Google Professional Machine Learning Engineer Exam  Dumps and Practice Test Questions Set 2 Q 21- 40

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Question 21:

You are building a machine learning model to predict loan defaults. During evaluation, you notice that the model performs well overall but poorly for customers with high incomes. Which approach is most suitable to address this issue?

A) Stratify the dataset based on income and retrain the model.
B) Increase the number of hidden layers in the neural network.
C) Remove high-income customers from the dataset.
D) Reduce regularization strength to allow more flexible fitting.

Answer: A) Stratify the dataset based on income and retrain the model.

Explanation:

 The observation indicates that the model is not generalizing well across certain segments of the population—in this case, high-income customers. This is often caused by uneven representation of subgroups in the training data, which can lead to biased predictions.

A) Stratifying the dataset involves dividing the data into subgroups (here, by income) and ensuring that each group is adequately represented during training and validation. By retraining the model on a stratified dataset or using stratified sampling for cross-validation, the model can learn patterns specific to high-income customers while maintaining performance for other groups. This directly addresses the fairness and performance gap across income segments. Stratification ensures that both training and evaluation reflect the distribution of important subgroups, allowing the model to generalize better.

B) Increasing the number of hidden layers in a neural network increases model capacity but does not guarantee improved performance for underrepresented subgroups. Without addressing the underlying data imbalance or subgroup-specific patterns, deeper networks may simply overfit the majority distribution, leaving the high-income segment poorly modeled.

C) Removing high-income customers eliminates the group with poor performance from the dataset. While this could superficially improve overall metrics, it reduces model fairness and applicability. The model would no longer serve the high-income population, which is not acceptable in real-world applications like loan prediction. Removing data is generally a last-resort approach and does not address the underlying issue.

D) Reducing regularization strength allows the model to fit the training data more flexibly. While this may improve accuracy on high-income customers in the training set, it also risks overfitting the majority groups, creating worse generalization. Regularization adjustments alone do not solve subgroup performance disparities.

In conclusion, stratifying the dataset or applying targeted sampling ensures all segments, including high-income customers, are adequately learned. Techniques such as subgroup-specific loss weighting or specialized models for minority groups can further enhance fairness and predictive accuracy. Proper stratification improves the model’s ability to generalize across diverse customer profiles.

Question 22:

You are working on an NLP model for named entity recognition (NER). The dataset contains many rare entities that appear in very few sentences. Which approach is most effective for improving recognition of rare entities?

A) Use transfer learning with a pre-trained language model.
B) Remove rare entities from the dataset to simplify training.
C) Increase dropout to prevent overfitting.
D) Apply standard TF-IDF vectorization for feature extraction.

Answer: A) Use transfer learning with a pre-trained language model.

Explanation:

 Rare entities present a challenge because the model has limited exposure during training, making it difficult to learn robust representations for these entities.

A) Transfer learning with pre-trained language models (e.g., BERT, RoBERTa, or GPT-based encoders) leverages knowledge from large corpora, including contextual understanding of entities. Pre-trained embeddings capture semantic relationships even for rare or unseen words. Fine-tuning such models on the NER dataset allows the model to recognize rare entities by contextual cues rather than relying solely on frequency in the training set. This approach is widely adopted in state-of-the-art NER systems because it mitigates the problem of data sparsity.

B) Removing rare entities simplifies the task but reduces the model’s utility. The goal of NER is to recognize all relevant entities, including rare ones. Dropping them may artificially improve training metrics but diminishes the real-world effectiveness of the system. This approach ignores the root problem rather than solving it.

C) Increasing dropout is a regularization technique to prevent overfitting. While it can improve generalization on frequent entities, it does not solve the problem of underrepresentation of rare entities. Dropout is orthogonal to the challenge of data sparsity and insufficient exposure.

D) TF-IDF vectorization represents words based on frequency across documents. While useful for some NLP tasks, it ignores context, which is crucial for recognizing rare entities. TF-IDF cannot handle sequences effectively or differentiate entity boundaries in sentences, making it insufficient for NER tasks involving rare words.

Fine-tuning a pre-trained language model provides contextualized embeddings that generalize well, even for infrequent entities. This allows rare entities to be recognized accurately by leveraging linguistic context and semantic similarity.

Question 23:

You are training a reinforcement learning agent in a complex environment. The agent occasionally receives very large rewards. You notice that these rare large rewards cause unstable learning. Which approach is most effective to stabilize training?

