Google Professional Machine Learning Engineer Exam Dumps and Practice Test Questions Set 1 Q1-15
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Question 1
Which approach is most appropriate for handling a dataset with a large number of missing values in a supervised learning problem?
A) Remove all rows with missing values
B) Replace missing values with the mean or the median
C) Use a model that handles missing values natively
D) Fill missing values with zeros
Answer: C
Explanation:
A) Removing all rows with missing values can drastically reduce the size of the dataset. If a dataset already has a limited number of examples, removing rows may discard valuable information and reduce the model’s ability to generalize. This approach may work if the percentage of missing values is very small, but in datasets with substantial missing information, this strategy can introduce bias or result in underfitting.
B) Replacing missing values with the mean or median is a common imputation technique. Mean, or median imputation, is simple and effective for numerical variables but can distort the distribution of the data and reduce variance. For categorical variables, using the mode can be applied. However, this approach does not leverage potential patterns in the data and may introduce bias, especially if missingness is not completely random.
C) Using a model that handles missing values natively is often the most robust approach. Some algorithms, like decision trees or gradient boosting frameworks, can deal with missing data directly during training. These models use internal heuristics to split on missing values, allowing them to make full use of the available data without introducing biases through imputation or losing examples. This approach maintains the dataset’s integrity while providing robust predictions.
D) Filling missing values with zeros is generally not recommended unless zero is a meaningful representation for the missing attribute. Arbitrary zero imputation can distort the data distribution and lead to incorrect relationships being learned by the model. This approach is most suitable when zeros naturally represent absence, but in many real-world datasets, it could create significant bias and mislead the model.
Overall, choosing a model capable of handling missing data natively is most appropriate because it minimizes data loss, avoids naive imputation, and effectively leverages patterns in both present and missing data. It balances performance, complexity, and robustness while ensuring predictive reliability in real-world scenarios where missingness is common.
Question 2
You are training a deep learning model on a highly imbalanced dataset. Which metric is most suitable for evaluating model performance?
A) Accuracy
B) Precision
C) Recall
D) F1 Score
Answer: D
Explanation:
A) Accuracy measures the proportion of correct predictions over the total number of predictions. While accuracy is a common metric, it can be misleading for imbalanced datasets. For example, if 95% of the data belongs to one class, a naive model that predicts only the majority class would achieve 95% accuracy but fail to identify any minority class instances. Thus, accuracy alone does not reflect true performance in imbalanced scenarios.
B) Precision measures the proportion of true positives out of all predicted positives. Precision is important when false positives are costly, but it does not account for false negatives. High precision can be achieved at the expense of recall, potentially ignoring many actual positive cases, which is often undesirable in imbalanced datasets where identifying minority instances is crucial.
C) Recall measures the proportion of true positives identified out of all actual positives. Recall is essential when missing a positive instance is costly. However, focusing solely on recall can lead to many false positives, reducing the model’s usefulness if precision is also important.
D)The F1 score is a widely used metric in machine learning and statistics that serves as a balanced measure of a model’s accuracy by taking into account both precision and recall. Precision is the measure of how many of the instances predicted as positive are actually positive, reflecting the model’s ability to avoid false positives, while recall, also known as sensitivity, measures how many of the actual positive instances the model correctly identifies, reflecting the ability to minimize false negatives. The F1 score combines these two metrics into a single value by calculating their harmonic mean, which emphasizes the lower of the two values, thereby ensuring that a model is not considered effective if it performs well on only one of these aspects. This characteristic makes the F1 score particularly valuable in situations where the dataset is imbalanced, meaning that one class, often the positive or minority class, is significantly underrepresented compared to the other. In such scenarios, accuracy alone can be misleading because a model could achieve high accuracy simply by predicting the majority class most of the time, while failing to correctly identify instances of the minority class. By incorporating both precision and recall, the F1 score mitigates this limitation and provides a more nuanced evaluation of performance, particularly for models tasked with detecting rare but critical events such as fraud detection, medical diagnosis, or spam detection. A high F1 score indicates that the model maintains a good balance between identifying positive instances correctly and avoiding incorrect positive predictions, ensuring that neither false positives nor false negatives dominate the assessment. Moreover, the F1 score allows practitioners to compare models more effectively when the cost of different types of errors varies and when the primary goal is to ensure that the minority class is accurately captured without generating an excessive number of false alarms. In practical applications, optimizing for the F1 score encourages model adjustments that improve both recall and precision simultaneously, often requiring careful tuning of thresholds, selection of features, and choice of algorithms that can handle imbalanced data. Overall, the F1 score is a critical tool for evaluating machine learning models in contexts where both the completeness and correctness of positive predictions matter, offering a comprehensive view of model effectiveness that goes beyond traditional accuracy metrics. It provides a single, interpretable number that captures the trade-off between false positives and false negatives, helping stakeholders make informed decisions about model deployment and improvement strategies while highlighting areas where further refinement may be necessary to achieve robust and reliable performance across all classes.
