AI0-001 Practice Test 14 — 15 Questions

Question 1

A company is deploying a chatbot using a large language model. They want to mitigate the risk of prompt injection attacks. Which TWO measures should be implemented?

Accepted Answer

Implement input validation and sanitisation. Input validation and sanitisation (A) prevent malicious user inputs from being interpreted as instructions by the LLM, directly mitigating prompt injection by stripping or escaping special characters and control sequences. A strict system prompt (B) defines the chatbot's role and boundaries, reducing the attack surface by making it harder for injected prompts to override the intended behavior.

Answer

Fine-tune the model on safe conversational examples

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Use a larger context window

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Limit the maximum output token length

Question 2

A team is training a deep neural network on a large image dataset. They observe that the training loss decreases smoothly but validation loss oscillates. Which regularization technique should be applied?

Accepted Answer

Dropout. Dropout is the correct regularization technique because it randomly drops neurons during training, which prevents co-adaptation of features and reduces overfitting. This addresses the validation loss oscillation (a sign of overfitting) while allowing the training loss to decrease smoothly, as dropout only applies during training and not during validation.

Answer

Data augmentation

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L1 regularization

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Batch normalization

Question 3

A financial institution is implementing an AI-based fraud detection system. The compliance officer is concerned about potential bias in the model that could lead to unfair treatment of certain customer groups. Which governance practice should be prioritized to address this concern?

Accepted Answer

Schedule regular bias audits using fairness metrics.. Regular bias audits using fairness metrics (Option B) are the correct governance practice because they provide a systematic, quantitative method to detect and measure disparate impact across protected groups. Unlike simply collecting more data, audits directly evaluate model outputs for statistical parity, equal opportunity, or other fairness definitions, enabling the institution to identify and remediate bias proactively. This aligns with regulatory expectations for ongoing monitoring and accountability in AI governance.

Answer

Increase the diversity of the training data by collecting more samples from underrepresented groups.

Answer

Retrain the model every month with the latest transaction data.

Answer

Use SHAP values to provide explanations for each prediction.

Question 4

A retail company uses a time-series model to forecast daily sales for inventory management. The model is a seasonal ARIMA trained on three years of daily data. It performed well during initial validation but after deployment, forecasts became inaccurate during holiday seasons, often underestimating demand by up to 40%. The data science team examined the features and found that the training data did not include any holiday indicators. They also discovered that the model's residuals show strong autocorrelation during holiday weeks. The company needs to improve the forecast for the upcoming holiday season. They have access to historical sales data with holiday dates and are considering several approaches. Which approach will BEST address the issue?

Accepted Answer

Engineer holiday-related features (e.g., holiday flag, days before/after) and retrain the ARIMA model with these features as exogenous variables.. The core issue is that the ARIMA model lacks holiday-related information, causing systematic underestimation during holiday periods. By engineering holiday features (e.g., binary flags, days-before/after indicators) and including them as exogenous variables in a SARIMAX model, the model can directly learn the demand spikes associated with holidays. This directly addresses the residual autocorrelation during holiday weeks and improves forecast accuracy without changing the underlying time-series framework.

Answer

Decrease the learning rate and increase the number of training epochs.

Answer

Create an ensemble of the ARIMA model and a seasonal naive model to average forecasts.

Answer

Replace the ARIMA model with a deep learning LSTM model trained on the same data.

Question 5

A team is training a convolutional neural network (CNN) for medical image diagnosis. They have a limited dataset of 500 labeled images. Which strategy is most effective to improve model generalization?

Accepted Answer

Data augmentation. With only 500 labeled medical images, the primary challenge is overfitting due to limited data. Data augmentation (e.g., random rotations, flips, zooms) artificially expands the training set by creating varied but realistic transformations, which forces the CNN to learn invariant features and significantly improves generalization to unseen data.

Answer

Increasing network depth

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Using a larger batch size

Answer

Reducing the number of filters

Question 6

An organization uses a third-party pre-trained model for a medical diagnosis system. Which supply chain security measure is MOST critical to verify before deployment?

Accepted Answer

Scanning the model for backdoors. Third-party pre-trained models can contain hidden backdoors inserted during training, such as poisoned weights that trigger misclassification on specific inputs. In a medical diagnosis system, such backdoors could cause life-threatening errors, making verification against adversarial manipulation the most critical supply chain security measure before deployment.