A) Normalize or clip rewards to a consistent range.
B) Reduce the number of episodes per training batch.
C) Increase the exploration rate indefinitely.
D) Remove the rare rewards from the environment.

Answer: A) Normalize or clip rewards to a consistent range.

Explanation:

 Reinforcement learning algorithms rely on the reward signal to update policies. Large, infrequent rewards can cause large updates, destabilizing learning and resulting in oscillations or divergence.

A) Normalizing rewards scales them to a consistent range, reducing the magnitude of extreme values and stabilizing gradient updates. Clipping rewards limits extreme signals to a predefined threshold, ensuring updates remain bounded. Both methods prevent instability while preserving relative differences between reward signals. This approach is standard in practice for RL environments with sparse or high-magnitude rewards.

B) Reducing the number of episodes per training batch may decrease the frequency of updates but does not resolve the problem of extreme reward magnitudes. Smaller batches may increase variance and make learning even less stable.

C) Increasing the exploration rate encourages the agent to try random actions. While exploration is important, doing so indefinitely does not solve instability caused by extreme rewards. Random actions can compound instability rather than stabilize learning.

D) Removing rare rewards alters the environment and may undermine the intended learning objective. Rare rewards are often critical signals representing important milestones or achievements, and removing them can prevent the agent from learning key behaviors.

Reward normalization and clipping are widely recommended in RL literature to stabilize training, especially in environments with sparse or outlier reward distributions. This approach ensures effective and safe policy updates.

Question 24:

You are designing a pipeline for real-time anomaly detection on streaming IoT data. The data arrives at high velocity and contains noise. Which approach is most suitable?

A) Use online learning algorithms with incremental updates and noise filtering.
B) Aggregate data into large batches and retrain the model periodically.
C) Train a batch model offline and apply it without updates.
D) Discard noisy sensor readings to reduce complexity.

Answer: A) Use online learning algorithms with incremental updates and noise filtering.

Explanation:

 Streaming IoT data presents challenges: high velocity, temporal dependencies, and noisy measurements. The model must adapt continuously without retraining from scratch.

A) Online learning algorithms, such as incremental gradient descent or streaming variants of tree-based models, update the model as new data arrives. Combining online learning with noise filtering (e.g., smoothing, outlier detection) ensures that updates reflect meaningful signals rather than transient sensor noise. This approach allows the model to adapt to evolving patterns in real-time, a key requirement for anomaly detection in streaming data.

B) Aggregating data into large batches and retraining periodically converts the system into an offline model, which introduces latency. In high-velocity environments, delayed updates may cause missed anomalies and reduced detection accuracy. This approach is suitable for batch processing but suboptimal for real-time requirements.

C) Training a batch model offline and applying it without updates ignores the evolving nature of streaming data. IoT data distributions can drift over time due to sensor degradation, environmental changes, or usage patterns. A static model will fail to detect anomalies effectively in non-stationary streams.

D) Discarding noisy sensor readings may reduce computational complexity but risks losing important anomaly signals. Noise filtering is more effective than outright removal, preserving critical information while mitigating spurious fluctuations.

Incremental learning with proper noise handling is the best solution for real-time, high-velocity streaming data, ensuring both adaptability and robustness.

Question 25:

 You are developing a multi-label image classification system. The labels are highly imbalanced, and some images have rare combinations of labels. Which loss function or training strategy is most appropriate?

A) Use binary cross-entropy with class weighting for each label.
B) Apply mean squared error between predicted and true labels.
C) Use categorical cross-entropy as if the problem were multi-class.
D) Only train on images with frequent label combinations.

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

Explanation:

 Multi-label classification differs from multi-class classification because each instance can have multiple active labels. Imbalanced labels require careful treatment.

A) Binary cross-entropy calculates the loss for each label independently and is suitable for multi-label problems. Adding class weights ensures that rare labels contribute more to the loss, encouraging the model to learn them even when they occur infrequently. This approach balances performance across frequent and rare labels, addressing both the multi-label nature and class imbalance.

B) Mean squared error is designed for regression tasks, not multi-label classification. Using MSE treats the problem as continuous rather than probabilistic, which can lead to poor convergence and inaccurate predictions.

C) Categorical cross-entropy assumes exactly one label per instance. Applying it to multi-label data forces the model to choose one label per image, which is incompatible with instances that have multiple labels.

D) Training only on frequent label combinations reduces the dataset size and ignores rare but important combinations. This may improve metrics for common cases but fails to generalize and reduces the system’s utility in real-world applications.