The F1 score is widely recommended in imbalanced dataset scenarios because it mitigates the limitations of accuracy, precision, and recall individually. It ensures a more holistic assessment of model performance, particularly when the minority class is the primary focus.
Question 3
Which technique helps prevent overfitting in a neural network?
A) Increasing the number of layers
B) Adding dropout layers
C) Using a smaller batch size
D) Reducing training epochs
Answer: B
Explanation:
A) Increasing the number of layers deepens the network, allowing it to capture more complex patterns. While this can improve model capacity, it also increases the risk of overfitting. More parameters mean the model may memorize the training data rather than generalize to unseen data, making this approach counterproductive if the dataset is not sufficiently large.
B) Adding dropout layers is a regularization technique that randomly deactivates a fraction of neurons during training. This prevents the network from becoming overly reliant on specific neurons, promoting robustness and better generalization. Dropout effectively reduces overfitting while maintaining model capacity, making it a widely used technique in deep learning architectures.
C) Using a smaller batch size affects the training dynamics and can introduce noise in gradient updates, potentially acting as implicit regularization. However, smaller batch sizes primarily influence training stability rather than directly preventing overfitting. The impact on generalization is less predictable and may require additional strategies to be effective.
D)Reducing training epochs is a strategy often considered in training neural networks to prevent overfitting, which occurs when a model learns the training data too well, including its noise and idiosyncrasies, and as a result performs poorly on new, unseen data. The number of epochs defines how many times the learning algorithm iterates over the entire training dataset, allowing the model to adjust its internal parameters and learn patterns within the data. While limiting the number of epochs can theoretically reduce the chance of overfitting by stopping the model before it begins to memorize the training data, doing so arbitrarily without careful monitoring can lead to underfitting. Underfitting happens when the model has not been trained enough to capture the underlying relationships in the data, resulting in poor performance both on the training set and on validation or test sets. The challenge, therefore, lies in finding a balance between providing sufficient training for the model to learn meaningful patterns and avoiding excessive training that can lead to overfitting. This is where early stopping becomes a valuable technique. Early stopping involves monitoring the model’s performance on a validation set during training and halting the process when the validation performance ceases to improve or begins to deteriorate. By using early stopping, the training duration is dynamically determined based on actual model performance rather than a fixed number of epochs, ensuring that the model trains long enough to capture relevant patterns while minimizing the risk of overfitting. Proper implementation of early stopping requires careful selection of a validation metric and potentially a patience parameter, which allows the model to continue training for a few more epochs in case there are minor fluctuations in validation performance. This method effectively balances learning and generalization, helping the model achieve optimal performance on unseen data. Arbitrarily reducing epochs without monitoring validation loss or accuracy, on the other hand, ignores the model’s learning trajectory and may stop training prematurely, leaving the network incapable of understanding complex patterns or relationships in the dataset. Furthermore, the ideal number of epochs is influenced by factors such as dataset size, model complexity, learning rate, and regularization techniques, meaning that a fixed, arbitrary limit is rarely appropriate across different scenarios. In practice, combining early stopping with other strategies, such as learning rate adjustments, dropout, and weight regularization, can further enhance generalization and prevent both underfitting and overfitting. While reducing epochs can theoretically limit overfitting, it is not a substitute for proper monitoring of validation performance. Early stopping provides a principled approach to determine the optimal training duration, ensuring that the model learns effectively from the data without compromising its ability to generalize to new examples. Therefore, careful observation of validation metrics and thoughtful application of early stopping are essential for achieving a well-balanced and robust neural network that performs reliably across a variety of inputs.
