AI0-001 Practice Test 35 — 15 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

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 2

A developer is building an AI-powered code completion tool. To ensure the model does not output malicious code when prompted with 'Write code to delete all files on the system', which defense is most effective?

Accepted Answer

Output filtering to detect and block dangerous code constructs. Output filtering can block generated code that contains dangerous patterns like file deletion commands.

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Input validation to block the word 'delete'

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Rate limiting on the number of requests per user

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Retraining the model on safe code only

Question 3

A company uses Azure OpenAI to generate customer support responses. The team notices that repeated queries with similar context incur high costs due to token usage. They want to reduce costs without affecting response quality. Which strategy is MOST effective?

Accepted Answer

Implement prompt caching. Prompt caching stores and reuses tokens from previous queries, reducing token consumption for similar requests and lowering costs without quality loss.

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Use a larger model to improve efficiency

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Increase the frequency penalty

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Reduce the max_tokens parameter

Question 4

A media company uses a natural language processing (NLP) model to classify news articles into topics. The model was trained on articles from 2015-2018. In 2023, the model's F1 score drops significantly. The data scientists find that the word embeddings no longer capture the meaning of some terms (e.g., 'covid', 'metaverse'). The model uses static word embeddings (Word2Vec) trained on the original corpus. Which solution BEST addresses the observed degradation? A. Replace static embeddings with contextual embeddings from a transformer model like BERT, then fine-tune the classifier. B. Retrain the static Word2Vec embeddings on a larger corpus from 2023. C. Apply data augmentation to the original training data by replacing words with synonyms. D. Increase the dimensionality of the static embeddings.

Accepted Answer

Replace static embeddings with contextual embeddings from a transformer model like BERT, then fine-tune the classifier.. Contextual embeddings (e.g., BERT) capture meaning based on context, adapting to new uses of words like 'covid' meaning pandemic. Fine-tuning the classifier on new data would update the model. Option A (retraining static embeddings) might capture new word senses but still assigns a single vector per word, missing context. Option B (increasing dimensionality) does not address the semantic shift. Option D (data augmentation) does not introduce new word meanings.

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Retrain the static Word2Vec embeddings on a larger corpus from 2023.

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Increase the dimensionality of the static embeddings.

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Apply data augmentation to the original training data by replacing words with synonyms.

Question 5

A data scientist is using a linear regression model to predict house prices and observes that the model performs well on training data but poorly on test data. Which regularisation technique is MOST appropriate to reduce overfitting?

Accepted Answer

L2 regularisation (Ridge). L2 regularisation (Ridge) adds a penalty term equal to the sum of the squared coefficients to the loss function, which shrinks coefficient magnitudes without forcing them to zero. This reduces variance and overfitting by making the model less sensitive to individual features, which is ideal when the model performs well on training data but poorly on test data due to high variance.

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L1 regularisation (Lasso)

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Dropout

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Data augmentation

Question 6

An AI model is deployed to a mobile app with limited computational resources. The model is a deep neural network with high latency. Which technique is best to reduce inference time?

Accepted Answer

Quantization. Quantization reduces the precision of the model's weights and activations (e.g., from 32-bit floating point to 8-bit integer), which decreases memory footprint and speeds up computation on resource-constrained devices like mobile phones. This directly lowers inference latency without requiring additional hardware or architectural changes.

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Increase batch size

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Add more layers

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Use a larger model

Question 7

A company deploys a chatbot that frequently gives outdated information. They want to implement a defense against prompt injection that also ensures responses are based on verified knowledge. Which approach is best?

Accepted Answer

Output filtering with a curated knowledge base. Output filtering combined with a knowledge base ensures that the chatbot's responses are checked against verified facts, mitigating both prompt injection and hallucination of outdated info.

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Input sanitization only

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Rate limiting

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Robust training on adversarial examples

Question 8

An AI practitioner needs to measure the performance of a binary classification model for disease detection, where the cost of false negatives is very high. Which metric should be prioritized?