Binary cross-entropy with class weighting allows the model to learn multiple labels simultaneously while addressing imbalance, making it the most suitable strategy for multi-label image classification.

Question 26

 You are building a machine learning system to predict equipment maintenance needs. The dataset contains a mix of sensor readings, categorical metadata, and time-based features. The model frequently predicts maintenance too late. Which approach is most effective to improve early detection?

A) Include lag features and rolling window statistics to capture temporal patterns.
B) Increase the depth of a fully connected neural network.
C) Remove categorical metadata to simplify the model.
D) Train the model only on the most recent data points.

Answer: A) Include lag features and rolling window statistics to capture temporal patterns.

Explanation:

 The challenge here is predicting maintenance early, which requires modeling temporal dependencies and trends in sensor data.

A) Including lag features means using previous time steps as input variables. For example, sensor readings at times t-1, t-2, … t-n can help the model understand trends. Rolling window statistics, such as moving averages, standard deviations, and max/min values over time windows, provide the model with information about recent patterns and anomalies. These features allow the model to detect subtle early warning signs that precede failures, improving the timeliness of maintenance predictions. By explicitly encoding temporal patterns, the model can anticipate problems rather than reacting after a failure occurs.

B) Increasing the depth of a fully connected neural network may increase model capacity, but it does not inherently capture sequential dependencies. Without temporal feature engineering, a deeper network only learns correlations between input variables at a single time step and cannot recognize patterns that unfold over time. This may improve general performance but is unlikely to improve early detection significantly.

C) Removing categorical metadata reduces the complexity of the model but eliminates potentially informative features. Metadata such as machine type, installation date, or maintenance history can be crucial for predicting failures, especially in industrial environments. Removing them may reduce model accuracy and ignore relevant signals that indicate upcoming failures.

D) Training only on the most recent data points may allow the model to focus on current conditions but discards long-term trends and patterns. Equipment failures often result from gradual wear or cumulative stress, so ignoring historical context can lead to missed early warning signs. Temporal patterns from historical data are essential for proactive maintenance prediction.

In conclusion, incorporating lag features and rolling window statistics is the most effective strategy. These features allow the model to recognize temporal trends and detect subtle signs of deterioration, enabling timely maintenance interventions.

Question 27:

You are training a deep learning model for speech recognition. The training data contains varying levels of background noise. The model performs poorly on noisy audio. Which approach is most suitable to improve robustness?

A) Apply data augmentation by adding synthetic noise to training audio.
B) Reduce model complexity to prevent overfitting.
C) Remove all noisy audio from the training set.
D) Increase the learning rate to speed up convergence.

Answer: A) Apply data augmentation by adding synthetic noise to training audio.

Explanation:

 Noise in audio data introduces variability that the model must handle to generalize well in real-world conditions.

A) Data augmentation with synthetic noise exposes the model to diverse noisy scenarios during training. By mixing original audio with background noise at different levels and frequencies, the model learns to extract robust features and becomes invariant to noise. This technique is widely used in speech recognition and audio processing, improving the model’s ability to generalize to unseen noisy environments. It helps the network learn meaningful patterns in the presence of noise rather than memorizing clean audio characteristics.

B) Reducing model complexity might decrease overfitting but does not directly improve robustness to noise. The model still needs exposure to noisy data to learn features invariant to background sounds. Complexity reduction alone does not address the variability introduced by real-world audio conditions.

C) Removing noisy audio reduces training difficulty but decreases model robustness. In practice, audio will rarely be perfectly clean. Excluding noisy samples prevents the model from learning to handle realistic scenarios, resulting in poor performance in deployment.

D) Increasing the learning rate affects optimization speed but does not improve the model’s ability to handle noise. In fact, a higher learning rate may destabilize training, especially in complex speech recognition networks. Noise robustness requires feature-level solutions rather than optimization adjustments.

Augmenting the dataset with synthetic noise allows the model to learn invariances and patterns that are robust to variations in audio, making it the most effective strategy for improving performance on noisy speech.

Question 28:

You are building a computer vision model for autonomous vehicles. The dataset contains images captured in daylight, but the model struggles with nighttime images. Which approach is most suitable to improve generalization across lighting conditions?

A) Use data augmentation to simulate different lighting conditions.
B) Train exclusively on daytime images with higher resolution.
C) Remove images with shadows or glare from the dataset.
D) Increase the number of convolutional layers to capture complex features.