Dropout is particularly effective because it directly targets the model’s tendency to overfit by forcing redundancy and independence among neurons. It allows the network to maintain flexibility while avoiding memorization of the training data.
Question 4
In supervised learning, which scenario is best suited for using a regression model?
A) Predicting whether an email is spam
B) Predicting the temperature tomorrow
C) Predicting customer churn (yes/no)
D) Classifying handwritten digits
Answer: B
Explanation:
A) Predicting whether an email is spam is a classification problem. The target variable is categorical (spam or not spam), making regression inappropriate. Regression is not designed to produce discrete labels but rather continuous values.
B) Predicting the temperature tomorrow is a regression problem. Temperature is a continuous variable, and the goal is to estimate its numerical value based on historical data and features such as weather conditions. Regression models are explicitly designed to predict continuous outputs, making them ideal for this task.
C) Predicting customer churn (yes/no) is a classification problem. Churn is a categorical outcome, so a model should output probabilities or discrete labels indicating whether a customer is likely to churn. Using regression would produce continuous values that require thresholding, which is less natural and may degrade interpretability.
D) Classifying handwritten digits involves discrete categories (0–9). This is also a classification task where the model needs to assign each input image to one of several classes. Regression models would not directly provide class labels, making them unsuitable for this scenario.
Regression is appropriate when the target variable is continuous and numeric, as it allows the model to learn patterns in the input features and estimate a precise value rather than a discrete category. Temperature prediction perfectly fits this requirement.
Question 5
Which method can help improve the interpretability of a complex machine learning model?
A) Using a deep neural network without constraints
B) Using SHAP or LIME explanations
C) Increasing the number of hidden layers
D) Reducing dataset size
Answer: B
Explanation:
A) Using a deep neural network without constraints often increases complexity. While it can improve predictive performance, interpretability suffers because the internal workings of deep networks are difficult to explain. Simply relying on a more complex model does not provide insights into feature contributions or decision logic.
B) Using SHAP or LIME explanations provides post-hoc interpretability. SHAP assigns contribution scores to each feature for a given prediction, while LIME approximates the model locally with an interpretable surrogate. Both methods allow practitioners to understand the model’s reasoning without changing its structure, making complex models more transparent and trustworthy.
C) Increasing the number of hidden layers enhances model capacity but makes interpretation harder. Deep networks with many layers produce non-linear transformations that are difficult to visualize or intuitively understand. Increasing depth alone does not aid interpretability; it typically exacerbates the opacity of the model.
D) Reducing dataset size may simplify training, but does not inherently improve model interpretability. In fact, smaller datasets can lead to underfitting or unstable model explanations. The relationship between dataset size and interpretability is indirect and often negative if the model loses the ability to generalize.
SHAP and LIME directly address interpretability by providing understandable explanations for individual predictions. They help bridge the gap between model complexity and human comprehension, making them essential tools for model transparency.
Question 6
You are building a machine learning model to detect fraudulent transactions. The dataset is highly imbalanced, with only 1% of transactions labeled as fraud. Which approach is most effective for training the model?
A) Train the model on the original dataset without adjustments
B) Use oversampling of the minority class
C) Remove non-fraud transactions to balance the dataset
D) Ignore the imbalance and rely on accuracy
Answer: B
Explanation:
Training the model on the original dataset without adjustments means feeding the model the highly imbalanced data as it exists. This approach can be problematic because the model will be biased toward predicting the majority class, which is non-fraud in this case. The model could achieve high accuracy simply by predicting all transactions as non-fraud, but it would fail to identify actual fraud cases. The minority class, which is the most critical for detection, may be underrepresented in the model’s learned patterns, leading to poor recall and ultimately rendering the model ineffective for the intended task.