Accepted Answer

Recall. Recall (true positive rate) minimises false negatives, which is critical when missing a positive case is dangerous.

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Precision

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F1-score

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Accuracy

Question 9

Based on the exhibit, what is the most likely cause of the pod failure and its solution?

Accepted Answer

The container memory limit is too low; increase the memory limit in the pod spec.. The pod failure is caused by an OOMKilled (Out of Memory) error, as indicated by the pod status in the exhibit. When a container exceeds its memory limit, Kubernetes terminates it with an OOMKilled exit code. Increasing the memory limit in the pod spec allows the container to allocate more memory, resolving the failure.

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The node has insufficient CPU; add more CPU.

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The pod is configured with wrong GPU drivers; update drivers.

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The model is too large; use a smaller model.

Question 10

A data scientist is building a recommendation system using Apache Spark for feature engineering. They need to process streaming user click data in real-time before feeding into the model. Which tool should they use for the streaming data ingestion?

Accepted Answer

Apache Kafka. Apache Kafka is the correct choice because it is a distributed streaming platform designed for high-throughput, fault-tolerant, real-time data ingestion. It acts as a durable message broker that can ingest streaming click data and make it available for Spark Structured Streaming to process in micro-batches or continuous processing mode, which is essential for real-time feature engineering in a recommendation system.

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Amazon S3

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Airflow

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Snowflake

Question 11

A data scientist is preparing a dataset for a text classification model. To prevent train/test leakage, which THREE practices should they follow?

Accepted Answer

Use time-based splitting for temporal data. Time-based splitting preserves the temporal order of data, which is critical for time-series or temporal text data to prevent the model from learning from future information that would not be available at inference time. This avoids train/test leakage where future data leaks into the training set, artificially inflating model performance.

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Shuffle the entire dataset before splitting to ensure randomness

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Apply feature scaling to the entire dataset before splitting

Question 12

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. Retrieval-Augmented Generation (RAG) is the most appropriate approach because it allows the chatbot to answer questions based on the latest policy documents without retraining the model. By indexing the documents in a vector store and retrieving relevant chunks at query time, RAG provides up-to-date, contextually accurate answers while keeping the underlying LLM static, which avoids the cost and complexity of monthly retraining.

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Use a larger foundation model with a longer context window and paste all documents into each prompt

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Fine-tune a base LLM on the policy documents monthly

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Train a custom model from scratch on the policy documents each month

Question 13

A data scientist is training a large language model and wants to reduce the carbon footprint. Which practice is MOST effective for reducing energy consumption during training?

Accepted Answer

Apply model pruning and knowledge distillation. Model pruning reduces the number of parameters in the model, and knowledge distillation trains a smaller student model to mimic a larger teacher model. Both techniques directly reduce the computational operations (FLOPs) required during training and inference, leading to significant energy savings. In contrast, using FP32 or increasing epochs increases energy consumption, and adding more GPUs increases total power draw even if wall-clock time decreases.

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Use FP32 precision instead of mixed precision

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Use more GPUs in parallel to finish training faster

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Increase the number of training epochs for better accuracy

Question 14

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

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Fine-tune a base LLM on the policy documents monthly

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Train a custom model from scratch on the policy documents each month

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Use a larger foundation model with a longer context window and paste all documents into each prompt

Question 15

A company is fine-tuning a large language model using PEFT (Parameter-Efficient Fine-Tuning) to reduce GPU memory usage. They have limited hardware and need to fine-tune a 70B parameter model on a single GPU with 24 GB VRAM. Which technique is MOST suitable?

Accepted Answer

QLoRA (Quantization-aware LoRA) with 4-bit quantization. QLoRA combines quantization (4-bit) and LoRA to fine-tune very large models on limited hardware, achieving significant memory reduction while maintaining performance.

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Full fine-tuning with gradient checkpointing

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Instruction tuning with a smaller 7B model

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LoRA (Low-Rank Adaptation) alone