Answer: A) Use data augmentation to simulate different lighting conditions.

Explanation:

 Autonomous vehicle models must generalize across a variety of real-world lighting conditions, including nighttime or low-light scenarios.

A) Data augmentation allows the model to experience varied lighting conditions during training. Techniques include adjusting brightness, contrast, and gamma, as well as simulating shadows, glare, or color shifts. By augmenting the dataset to include artificial nighttime conditions, the model learns features invariant to lighting variations, improving generalization. This is a standard practice in computer vision for real-world deployment where environmental conditions can vary widely.

B) Training exclusively on daytime images focuses the model on specific conditions. While this may improve accuracy on the daytime dataset, the model will fail to generalize to nighttime conditions. Limiting the diversity of input images exacerbates generalization issues and is counterproductive for autonomous vehicle safety.

C) Removing images with shadows or glare reduces dataset complexity but eliminates valuable scenarios that the model will encounter in practice. The model would not learn to handle challenging lighting situations, leading to poor performance under real-world conditions.

D) Increasing the number of convolutional layers increases model capacity but does not solve the problem of insufficient exposure to varied lighting. A deeper network may overfit daytime conditions without learning invariances to nighttime illumination. Exposure to diverse lighting is essential for generalization, independent of network depth.

In data augmentation that simulates diverse lighting conditions is the most effective approach for enabling the model to perform well in both day and night scenarios. It allows learning of robust features while maintaining accuracy across environmental variations.

Question 29:

You are training a transformer-based language model for document summarization. The model struggles with long documents and often omits critical sections. Which approach is most appropriate to improve performance?

A) Use hierarchical encoding to process sections of the document separately before aggregation.
B) Reduce the sequence length to focus on the beginning of the document.
C) Increase the batch size during training.
D) Remove stopwords from the text.

Answer: A) Use hierarchical encoding to process sections of the document separately before aggregation.

Explanation:

 Transformer models have a maximum input length, and long documents may exceed this limit. Processing the entire document at once can result in truncated inputs or inefficient attention over excessively long sequences.

A) Hierarchical encoding splits the document into smaller segments (paragraphs or sections), encodes each segment independently using transformers, and then aggregates the representations to produce a global summary. This approach allows the model to retain information from all sections without being constrained by input length limitations. Hierarchical methods are particularly effective for long documents because they maintain context at both local (section-level) and global (document-level) scales, enabling comprehensive summarization.

B) Reducing sequence length to focus on the beginning of the document is a naive approach. While it may capture the introduction or abstract, critical information from later sections is ignored, resulting in incomplete or biased summaries. The model does not learn to summarize the entire content effectively.

C) Increasing the batch size affects training efficiency and stability but does not address the challenge of long input sequences. Memory constraints may even prevent large batch sizes when sequences are long, and the model will still struggle with omitted sections.

D) Removing stopwords slightly reduces input length but does not solve the problem of long-range dependencies. Stopwords are often necessary for maintaining grammatical and semantic coherence, especially in summarization tasks. Their removal can even degrade model quality.

Hierarchical encoding is therefore the most effective strategy, allowing the model to capture both local and global context, ensuring critical sections of long documents are included in the summary.

Question 30:

 You are building a recommendation system using matrix factorization. The user-item interaction matrix is extremely sparse, and the model frequently fails to make predictions for some users. Which approach is most suitable?

A) Incorporate side information such as user demographics and item features.
B) Reduce the number of latent factors in the matrix factorization.
C) Remove users or items with insufficient interactions.
D) Train the model using only dense regions of the matrix.

Answer: A) Incorporate side information such as user demographics and item features.

Explanation:

 Sparse user-item matrices pose a challenge because many users or items have few interactions, leading to cold-start problems and poor predictive coverage.

A) Incorporating side information introduces additional signals that help generate recommendations for users or items with limited interaction data. For example, user demographics, preferences, and item attributes (e.g., category, price, metadata) can be embedded and integrated into matrix factorization models. Hybrid models that combine collaborative filtering with content-based features allow predictions for users or items with sparse interactions, effectively alleviating cold-start issues. This approach enhances model robustness while preserving the benefits of matrix factorization for collaborative patterns.

B) Reducing the number of latent factors simplifies the model but does not solve sparsity problems. Fewer latent factors may underfit the interaction patterns, reducing accuracy for both dense and sparse regions of the matrix.