Using oversampling of the minority class addresses the imbalance by replicating or generating additional examples of fraud transactions. Techniques such as SMOTE (Synthetic Minority Over-sampling Technique) can create synthetic samples based on existing data, which helps the model learn patterns for the minority class without losing information from the majority class. Oversampling allows the model to focus on fraud cases, improving recall and F1 score while still maintaining enough majority-class examples to capture general transaction patterns. This approach balances the need for representation with the requirement to avoid bias in the learning process.
Removing non-fraud transactions to balance the dataset can produce a dataset where the classes are equal in size, but this comes at a high cost. The majority class is critical for learning legitimate transaction behavior, and removing large portions of it can reduce the model’s ability to generalize. The resulting model may overfit to the small dataset and perform poorly on unseen data. This approach may be tempting for simplicity, but it sacrifices valuable information, and in production, it can lead to false positives and unreliable predictions.
Ignoring the imbalance and relying on accuracy is almost never recommended for highly skewed datasets. Accuracy does not reflect the model’s ability to detect the minority class, which in fraud detection is the most important measure. A model could achieve 99% accuracy without detecting any fraud, rendering it practically useless. Metrics such as precision, recall, and F1 score are far more appropriate because they focus on the performance of the model with respect to the minority class, which is the primary concern in imbalanced scenarios.
Oversampling is effective because it allows the model to better understand patterns in the minority class without discarding important information from the majority class. It improves the model’s ability to detect fraud while maintaining overall predictive capability. Combined with proper evaluation metrics like F1 score, it ensures that the model is both accurate and capable of identifying critical events in highly imbalanced datasets.
Question 7
You are training a convolutional neural network to classify medical images. The dataset is small, and overfitting is a concern. Which strategy is most effective for improving generalization?
A) Increase the number of layers in the network
B) Use data augmentation techniques
C) Train for more epochs without regularization
D) Remove dropout layers
Answer: B
Explanation:
Increasing the number of layers in the network increases the model’s capacity to learn complex patterns. However, with a small dataset, adding more layers typically worsens overfitting because the model can memorize the training examples rather than learning generalizable features. Deeper networks require more data to effectively train without overfitting, so increasing depth is counterproductive in this scenario. It could lead to poor performance on new medical images that were not part of the training set.
Using data augmentation techniques is a widely recommended approach for small image datasets. Data augmentation generates additional training examples by applying transformations such as rotation, flipping, scaling, cropping, and color adjustments. This creates variation in the dataset, which forces the model to learn invariant features rather than memorizing individual examples. Augmentation effectively enlarges the dataset without collecting new data and helps the model generalize better to unseen images. In medical imaging, augmentation can simulate real-world variations, making predictions more robust and reducing the risk of overfitting.
Training for more epochs without regularization exacerbates overfitting. Extended training allows the network to continue memorizing the training set, particularly if the dataset is small. While early stopping or regularization techniques can prevent overfitting, simply increasing epochs without constraints will likely reduce validation performance. The model may achieve near-perfect training accuracy but fail dramatically when evaluated on new images.
Removing dropout layers is counterproductive because dropout is a regularization method that reduces overfitting by randomly deactivating neurons during training. Dropout forces the network to develop redundant pathways and prevents it from relying too heavily on specific features in the training set. Eliminating dropout reduces model robustness and increases memorization of the small dataset, which worsens generalization.
Data augmentation is the most effective strategy because it directly addresses the lack of data and introduces variability that mimics real-world conditions. When combined with other regularization techniques like dropout or weight decay, augmentation significantly enhances the model’s ability to generalize, which is critical in sensitive applications such as medical image classification. By exposing the model to diverse variations, it can learn meaningful patterns rather than memorizing limited examples.
Question 8
You are designing a recommendation system for an e-commerce platform. Users’ purchase history is sparse, and new products are frequently added. Which approach is most suitable?
A) Collaborative filtering based only on user-item interactions
B) Content-based filtering using product features
C) Use a simple popularity-based recommendation
D) Randomly suggest products to users
Answer: B
Explanation:
Collaborative filtering based only on user-item interactions relies on historical purchase data to identify similar users or items. This method works well when the interaction matrix is dense and users have many historical interactions. However, in sparse datasets, collaborative filtering struggles because there is insufficient data to find meaningful similarities. Additionally, new products that have not been purchased by anyone cannot be recommended, leading to the cold-start problem. Sparse user history and frequent product addition reduce the effectiveness of this approach in e-commerce environments.
