standard supervised training with cross-entropy loss tends to bias toward the majority class, making the model excellent at classifying normal traffic but poor at identifying rare anomalies.

A) Using anomaly detection techniques, such as autoencoders, isolation forests, or one-class SVMs, focuses learning on modeling normal behavior. Any deviation from the learned normal patterns is flagged as anomalous. Autoencoders, for example, learn to reconstruct normal traffic features efficiently, but anomalies reconstruct poorly, producing high reconstruction errors that can be thresholded to detect outliers. Additionally, in a supervised learning context, applying class-weighted loss increases the contribution of rare anomaly examples to the overall loss, forcing the network to focus on learning discriminative features for anomalies. These techniques, combined with careful preprocessing (feature scaling, categorical encoding, and temporal aggregation), allow the model to detect rare but critical events effectively.

B) Removing normal traffic examples reduces the class imbalance artificially but also discards valuable data that defines the typical behavior of the network. Without a robust model of normal patterns, the model may produce a high false positive rate, incorrectly flagging normal traffic as anomalous. In cybersecurity, false positives are costly, causing unnecessary alerts, analyst fatigue, and reduced trust in the system.

C) Training with standard cross-entropy loss without adjustment will bias the model toward the majority class, as the loss function is dominated by normal traffic examples. This leads to a model that achieves high overall accuracy (since normal traffic is the majority) but fails to detect the rare anomalies, rendering the system ineffective in practice.

D) Reducing model complexity may prevent overfitting but does not address the fundamental imbalance problem. The model will still underperform on rare anomalies because the training procedure does not emphasize learning from those examples.

In the combination of anomaly detection techniques or class-weighted loss ensures the model learns both the patterns of normal traffic and the distinguishing features of anomalies. Anomaly detection methods are particularly suited when labeled anomaly examples are scarce, while class weighting ensures that when labels exist, the network gives sufficient attention to rare cases. Together, they provide robust detection, minimize false negatives, and maintain operational utility in real-world network monitoring scenarios, where missing even a single anomalous event can have serious consequences.

Question 77:

 You are developing a recommendation system for a streaming platform. Many new movies are added daily, and most users have interacted with only a few items. Which approach is most suitable to provide relevant recommendations under these conditions?

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 two classic cold-start problems: new users with sparse interactions and new items with no interaction history. Collaborative filtering relies on historical interactions to learn similarities between users or items, but it fails when there are few interactions. Content-based filtering leverages item metadata (e.g., genre, cast, description) or user profile information to make recommendations without relying on historical interactions.

A) A hybrid recommendation system combines both approaches. Content-based methods handle the cold-start problem by recommending new items based on similarity to previously liked items. For example, a user who likes action movies can be recommended a newly added action movie even if no one has rated it yet. As more interactions occur, collaborative filtering contributes to personalization, finding patterns among similar users and refining recommendations. Hybrid systems achieve a balance between immediate usefulness (content-based) and long-term personalization (collaborative filtering). In practice, this approach increases coverage, diversity, and relevance of recommendations, ensuring that users see both popular and niche content appropriate for their tastes.

B) Removing new movies limits the recommendation pool and reduces user engagement. Users expect to discover fresh content, and excluding new movies negatively affects the user experience.

C) Recommending only popular movies maximizes short-term engagement but fails to provide personalization. Users with unique or niche tastes may find recommendations irrelevant, leading to decreased satisfaction and retention.

D) Relying solely on collaborative filtering fails for new items and users because the model cannot generate meaningful predictions without sufficient interaction data. Recommendations for cold-start scenarios would be poor or impossible.

By combining collaborative filtering with content-based approaches, a hybrid system effectively mitigates cold-start challenges while providing personalized recommendations. This ensures relevant suggestions for new users and newly added items while maintaining high overall recommendation quality.

Question 78:

 You are building a time series forecasting model for retail demand. The series exhibits complex patterns, including weekly cycles, yearly spikes, and promotional effects. Which approach is most suitable?

A) Use a model capable of handling 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 seasonal fluctuations.

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

Explanation:

 Retail demand exhibits overlapping seasonalities and trends, including weekly shopping patterns, annual holidays, and promotional campaigns. Capturing these patterns is critical for accurate forecasting, inventory management, and operational planning.