C) Removing users or items with insufficient interactions reduces sparsity but discards potentially valuable data. It may improve training metrics but limits the system’s ability to serve new users or rare items, which is undesirable in recommendation systems.

D) Training on dense regions of the matrix ignores sparse areas entirely. While this may improve performance for well-represented users or items, it exacerbates the cold-start problem and reduces coverage across the user-item space.

Incorporating side information is the most effective strategy for dealing with sparsity in recommendation systems. It allows leveraging auxiliary data to improve predictions for users or items with few interactions while maintaining the advantages of collaborative filtering.

Question 31:

You are building a machine learning model for predicting customer churn in a subscription service. You notice that customers who recently joined are consistently misclassified. Which approach is most effective to improve predictions for these new users?

A) Add features representing the customer tenure or account age.
B) Increase the depth of the neural network.
C) Remove new customers from the dataset to reduce noise.
D) Apply heavier L2 regularization to prevent overfitting.

Answer: A) Add features representing the customer tenure or account age.

Explanation:

 The challenge here is a typical cold-start problem: the model lacks sufficient information about new users to make accurate predictions.

A) Including features like customer tenure, account age, or the number of interactions provides the model with explicit context regarding how long a user has been active. These features allow the model to differentiate between long-term behavior patterns and the initial behavior of new customers. Often, newly joined users exhibit behaviors that are statistically distinct from established users (e.g., high initial engagement, trial periods). By encoding tenure explicitly, the model can learn patterns specific to new users, improving accuracy and reducing misclassification. Feature engineering that captures temporal or behavioral context is widely recommended for addressing cold-start issues in churn prediction.

B) Increasing the depth of the neural network increases model capacity but does not address the lack of information about new users. Without features explicitly capturing new-user behavior, a deeper network may overfit to patterns of long-term users and still misclassify recent customers. Depth alone cannot solve a data representation problem.

C) Removing new customers reduces the misclassification rate on the training set but ignores an important segment. In practice, accurately predicting churn for new users is critical for retention strategies. Excluding this segment leads to biased models and reduces the model’s real-world utility.

D) Applying heavier L2 regularization constrains model weights to reduce overfitting. While it can improve generalization, it does not solve the issue of insufficient information for new users. Regularization is orthogonal to the need for features that represent user tenure and early behavior.

In adding features that explicitly capture tenure, account age, and initial engagement is the most effective approach. It directly addresses the cold-start challenge, allowing the model to generalize to new users while preserving predictive accuracy for long-term users.

Question 32:

You are developing a convolutional neural network (CNN) for classifying medical images. After training, the model performs very 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 rotation, flipping, and scaling.
B) Increase the number of convolutional layers to improve feature extraction.
C) Remove low-importance layers to reduce model complexity.
D) Reduce the learning rate to improve convergence.

Answer: A) Apply data augmentation techniques such as rotation, flipping, and scaling.

Explanation:

 The scenario describes overfitting: the model memorizes training examples but fails to generalize to unseen validation data.

A) Data augmentation generates modified versions of existing images (e.g., rotations, flips, scaling, color jitter, and noise injection) to simulate variations the model may encounter in real-world scenarios. Augmentation effectively enlarges the training dataset, allowing the model to learn invariant features rather than memorizing exact pixel patterns. For medical imaging, where datasets are often limited, augmentation is critical for improving generalization. By exposing the network to diverse versions of the same underlying structures, the model becomes robust to variations in orientation, scale, and imaging conditions, which reduces overfitting and improves validation performance.

B) Increasing the number of convolutional layers increases model complexity, which may exacerbate overfitting rather than improve generalization. While deeper layers can extract higher-level features, without sufficient data or regularization, they can memorize training examples, worsening validation performance.

C) Removing layers reduces model capacity and can mitigate overfitting if the network is excessively deep. However, if the model is already appropriately sized, removing layers risks underfitting and losing the ability to capture complex features necessary for medical image classification. Reducing capacity is a blunt tool and less effective than data augmentation for improving generalization.

D) Reducing the learning rate affects optimization dynamics and can improve convergence stability, but it does not directly address overfitting. The core issue is not the optimizer but the model’s exposure to a limited range of data variations.

Data augmentation is therefore the most effective strategy for improving generalization in CNNs trained on limited medical image datasets. It directly mitigates overfitting and enhances the network’s ability to recognize features in varied real-world scenarios.