Content-based filtering using product features leverages metadata, such as product category, description, price, and tags, to generate recommendations. By focusing on item attributes rather than user interaction history alone, this approach can recommend new products to users based on their preferences. For example, if a user has purchased running shoes, the system can suggest similar athletic shoes even if those specific items have never been purchased before. Content-based filtering is robust to sparsity and solves the cold-start problem for both new users and new products, making it highly suitable for dynamic e-commerce platforms.
Using a simple popularity-based recommendation system suggests products based solely on overall popularity. While this method is easy to implement, it lacks personalization and may fail to engage users. Popular items may already be well-known, and recommending them to everyone does not account for individual tastes. In the context of sparse data and frequent new product introduction, popularity-based recommendations do not effectively capture user-specific preferences or provide meaningful guidance.
Randomly suggesting products is generally ineffective as a recommendation strategy. It does not consider user preferences or product attributes and provides little value in improving engagement or sales. While random suggestions might occasionally introduce users to new products, they are inefficient and unlikely to produce consistent satisfaction.
Content-based filtering is the most appropriate approach in this scenario because it leverages product information to overcome the challenges of sparse user history and frequent product addition. By analyzing features rather than relying solely on historical interactions, the system can provide personalized recommendations even in cold-start situations, ensuring better user engagement and relevance in a rapidly changing catalog.
Question 9
You are training a natural language processing model to classify customer support tickets. The dataset contains many rare words and domain-specific terminology. Which preprocessing step is most important to improve model performance?
A) Lowercasing all text only
B) Removing all rare words from the vocabulary
C) Using subword tokenization like BPE or WordPiece
D) Removing punctuation and stopwords only
Answer: C
Explanation:
Lowercasing all text is a common preprocessing step that reduces vocabulary size by treating «Payment» and «payment» as the same word. While this simplifies the model’s input, it does not fully address rare words or domain-specific terms, which may appear infrequently and hinder the model from learning meaningful representations. Lowercasing alone does not solve the problem of unseen or rare vocabulary items and may even remove distinctions important for proper nouns or acronyms in a domain-specific corpus.
Removing all rare words from the vocabulary might reduce noise and computational load, but it can be detrimental in this scenario. Rare words often contain crucial information, especially in specialized domains like finance or healthcare. Removing them would discard valuable semantic information and make it harder for the model to recognize domain-specific terminology. This approach risks reducing model accuracy and its ability to generalize to new inputs containing infrequent but important words.
Using subword tokenization like Byte-Pair Encoding (BPE) or WordPiece addresses both rare and domain-specific words effectively. Subword tokenization splits words into smaller meaningful units, allowing the model to represent unseen or rare words by combining subword embeddings. For example, a rare technical term could be broken into common morphemes that the model has seen in other words. This reduces out-of-vocabulary issues, increases vocabulary coverage, and allows the model to generalize better. Subword tokenization is particularly effective in NLP models such as transformers, where fixed-size vocabularies are used and robust handling of rare words is critical for performance.
Removing punctuation and stopwords is useful for reducing noise, but it is not sufficient for handling rare or domain-specific words. Stopwords like «the» or «is» provide little semantic content, and punctuation often does not carry meaning for classification. While these steps can slightly simplify the input, they do not address the challenge of rare words, which is the primary concern in this scenario.
Subword tokenization is the most important preprocessing step because it directly mitigates the impact of rare and domain-specific words without discarding information. It enables the model to understand complex vocabulary and improves performance on classification tasks by increasing representation coverage and semantic understanding.
Question 10
You are building a reinforcement learning agent to optimize warehouse robot movement. The robot receives a reward for minimizing travel time while avoiding collisions. What technique is most suitable to stabilize learning in this environment?