Answer

Reviewing the model's software bill of materials (SBOM)

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Ensuring the model is hosted on a private cloud

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Checking for proper API key management

Question 7

A machine learning team is training a large transformer model on a text corpus. They need to reduce training time while maintaining model accuracy. Which hardware configuration would be MOST effective for this task?

Accepted Answer

Use a cluster of GPUs with data parallelism. GPUs are optimized for the parallel computations required in deep learning training, offering significant speedups over CPUs. TPUs are also effective but less accessible and more specialized. The question specifies 'most effective' for training a transformer model, which aligns with GPU acceleration.

Answer

Use a high-core-count CPU with large RAM

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Use a single GPU with model parallelism

Answer

Use a single TPU with model parallelism

Question 8

In unsupervised learning, which task involves grouping similar data points together based on feature similarities?

Accepted Answer

Clustering. Clustering partitions data into groups where intra-cluster similarity is high. Classification is supervised; anomaly detection finds outliers; regression predicts continuous values.

Answer

Anomaly detection

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Classification

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Regression

Question 9

A team is training a recurrent neural network (RNN) with LSTM units to predict stock prices. The validation loss is significantly higher than the training loss. Which action is MOST likely to reduce the gap?

Accepted Answer

Increase the dropout rate in LSTM layers. A large gap between training and validation loss indicates overfitting. Increasing dropout (a regularisation technique) reduces overfitting by preventing co-adaptation of neurons. Increasing LSTM units or epochs would worsen overfitting, and reducing sequence length may lose important temporal patterns.

Answer

Increase the number of LSTM units

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Increase the number of training epochs

Answer

Reduce the sequence length

Question 10

A team is developing a sentiment analysis model and obtains the following performance on the test set: accuracy=0.92, precision=0.75, recall=0.80, F1=0.77. The baseline majority-class classifier achieves 0.85 accuracy. Which conclusion is MOST justified?

Accepted Answer

The model likely suffers from class imbalance, as the gap between accuracy and precision suggests. Accuracy is high but precision and recall are notably lower, indicating class imbalance where the model biases toward the majority class, inflating accuracy.

Answer

The model should use a different evaluation metric like BLEU

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The model is excellent because accuracy is high

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The model has high variance and is overfitting

Question 11

A data scientist is selecting a model for a binary classification task where interpretability is critical because of regulatory requirements. The dataset has 20 features and 10,000 samples. Which model is MOST appropriate?

Accepted Answer

Decision tree. Decision trees are inherently interpretable, showing the decision rules. Random forests and gradient boosting are ensembles that sacrifice interpretability for accuracy. Neural networks are black-box models.

Answer

Neural network (MLP)

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Gradient boosting machine

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Random forest classifier

Question 12

A data analyst wants to use a model that provides feature importance scores to understand which factors most influence customer churn. They also need the model to handle both numerical and categorical data with minimal preprocessing. Which algorithm is BEST suited?

Accepted Answer

Random forest. Random forests provide feature importance, handle mixed data types, and require little preprocessing.

Answer

Support vector machine (SVM) with RBF kernel

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Logistic regression

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k-nearest neighbours (k-NN)

Question 13

An organization is building a recommendation system that requires low-latency vector similarity search. They need to store and query millions of embeddings. Which THREE technologies are appropriate for this task?

Accepted Answer

Weaviate. Pinecone, Weaviate, and pgvector are vector databases designed for similarity search. Snowflake and S3 are not optimized for vector search.

Answer

Snowflake

Answer

Amazon S3

Question 14

A data scientist is building a model to predict whether a credit card transaction is fraudulent, using labeled historical data. Which machine learning paradigm is being used?

Accepted Answer

Supervised learning. Supervised learning uses labeled data to train a model to map inputs to outputs. Fraud detection with historical labels is a classic binary classification problem.

Answer

Reinforcement learning

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Unsupervised learning

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Self-supervised learning

Question 15

A data scientist is training a large language model on a custom dataset using PyTorch on AWS. The training is taking too long due to GPU memory constraints. The team wants to use multiple GPUs across instances with minimal code changes. Which AWS service should they use?

Accepted Answer

Amazon SageMaker with distributed training libraries. SageMaker distributed training libraries support data parallelism and model parallelism with minimal code changes, enabling multi-GPU training across instances efficiently.

Answer

AWS Elastic Fabric Adapter (EFA)

Answer

AWS Batch with GPU instances

Answer

AWS ParallelCluster with Slurm