Question 33:

 You are training a reinforcement learning (RL) agent for a strategy game. The environment has sparse rewards, and the agent rarely receives positive feedback. Which approach is most suitable to accelerate learning?

A) Implement reward shaping to provide intermediate feedback.
B) Reduce the discount factor to focus on immediate rewards.
C) Increase the size of the replay buffer.
D) Remove random exploration to focus on known strategies.

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

Explanation:

 Sparse rewards in RL create slow learning because the agent receives very little feedback to guide policy updates.

A) Reward shaping introduces intermediate rewards that provide incremental feedback for partial progress toward the goal. For example, in a strategy game, the agent can receive small rewards for capturing resources, achieving sub-goals, or completing intermediate objectives. These signals accelerate learning by providing more frequent gradient information for policy updates. Properly designed reward shaping preserves the original goal’s optimal policy while improving learning efficiency. It is a widely used technique in RL, particularly in sparse-reward environments, and allows the agent to learn complex behaviors more effectively.

B) Reducing the discount factor emphasizes immediate rewards but does not generate additional feedback. Sparse-reward environments often require recognition of long-term consequences, so lowering the discount factor may hinder learning optimal strategies.

C) Increasing the replay buffer size stores more past experiences, which can improve sample efficiency, but it does not solve the problem of sparse feedback. If positive rewards are extremely rare, adding more historical experience may still provide insufficient signals for learning.

D) Removing random exploration limits the agent to its current policy, reducing its ability to discover states that yield positive rewards. Exploration is critical in sparse-reward settings to identify rare but important feedback. Reducing exploration would further slow learning.

Reward shaping directly addresses the sparse reward challenge by providing frequent, informative feedback, enabling the agent to learn effectively in complex environments where raw rewards are too infrequent to guide policy learning.

Question 34:

 You are developing a time series forecasting model for predicting stock prices. You notice that the model performs poorly on sudden market shifts and extreme events. Which approach is most suitable to improve robustness?

A) Incorporate exogenous variables and event-based features into the model.
B) Reduce model complexity to avoid overfitting.
C) Apply standard scaling to the input features.
D) Train the model only on the most stable periods.

Answer A) Incorporate exogenous variables and event-based features into the model.

Explanation:

 Financial time series are often influenced by external factors such as economic indicators, news events, and policy announcements. Models trained solely on past price data may fail to anticipate abrupt changes.

A) Including exogenous variables (macro indicators, sector performance, interest rates) and event-based features (earnings reports, geopolitical events, natural disasters) provides the model with additional context to anticipate market shifts. For example, incorporating a binary feature for major announcements allows the model to learn patterns associated with such events. This approach improves robustness, allowing the model to generalize better to extreme situations rather than relying solely on historical trends. Many state-of-the-art financial forecasting models leverage exogenous variables for this purpose.

B) Reducing model complexity may reduce overfitting but does not address the inability to anticipate external shocks. Simpler models cannot predict extreme events unless they are provided relevant signals.

C) Standard scaling ensures numerical stability but does not improve robustness to abrupt market changes. Scaling is beneficial for optimization but does not add information about exogenous influences.

D) Training only on stable periods avoids extreme events but reduces the model’s generalization ability. It biases the model toward stable market behavior, making it worse at forecasting shocks. Avoiding volatility in training data reduces predictive accuracy when unusual events occur.

Incorporating exogenous variables and event-based features provides the necessary context to handle sudden market shifts and extreme events, directly addressing the root cause of poor performance in volatile scenarios.

Question 35:

You are building a multi-class classification model for predicting disease categories from genomic data. The dataset contains many correlated features and high-dimensional inputs. Which approach is most appropriate to improve model performance?

A) Apply dimensionality reduction techniques such as PCA or feature selection.
B) Remove all correlated features arbitrarily.
C) Train a very deep neural network without preprocessing.
D) Increase the learning rate to accelerate convergence.

Answer: A) Apply dimensionality reduction techniques such as PCA or feature selection.

Explanation:

 High-dimensional genomic data often contain thousands of correlated features. Models trained on such data may overfit, learn redundant information, or converge slowly.

A) Dimensionality reduction techniques like PCA reduce the number of features while preserving the majority of variance in the dataset. PCA creates uncorrelated principal components that capture the most informative patterns, which improves model stability and generalization. Alternatively, feature selection methods identify the most relevant features for prediction, reducing noise and redundancy. Both approaches address multicollinearity, reduce overfitting, and improve predictive performance in high-dimensional genomic datasets. This is a standard practice in bioinformatics and medical ML applications.