A) Use a small learning rate only
B) Apply experience replay
C) Avoid discounting future rewards
D) Reduce the state space by ignoring obstacles
Answer: B
Explanation:
Using a small learning rate can reduce the risk of unstable updates, but it does not fully stabilize reinforcement learning. While it prevents abrupt changes in the Q-values or policy, it can slow down convergence significantly, making the agent take longer to learn an effective strategy. Additionally, it does not address the issue of correlated experiences, which is common in reinforcement learning when consecutive states are highly similar.
Applying experience replay is a widely adopted method to stabilize reinforcement learning. Experience replay stores past experiences in a buffer and samples random mini-batches for training. This breaks the correlation between consecutive states, allowing the model to learn from a more diverse set of experiences at each update. It also enables multiple learning updates from the same experience, improving data efficiency. In environments like warehouse robot navigation, where consecutive states are often similar, experience replay helps reduce variance, stabilizes learning, and allows the agent to generalize better across different situations, ultimately producing more reliable policies.
Avoiding discounting future rewards is generally inappropriate because discounting is central to reinforcement learning. The discount factor allows the agent to balance immediate versus long-term rewards. Ignoring future rewards can lead the agent to develop myopic strategies, focusing only on short-term gains and failing to optimize overall performance. Discounting is essential for convergence and aligning the agent’s learning objectives with long-term goals.
Reducing the state space by ignoring obstacles is not a viable approach because obstacles are critical to navigation and safety. Removing them would simplify the environment artificially but produce a policy that is unsafe and unrealistic. Ignoring important information prevents the agent from learning the correct behavior and can lead to collisions in deployment.
Experience replay is the most suitable technique because it addresses the stability challenges inherent in reinforcement learning, including correlated experiences and inefficient learning from rare events. By sampling from a diverse set of past experiences, it reduces variance, improves convergence, and ensures that the agent learns a robust policy capable of navigating complex environments safely and efficiently.
Question 11
You are designing a machine learning pipeline for real-time sentiment analysis of social media posts. The incoming data is high-volume and continuously streaming. Which architecture is most appropriate?
A) Batch processing using periodic Hadoop jobs
B) Real-time streaming with Apache Kafka and a model served via REST API
C) Offline analysis using CSV exports
D) Manual labeling and human review only
Answer: B
Explanation:
Batch processing using periodic Hadoop jobs is suitable for large-scale data analysis that does not require immediate feedback. In batch processing, data is accumulated and processed in large chunks at scheduled intervals. While this can work for reporting or historical analysis, it is not compatible with real-time sentiment analysis, where timely predictions are critical. Waiting for periodic batch jobs introduces latency, making the system unable to respond to social media trends as they occur.
Real-time streaming with Apache Kafka and a model served via REST API is well-suited for high-volume, continuously streaming data. Apache Kafka provides a robust message queue for handling streams of social media posts, allowing the system to process data in real-time. A machine learning model served via REST API can immediately consume each message, perform sentiment analysis, and output predictions without significant delay. This architecture ensures low-latency responses, scalability, and the ability to handle high-throughput streams while integrating seamlessly into other applications or dashboards.
Offline analysis using CSV exports is inefficient for continuous streaming data. Exporting and analyzing CSV files introduces delays, requires additional storage, and limits the ability to respond dynamically to trends. While it is acceptable for historical data analysis or model evaluation, it cannot provide the immediate insights required for real-time applications.
Manual labeling and human review only are not feasible for high-volume, real-time streams. Human analysis is too slow to keep up with social media data, and relying solely on manual methods prevents the system from scaling effectively. While human-in-the-loop approaches can complement machine learning for quality control, they cannot replace automated processing in a streaming environment.
Real-time streaming with Kafka and model deployment via REST API is the most appropriate architecture because it supports continuous ingestion, low-latency prediction, and scalable processing. This approach allows the sentiment analysis system to operate efficiently in production, providing timely insights while handling large volumes of data without bottlenecks or delays.
Question 12
You are building a machine learning model for predicting customer lifetime value (CLV). The dataset includes both categorical and numerical features, and some features are highly skewed. Which preprocessing strategy is most appropriate?
