AI0-001 Practice Test 5 — 25 Questions

Question 1

A data scientist notices that a model's performance on the training set is excellent, but validation accuracy is poor. The team used the same dataset for feature engineering and model selection. What is the MOST likely cause?

Accepted Answer

Train/test leakage caused by using the same data for feature engineering and model selection. Reusing the same data for feature engineering and model selection creates data leakage from validation set into training decisions, leading to overfitting and poor generalization.

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The dataset is too small for the model complexity

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The learning rate is too high

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The model is overfitting due to high variance

Question 2

A data science team is developing a churn prediction model. Which TWO data preparation best practices are MOST important to prevent overfitting and ensure generalization?

Accepted Answer

Split data into training and test sets before any preprocessing. Splitting data before any preprocessing prevents leakage from test set into training. Cross-validation provides a more robust estimate of generalization than a single hold-out set.

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Normalize all features using the entire dataset's statistics

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Remove outliers based on the full dataset distribution

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Encode categorical variables with target encoding on the full dataset

Question 3

A machine learning engineer is training a convolutional neural network (CNN) for object detection in satellite imagery. The training loss is not decreasing significantly. Which TWO adjustments could help the model converge? (Select TWO)

Accepted Answer

Normalize the pixel values to zero mean and unit variance. Normalizing pixel values to zero mean and unit variance (A) ensures that input features have similar scales, which prevents certain weights from updating disproportionately and stabilizes gradient descent. This is especially important for CNNs processing satellite imagery, where raw pixel intensities can vary widely across bands and scenes, leading to poor convergence.

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Remove dropout layers to allow more gradient flow

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Use a smaller batch size to reduce memory

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Increase the learning rate by 10x

Question 4

An AI engineer trains a deep learning model for image classification. After training, the training accuracy is 99% but validation accuracy is 85%. Which technique would best address this discrepancy?

Accepted Answer

Add dropout layers. The high training accuracy (99%) and lower validation accuracy (85%) indicate overfitting, where the model memorizes training data but fails to generalize. Dropout layers randomly deactivate neurons during training, forcing the network to learn more robust features and reducing overfitting. This technique directly addresses the discrepancy by improving validation performance without sacrificing training capacity.

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Increase data augmentation

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Decrease the learning rate

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Increase the number of layers

Question 5

An AI model for detecting fraudulent transactions has high precision but low recall. Which business impact is most likely?

Accepted Answer

Many fraudulent transactions go undetected. High precision means that when the model flags a transaction as fraudulent, it is very likely correct. However, low recall indicates that the model misses a significant proportion of actual fraudulent transactions. Therefore, the most likely business impact is that many fraudulent transactions go undetected, leading to financial losses.

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The model has no impact on fraud detection

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The model detects all fraudulent transactions

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Many legitimate transactions are flagged as fraud

Question 6

An organization wants to classify support tickets into categories (billing, technical, etc.). Which type of machine learning is most suitable?

Accepted Answer

Supervised learning. Supervised learning is the correct choice because the organization has labeled historical support tickets (e.g., 'billing' or 'technical') and wants to train a model to map new tickets to these predefined categories. This is a classic classification task, where the algorithm learns from input-output pairs to predict the correct label for unseen data.

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Unsupervised learning

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Reinforcement learning

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Regression

Question 7

A deep learning model for image classification is overfitting the training data. The team has already tried data augmentation and dropout. Which additional technique should they implement to reduce overfitting?

Accepted Answer

Early stopping. Early stopping (Option D) is the correct additional technique because it halts training when validation performance stops improving, directly preventing the model from memorizing noise in the training data. Since data augmentation and dropout are already in use, early stopping provides a complementary regularization effect by limiting the number of training iterations before overfitting occurs.

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Batch normalization

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Increase number of epochs

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Gradient clipping

Question 8

A data engineer is designing a pipeline to train a linear regression model on a dataset with 10 million rows and 50 features. The dataset fits in memory. Which approach should the engineer use to train the model efficiently?

Accepted Answer

Stochastic gradient descent. Stochastic gradient descent (SGD) is the most efficient approach for training a linear regression model on a dataset with 10 million rows and 50 features because it updates the model parameters using only one training example per iteration, leading to much faster convergence per epoch compared to batch methods. Since the dataset fits in memory, SGD can still be implemented efficiently without the overhead of loading data in batches from disk, and it scales well to large datasets where the normal equation or batch gradient descent would be computationally prohibitive.