B) Removing correlated features arbitrarily may discard valuable information and reduce predictive power. Correlation alone does not indicate irrelevance, and naive removal can harm model performance.

C) Training a very deep neural network without preprocessing is risky in high-dimensional settings. The network may overfit, suffer from vanishing gradients, or become computationally intractable. Without dimensionality reduction or feature selection, the model struggles to learn meaningful patterns efficiently.

D) Increasing the learning rate affects convergence speed but does not solve the problem of high-dimensional correlated inputs. A higher learning rate may destabilize training, especially with complex genomic data.

Dimensionality reduction or feature selection is therefore the most effective strategy for high-dimensional, correlated genomic data. It improves model efficiency, generalization, and predictive accuracy.

Question 36:

 You are building a recommendation system for an e-commerce platform. You notice that new users rarely receive relevant recommendations. Which approach is most suitable to handle this cold-start problem?

A) Incorporate content-based filtering using user profiles and item metadata.
B) Remove new users from the training dataset.
C) Increase the number of latent factors in matrix factorization.
D) Train the model only on users with extensive histories.

Answer: A) Incorporate content-based filtering using user profiles and item metadata.

Explanation:

 The cold-start problem arises when the system has insufficient interaction data for new users. Without interaction history, collaborative filtering methods struggle to make accurate recommendations.

A) Content-based filtering leverages features of users and items to make predictions. For new users, the system can use their profile information (e.g., demographics, interests, or preferences) and match it to item metadata (e.g., category, brand, or price range). This allows the system to generate recommendations even in the absence of historical interactions. Content-based methods effectively mitigate cold-start issues, providing a principled way to make relevant suggestions while collaborative filtering continues to improve as user-item interactions accumulate.

B) Removing new users ignores the segment that needs recommendations the most. While it may temporarily improve training metrics, it is impractical in real-world systems where onboarding new users is critical for retention. Excluding them from training fails to address the cold-start challenge.

C) Increasing the number of latent factors in matrix factorization may improve expressivity for users with sufficient history but does not help new users. Latent factors rely on observed interactions to learn embeddings. For users without interactions, these latent factors are undefined, and the model cannot make predictions.

D) Training only on users with extensive histories focuses on well-represented data but completely ignores the cold-start population. While this may improve model performance metrics, it prevents serving new users effectively, reducing the overall utility of the recommendation system.

Incorporating content-based filtering ensures that new users can receive meaningful recommendations immediately, while collaborative filtering remains effective for established users. This hybrid approach is widely used in production recommendation systems to address the cold-start problem without compromising overall performance.

Question 37:

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

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

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

Explanation:

 Imbalanced datasets, especially in medical imaging, present a challenge because standard loss functions may bias the model toward the majority class, leading to poor recall for rare but clinically critical diseases.

A) Class-weighted loss assigns higher importance to the minority class (rare disease) by scaling the loss contribution for each sample according to its class frequency. Focal loss goes a step further by dynamically down-weighting easy-to-classify majority samples, focusing the model’s learning on hard examples, which often belong to the minority class. These approaches ensure that the model pays attention to rare disease cases, improving recall and balanced performance metrics such as F1 score. In practice, weighted or focal loss is standard for medical applications with highly imbalanced datasets.

B) Removing negative samples artificially balances the dataset but discards potentially useful information about healthy cases. This can lead to a biased model and reduce generalization to real-world scenarios where negatives are common.

C) Standard cross-entropy loss treats all classes equally, so the model will focus on predicting negatives correctly due to their overwhelming frequency. This leads to very low recall for the minority class, which is unacceptable in medical diagnostics where identifying rare diseases is crucial.

D) Reducing model capacity may help prevent overfitting but does not address class imbalance. The model may still fail to detect rare diseases because it is not trained to prioritize them. Lower capacity alone cannot compensate for insufficient exposure to minority samples.

Using class-weighted or focal loss is the most effective approach for handling imbalanced medical datasets, ensuring the model learns to detect rare diseases without sacrificing performance on majority classes.

Question 38:

 You are building a time series model for predicting energy consumption. The residuals show heteroscedasticity: larger errors occur during peak hours. Which approach is most appropriate to address this issue?

A) Apply variance-stabilizing transformations such as log or Box-Cox on the target variable.
B) Remove peak-hour data from the training set.
C) Increase the number of layers in a deep neural network.
D) Normalize input features to zero mean and unit variance.