A) Apply one-hot encoding for categorical features and log-transform skewed numerical features
B) Standardize all features without handling skewness
C) Remove skewed features from the dataset
D) Replace categorical features with arbitrary numeric codes
Answer: A
Explanation:
Applying one-hot encoding for categorical features and log-transforming skewed numerical features is a standard and effective preprocessing strategy for this type of dataset. One-hot encoding converts categorical variables into binary vectors, allowing machine learning models to interpret categories without imposing any ordinal relationship. This is important for models like linear regression, tree-based models, or gradient boosting, where numeric codes could create artificial relationships between categories that do not exist. Log-transforming skewed numerical features helps reduce the impact of extreme values, bringing the distribution closer to normal. This improves the model’s ability to learn meaningful patterns, stabilizes training, and reduces sensitivity to outliers, which is particularly important for predicting metrics like CLV that can span several orders of magnitude.
Standardizing all features without handling skewness standardizes numerical features to a zero mean and unit variance. While standardization is useful for algorithms that rely on distances or gradient descent optimization, it does not address the issue of highly skewed features. Skewed distributions can still distort model learning because extreme values remain far from the mean, potentially leading to biased predictions. Standardization alone may not improve model performance if the underlying data distribution is highly non-normal.
Removing skewed features from the dataset is generally not recommended because skewed features often contain important predictive information. Eliminating them to simplify preprocessing can degrade model performance and lead to the loss of valuable insights. Skewed distributions can often be corrected using transformations rather than discarded, preserving their contribution to the model while improving stability.
Replacing categorical features with arbitrary numeric codes can mislead the model into assuming a numerical or ordinal relationship where none exists. For example, assigning values 1, 2, and 3 to three product categories may suggest that category 3 is “larger” or “better” than category 1, which is not meaningful. This can introduce bias and reduce model interpretability, especially in linear or distance-based models.
Applying one-hot encoding combined with log transformation addresses both categorical and skewed numerical features effectively. One-hot encoding ensures that categorical variables are represented without introducing artificial ordinal relationships, while log-transforming numerical features stabilizes variance, reduces outlier impact, and makes model training more efficient. This combined strategy preserves information, reduces bias, and allows the model to produce more accurate and reliable predictions for CLV.
Question 13
You are optimizing a recommendation system using reinforcement learning. The system must balance recommending popular items and exploring new products. Which technique is most appropriate?
A) Use a purely greedy policy
B) Use an epsilon-greedy strategy
C) Always recommend new products randomly
D) Recommend only based on historical popularity
Answer: B
Explanation:
Using a purely greedy policy means always recommending the item with the highest predicted reward. While this maximizes immediate expected reward, it does not allow exploration of new items. In recommendation systems, new products or niche items may have high potential but lack historical data. A purely greedy approach can lead to suboptimal long-term performance because the system fails to discover these opportunities. Over time, this can create a feedback loop where only popular items are recommended, limiting diversity and reducing user satisfaction.
Using an epsilon-greedy strategy balances exploration and exploitation by choosing the best-known item most of the time but occasionally selecting a random item with a small probability (epsilon). This allows the system to explore new or under-represented products, gather feedback, and update predictions, while still leveraging historical performance for reliable recommendations. The epsilon-greedy approach is widely used in reinforcement learning because it prevents the system from getting stuck in local optima and ensures that both user preferences and novel items are considered. Over time, this strategy improves long-term reward and system adaptability.
Always recommending new products randomly is purely exploratory. While it ensures that new items receive exposure, it sacrifices immediate user satisfaction because recommendations may not match user interests. Random selection is inefficient for learning user preferences and can decrease engagement if users receive irrelevant suggestions. Random exploration alone is unsuitable for reinforcement learning objectives that require balancing immediate reward with long-term learning.
Recommending only based on historical popularity ignores exploration entirely and favors exploitation of known items. While it maximizes short-term engagement with popular products, it suffers from the cold-start problem and cannot adapt to trends or new items. This approach reduces the system’s ability to discover products that may have higher long-term value and can lead to stagnation in recommendation diversity.