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Normal equation

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Batch gradient descent

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Principal component analysis

Question 9

Which THREE are common activation functions used in neural networks? (Choose THREE.)

Accepted Answer

ReLU. ReLU (Rectified Linear Unit) is a common activation function in neural networks because it introduces non-linearity while being computationally efficient. It outputs the input directly if positive, otherwise zero, which helps mitigate the vanishing gradient problem compared to sigmoid or tanh. This makes it a default choice for hidden layers in many deep learning architectures.

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Softmax

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Linear

Question 10

Refer to the exhibit. A data scientist is training a binary classifier. Based on the training log, which problem is the model experiencing?

Accepted Answer

Overfitting. The training log shows that the model's training accuracy continues to improve while the validation accuracy plateaus or degrades after a certain number of epochs. This divergence between training and validation performance is the hallmark of overfitting, where the model memorizes the training data noise rather than learning generalizable patterns.

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Underfitting

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Data leakage

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Vanishing gradient

Question 11

A team is training a deep learning model for image classification. The training loss decreases rapidly but validation loss starts increasing after a few epochs. Which regularization technique should be applied to mitigate this issue?

Accepted Answer

Early stopping. Option C is correct because early stopping halts training when validation loss increases, preventing overfitting. Option A is wrong because L2 regularization penalizes large weights but doesn't stop training. Option B is wrong because dropout randomly drops neurons during training, but early stopping directly addresses the symptom. Option D is wrong because data augmentation increases data diversity, but the issue is overfitting due to training too long.

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Data augmentation

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L2 regularization

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Dropout

Question 12

An organization needs to store sensitive customer data for training a machine learning model. The data must be encrypted at rest and in transit, and access must be audited. Which combination of practices should be implemented?

Accepted Answer

Use TLS for transfer, AES-256 for storage, and AWS CloudTrail for auditing. Option A is correct because it combines TLS (Transport Layer Security) for encrypting data in transit, AES-256 for strong encryption at rest, and AWS CloudTrail for auditing API-level access. TLS ensures confidentiality and integrity during transmission, AES-256 provides robust symmetric encryption for stored data, and CloudTrail logs all AWS API calls for compliance and audit trails. This triad satisfies the requirements of encryption in transit, at rest, and audited access.

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Use FTP for transfer, AES-128 for storage, and manual log review

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Use SSH for transfer, store data in a database, and enable access logs

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Use MD5 for hashing, store data in plaintext, and enable server logs

Question 13

A data scientist is training a deep learning model for image classification. The training loss decreases steadily but the validation loss starts increasing after 10 epochs. Which technique should the scientist apply to address this issue?

Accepted Answer

Implement early stopping. The scenario describes overfitting: the model memorizes training data (loss decreases) but fails to generalize to unseen validation data (validation loss increases). Early stopping (Option C) halts training when validation performance degrades, preventing overfitting while preserving the best model weights. This is a standard regularization technique in deep learning frameworks like TensorFlow and PyTorch.

Answer

Add more dropout layers

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Reduce the learning rate

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Increase the number of training epochs

Question 14

A data engineer is reviewing an S3 bucket policy for a machine learning project. The policy is intended to allow access to training data only from the corporate network (10.0.0.0/16). However, users in the corporate network report access denied. Which issue is most likely causing the problem?

Accepted Answer

The policy does not include s3:ListBucket action, which is needed to list objects.. Option C is correct. The condition key uses "aws:SourceIp" but the correct key for IP address condition is "aws:SourceIp". However, the exhibit shows "aws:SourceIp" which is correct? Wait, the exhibit shows "aws:SourceIp" — that is correct. But the problem might be that the policy uses "GetObject" but the users might be trying to list objects (s3:ListBucket). Option C says the action does not include s3:ListBucket, which is often needed first. Option A is wrong because the resource is correct. Option B is wrong because the policy allows access from 10.0.0.0/16. Option D is wrong because the effect is Allow.

Answer

The policy is missing a Deny statement.

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The resource ARN is incorrect.

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The IP address condition is not matching because the corporate network uses a different CIDR.

Question 15

A data engineer is designing a pipeline for a streaming data application that uses a machine learning model to detect anomalies in real time. Which TWO practices should the engineer implement to ensure data quality and model reliability?