A) Prophet decomposes the time series into trend, multiple seasonal components, and holiday effects. It is highly flexible, can handle missing data, and models non-linear trends. TBATS incorporates Fourier terms for multiple seasonalities, Box-Cox transformations, ARMA errors, trend components, and seasonal adjustments. Both models explicitly account for overlapping cycles, allowing forecasts to reflect weekly behaviors and annual peaks, as well as the effects of promotions and holidays. Accurate modeling of these patterns ensures better stock planning, reduces inventory shortages or overstock, and allows more efficient resource allocation.

B) Ignoring seasonality with standard ARIMA may capture trends and short-term autocorrelations but fails to capture multiple overlapping cycles. This results in systematic forecast errors during high-demand periods, which could affect supply chain efficiency.

C) Linear regression on raw values cannot model non-linear trends or multiple seasonalities. Predictions would miss cyclic peaks, leading to inaccurate demand forecasts and poor operational decisions.

D) Aggregating data smooths high-frequency fluctuations, reducing noise but also eliminating critical seasonal patterns. Peaks corresponding to promotional periods or weekly cycles are lost, resulting in forecasts that are less actionable.

Using models designed for multiple seasonalities ensures accurate representation of complex temporal patterns, improving operational planning and overall business efficiency.

Question 79:

 You are training a multi-label text classification model. Some labels are rare, causing poor recall. Which approach is most effective to improve predictions for these 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 with 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, and rare labels are underrepresented. Standard loss functions often underweight rare categories, leading to 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. This focuses learning on underrepresented categories, improving recall without negatively impacting performance on frequent labels. Weighted binary cross-entropy is widely used in document tagging, medical diagnosis, and multi-topic classification, ensuring all categories, including rare ones, are learned effectively.

B) Removing rare labels simplifies the dataset but eliminates the ability to predict these important categories, which may be critical in applications where rare events carry significant importance.

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

D) Training only on frequent labels ignores rare categories entirely, guaranteeing poor recall for these labels.

Weighted binary cross-entropy is the most effective approach to address rare label imbalance, ensuring improved recall and balanced learning across all categories.

Question 80:

You are building a 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:

The scenario described indicates overfitting, where the convolutional neural network (CNN) memorizes specific patterns in the training dataset but fails to generalize to unseen validation data. Overfitting is a common challenge in deep learning, particularly in computer vision tasks, where datasets are limited in size or do not adequately represent real-world variability.

A) Data augmentation is an effective solution to overfitting because it artificially increases the diversity and size of the training dataset without collecting new data. By applying transformations such as rotations, horizontal and vertical flips, scaling, cropping, translations, brightness adjustments, and color jittering, the model learns to recognize features invariant to these transformations. For example, an object like a cat or car should be classified correctly regardless of orientation, position, or lighting conditions. This encourages the network to focus on robust, generalizable patterns rather than memorizing the exact pixel arrangements of training images.

Data augmentation is particularly beneficial when datasets are small or imbalanced. It also acts as a form of regularization, helping prevent the model from fitting noise or specific idiosyncrasies in the training data. Techniques like random rotations simulate real-world variations, flips handle symmetrical objects, and color adjustments help the network become invariant to lighting conditions. Combined with standard regularization methods such as dropout or weight decay, augmentation significantly improves validation performance and reduces the generalization gap.

B) Increasing the number of convolutional layers increases the model’s capacity, allowing it to extract more complex features. While this may improve training accuracy, it can exacerbate overfitting if the dataset does not provide sufficient diversity. Simply adding layers without addressing the fundamental issue of limited variability does not improve generalization.

C) Reducing the number of filters decreases model capacity, which may prevent overfitting in some cases. However, it risks underfitting, where the network cannot capture enough meaningful features to classify images accurately. This trade-off may harm overall performance if the network becomes too shallow or narrow.

D) Training for fewer epochs may reduce overfitting by preventing the network from fully memorizing the training data, but this can also lead to undertraining, where essential features are not learned, and the network fails to achieve high accuracy even on the training set.

In  data augmentation is the most effective and practical approach to improve generalization in CNNs. By exposing the network to a wider variety of examples that mimic real-world variability, augmentation helps the model learn robust and invariant features, ensuring better performance on unseen validation and test data. It is widely adopted in image classification, object detection, medical imaging, autonomous driving, and other computer vision applications to enhance both reliability and accuracy.