An epsilon-greedy strategy is most appropriate because it combines the benefits of exploitation and exploration. It ensures that the system can reliably recommend popular items while still testing new products, updating reward estimates, and learning user preferences dynamically. This approach improves long-term performance, maintains diversity in recommendations, and addresses the challenges of balancing short-term engagement with long-term system optimization.
Question 14
You are deploying a deep learning model on mobile devices with limited memory and compute resources. Which technique is most effective for reducing model size without significant loss in accuracy?
A) Increase the number of layers to improve accuracy
B) Apply model quantization
C) Remove data augmentation during training
D) Use full-precision floating-point weights only
Answer: B
Explanation:
Increasing the number of layers improves model capacity and may increase accuracy on large datasets. However, for mobile deployment, deeper networks increase memory footprint, computational demand, and inference latency. Larger models consume more power, reduce battery life, and may exceed device constraints, making this approach unsuitable for resource-limited environments.
Applying model quantization is a highly effective method for reducing model size and computational cost. Quantization converts weights and activations from high-precision floating-point (e.g., 32-bit) to lower-precision representations such as 8-bit integers. This reduces memory requirements, improves inference speed, and allows deployment on devices with limited hardware capabilities. Post-training quantization or quantization-aware training often maintains accuracy within a small margin while drastically reducing the model footprint. Quantization is widely used in mobile and embedded AI applications because it provides a practical trade-off between size, efficiency, and performance.
Removing data augmentation during training reduces training complexity but does not affect the deployed model size. While it may slightly improve training speed, it sacrifices model generalization and does not contribute to compressing the model for mobile deployment. Augmentation primarily enhances robustness rather than influencing memory or computational requirements.
Using full-precision floating-point weights only maximizes numerical precision but increases memory and computation demands. Full-precision weights require more storage and processing power, making them less suitable for deployment on constrained devices. Lower-precision representations like those used in quantization offer a better balance of accuracy and efficiency without unnecessary resource overhead.
Model quantization is the most effective approach for deploying deep learning models on resource-constrained devices. It reduces memory and compute requirements, accelerates inference, and enables real-time performance on mobile platforms while maintaining accuracy close to the original model. This makes quantization essential for efficient mobile deployment of AI applications.
Question 15
You are building a predictive maintenance system for industrial machines. Sensor readings are continuous, and equipment failures are rare. Which machine learning approach is most suitable?
A) Standard regression predicting exact failure time
B) Binary classification predicting failure within a time window
C) Clustering sensor readings without labels
D) Recommending maintenance based on average usage
Answer: B
Explanation:
Standard regression predicting exact failure time assumes sufficient data for all failure intervals and requires the model to estimate the precise timing of breakdowns. In reality, failures are rare events, and exact prediction is difficult because of noise, missing data, and sensor variability. Regression may perform poorly due to sparse failure instances and the high uncertainty in continuous failure time prediction. This makes standard regression less suitable for predictive maintenance in industrial settings.
Binary classification predicting failure within a time window frames the problem as predicting whether the machine will fail in a defined period, such as the next week or month. This approach is more realistic for rare event prediction because it allows the model to focus on identifying high-risk conditions rather than predicting exact timing. It works well with imbalanced datasets using techniques like oversampling, weighting, or anomaly detection. This method enables proactive maintenance scheduling, reducing downtime and minimizing operational costs.
Clustering sensor readings without labels is unsupervised and may reveal patterns or anomalies, but it does not directly predict failures. While clustering can help identify unusual operating conditions, it does not provide actionable predictions and cannot quantify the risk of failure. Clustering is useful for exploratory analysis or anomaly detection, but not for explicit predictive maintenance.
Recommending maintenance based on average usage ignores current machine conditions and sensor data. It assumes a uniform degradation rate, which is rarely true in industrial environments. This approach is simplistic, does not account for varying operating conditions, and may result in unnecessary maintenance or unexpected failures. It lacks predictive capability and is suboptimal for improving operational efficiency.
Binary classification within a time window is the most suitable approach because it balances realism, feasibility, and predictive utility. It allows the model to focus on high-risk scenarios, handles rare failures effectively, and supports actionable maintenance decisions. By predicting failure probabilities rather than exact times, the system can optimize interventions, improve safety, and reduce costs in industrial settings.