Accepted Answer

Use a sliding window for feature computation. Option C is correct because streaming anomaly detection requires real-time feature computation over recent data, and a sliding window ensures that only the most relevant data points are used for model inference, maintaining low latency and adapting to concept drift. This approach avoids the staleness of batch processing and aligns with the continuous nature of streaming pipelines.

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Use batch processing to transform data in fixed intervals

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Store all raw data indefinitely for future analysis

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Retrain the model on a fixed schedule every 24 hours

Question 16

A company deploys a computer vision model for quality inspection on a manufacturing line. After deployment, the model's accuracy drops from 95% to 80% over two weeks. Which action is most likely to address this issue?

Accepted Answer

Retrain the model using recently collected production data.. Option B is correct because data drift is a common cause of performance degradation over time, and retraining with recent data realigns the model. Option A is wrong because increasing threshold may reduce false positives but does not address drift. Option C is wrong because adjusting learning rate is irrelevant for inference. Option D is wrong because adding redundant models increases complexity without solving drift.

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Increase the confidence threshold for predictions.

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Decrease the learning rate of the training algorithm.

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Deploy an additional ensemble of models for redundancy.

Question 17

A data scientist fine-tunes a large language model for a legal document summarization task. After fine-tuning, the model performs well on test data but produces summaries that include hallucinated legal clauses. Which mitigation strategy is most effective?

Accepted Answer

Implement retrieval-augmented generation (RAG) to provide factual context.. Option A is correct because RAG grounds the model in retrieved documents, reducing hallucinations. Option B is wrong as temperature affects creativity but does not eliminate hallucinations. Option C is wrong because it reduces output length but may not prevent false content. Option D is wrong because it does not address the model's tendency to fabricate.

Answer

Use a different tokenizer during fine-tuning.

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Decrease the temperature parameter to 0.1 during inference.

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Set a maximum token limit of 50 for each summary.

Question 18

A healthcare organization deploys an AI system to analyze medical images and detect anomalies. During a routine audit, the security team discovers that the AI model occasionally returns results that include data from patients who have opted out of data sharing. Which security control should be implemented to prevent this violation?

Accepted Answer

Apply data anonymization techniques to the training dataset.. Option B is correct because data anonymization ensures that patient identities are removed from training data, preventing re-identification of opt-out patients. Option A is incorrect because access control does not address data already in the model. Option C is incorrect because encryption protects data in transit/rest but does not prevent data leakage from model outputs. Option D is incorrect because differential privacy adds noise to queries but does not directly remove specific patient data from model results.

Answer

Implement role-based access control (RBAC) on the AI model's inference API.

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Use differential privacy during model training.

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Encrypt the training data at rest and in transit.

Question 19

A company uses a machine learning model to recommend products to customers. The marketing team notices that the model is recommending high-profit items more frequently than low-profit items, even when customers are likely to prefer the latter. This behavior is causing customer dissatisfaction. Which approach would best align the model with customer preferences while maintaining profitability?

Accepted Answer

Use a multi-objective optimization framework to balance profit and customer satisfaction.. Option D is correct because multi-objective optimization allows the model to balance multiple goals (e.g., profit and customer satisfaction) explicitly. Option A is incorrect because it still prioritizes profit, which may not address satisfaction. Option B is incorrect because it completely removes profit, which may harm business goals. Option C is incorrect because it only adjusts profit weights without true multi-objective framework.

Answer

Train the model with a loss function that weights profit more heavily than customer satisfaction.

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Adjust the model's hyperparameters to reduce the influence of profit features.

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Remove profit data from the training set and only use customer preference data.

Question 20

A company is developing an AI chatbot for customer service. The legal team is concerned that the chatbot might generate responses that violate privacy regulations. Which governance mechanism should be implemented to mitigate this risk?

Accepted Answer

Implement a human-in-the-loop review process for high-risk responses.. Option C is correct because a human-in-the-loop (HITL) review process directly addresses the risk of privacy violations by ensuring that high-risk responses are reviewed by a human before being sent to the customer. This governance mechanism provides a safety net for unpredictable outputs from the generative AI model, which may inadvertently leak personally identifiable information (PII) or violate data protection regulations like GDPR or CCPA. Unlike technical controls that only reduce the attack surface, HITL offers real-time compliance oversight for the chatbot's natural language generation (NLG) outputs.

Answer

Use explainable AI techniques to understand why the chatbot generates certain responses.

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Encrypt all chatbot conversations at rest and in transit.

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Anonymize the training data used to train the chatbot.