Answer: A) Apply variance-stabilizing transformations such as log or Box-Cox on the target variable.

Explanation:

 Heteroscedasticity indicates that the variance of errors is not constant across the range of predictions, often occurring in domains like energy consumption where peak hours have higher variability.

A) Variance-stabilizing transformations like log or Box-Cox reduce the effect of large deviations by compressing the scale of high-variance regions. Applying these transformations to the target variable allows the model to learn more uniformly across different ranges, reducing the impact of large residuals during peak hours. After training, predictions can be inverse-transformed to the original scale. This approach is widely used in time series modeling and regression problems with non-constant variance.

B) Removing peak-hour data avoids the problem but introduces bias. The model will fail to predict accurately during high-demand periods, which are critical in energy planning. This is not a practical solution.

C) Increasing the number of layers in a neural network may improve model capacity but does not directly address heteroscedasticity. The network may overfit or fail to properly model variance patterns without explicit transformations.

D) Normalizing input features improves optimization but does not stabilize output variance. Heteroscedasticity is a property of the target variable and must be addressed directly through transformations or specialized loss functions.

Applying variance-stabilizing transformations allows the model to handle periods of high variability effectively, improving accuracy and making predictions more reliable across different time intervals.

Question 39:

You are training a multi-class text classification model with thousands of classes. You notice slow convergence and high memory usage. Which approach is most effective to improve training efficiency?

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

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

Explanation:

 Multi-class problems with thousands of classes can be computationally intensive because computing the full softmax requires normalization over all classes for each prediction.

A) Hierarchical softmax organizes classes into a tree structure, reducing computation from O(n) to O(log n) per training example, where n is the number of classes. Sampled softmax approximates the full softmax by only considering a subset of classes in each update. Both methods significantly reduce memory and computational requirements while preserving model accuracy. These approaches are standard in NLP and large multi-class classification scenarios, especially when the number of classes is extremely large.

B) Removing rare classes may reduce output dimensions but discards potentially important categories, biasing predictions and harming real-world applicability. This is not a desirable solution for practical multi-class tasks.

C) Training with very small batch sizes reduces memory per batch but increases gradient noise, potentially slowing convergence. This approach does not address the core computational cost of computing the full softmax for thousands of classes.

D) L1 regularization sparsifies weights but does not reduce softmax computation complexity. Sparsity helps in model compression and generalization but does not directly solve the issue of slow convergence or high memory usage for large-class outputs.

Using hierarchical or sampled softmax is the most effective method to improve efficiency in large-scale multi-class classification tasks, enabling faster training and lower memory consumption without sacrificing performance.

Question 40:

You are developing a machine learning model for fraud detection in transactions. The dataset contains categorical features with high cardinality and missing values. Which approach is most appropriate to preprocess these features?

A) Use target encoding with careful cross-validation to prevent leakage.
B) Drop categorical features with many levels.
C) Apply standard one-hot encoding without handling missing values.
D) Replace missing values with zero and treat categories as integers.

Answer: A) Use target encoding with careful cross-validation to prevent leakage.

Explanation:

High-cardinality categorical features pose challenges for standard encoding methods. One-hot encoding can lead to very large sparse matrices, increasing memory usage and computational cost. Missing values further complicate preprocessing.

A) Target encoding replaces each category with a statistic derived from the target variable (e.g., mean fraud probability). When implemented correctly with cross-validation or out-of-fold encoding, target encoding prevents data leakage and allows the model to use high-cardinality categorical features effectively. This method reduces dimensionality, handles missing values gracefully (by assigning global or group statistics), and preserves predictive information that may be lost with naive encoding. Target encoding is widely used in tabular ML for categorical features with many levels.

B) Dropping features reduces dimensionality but discards potentially predictive information. High-cardinality categories often carry critical signals for fraud detection (e.g., merchant IDs or product codes), and removing them can decrease model accuracy.

C) Applying standard one-hot encoding without handling missing values can create additional sparse categories (e.g., a separate column for missing), which may be inefficient and lead to poor learning if missingness is informative.

D) Replacing missing values with zero and treating categories as integers introduces artificial ordinal relationships. The model may infer false patterns, reducing predictive performance and potentially causing bias.

Target encoding with careful cross-validation is therefore the most appropriate strategy for high-cardinality categorical features with missing values in fraud detection tasks. It preserves predictive power, reduces dimensionality, and avoids data leakage.