AI0-001 Implementing AI Solutions — Timed 30 Questions

Question 1

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, the system retrieves relevant chunks for each query and passes them to a pre-trained LLM for generation, ensuring up-to-date responses with minimal maintenance overhead.

Answer

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

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Fine-tune a base LLM on the policy documents monthly

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

Question 2

A data science team is preparing a dataset for a binary classification model. The dataset has 95% negative class and 5% positive class. Which technique should they apply to avoid biased model predictions?

Accepted Answer

Apply resampling techniques such as SMOTE or random undersampling. Handling imbalanced data (e.g., oversampling the minority class or undersampling the majority class) is necessary to prevent the model from always predicting the majority class.

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Normalise all numerical features to a [0,1] range

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Shuffle the dataset randomly before splitting into train and test sets

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Remove all rows with missing values

Question 3

In the AI project lifecycle, which phase involves partitioning the dataset into training, validation, and test sets?

Accepted Answer

Data preparation. Data preparation includes splitting the data to evaluate model performance and prevent leakage.

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Data acquisition

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Model selection

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Problem definition

Question 4

A developer is implementing a RAG system and needs to chunk large legal documents. The documents contain nested clauses and cross-references that should not be split across chunks. Which chunking strategy is MOST suitable?

Accepted Answer

Semantic chunking based on sentence and paragraph boundaries. Semantic chunking uses sentence boundaries or natural breakpoints, preserving logical units.

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Random chunking with varying sizes

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Hierarchical chunking combining small and large chunks

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Fixed-size chunking with 256 tokens

Question 5

A team is evaluating a fine-tuned LLM for a code generation task. They notice the model rarely generates correct syntax but often produces plausible-looking code. Which evaluation metric is MOST appropriate to quantify this issue?

Accepted Answer

Pass@k (execution success rate). Pass@k measures the probability that at least one of k generated code samples passes a set of unit tests, directly quantifying execution correctness. Since the model produces plausible-looking but syntactically incorrect code, execution success rate (Pass@k) is the most appropriate metric to capture whether the code actually runs correctly, unlike surface-level similarity metrics.

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BLEU score

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Perplexity

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Exact match accuracy

Question 6

An AI system uses a pre-trained image classification model to detect defects in manufacturing. The team wants to deploy the model in an edge device with limited GPU memory. Which technique should they consider first?

Accepted Answer

Apply quantization to reduce model size. Quantization reduces the precision of the model's weights and activations (e.g., from 32-bit floating point to 8-bit integer), which significantly shrinks the model size and memory footprint while often maintaining acceptable accuracy. This is the most direct and effective first step for deploying a pre-trained model on an edge device with limited GPU memory, as it requires no retraining and immediately addresses the memory constraint.

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Train the model from scratch using a smaller dataset

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Use a larger model with more parameters for higher accuracy

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Increase the batch size to improve throughput

Question 7

A team is deploying a multi-modal AI model that processes both text and images. They need to ensure that inference requests are handled quickly even during traffic spikes. Which integration pattern is BEST suited for this use case?

Accepted Answer

Use an event-driven architecture with a queue and worker instances. Asynchronous processing with a message queue allows requests to be buffered and processed without blocking the user, scaling out workers as needed.

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Deploy a synchronous REST API with auto-scaling

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Stream responses directly from the model to the client

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Pre-compute all possible outputs and cache them

Question 8

In prompt engineering, which technique involves providing a few correct input-output examples in the prompt to guide the model's response?

Accepted Answer

Few-shot prompting. Few-shot learning includes examples in the prompt to demonstrate the desired output format and reasoning pattern.

Answer

System prompt engineering

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Chain-of-thought prompting

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Zero-shot prompting

Question 9

A recommendation system for an e-commerce site is producing stale suggestions that do not reflect recent user behavior. The system is updated offline every 24 hours. Which change would MOST directly address this issue?

Accepted Answer

Implement online learning to update the model incrementally in real time. Shorter update cycles or online learning can incorporate recent interactions more quickly, improving freshness.

Answer

Increase the number of features used in the model

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Add more training data from the past year

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Use a deeper neural network architecture

Question 10

During testing a chatbot, the QA team observes that the bot sometimes responds with harmful content when given adversarial prompts. Which type of testing should be prioritised to catch these edge cases?

Accepted Answer

Red-teaming and adversarial testing. Red-teaming and adversarial testing are specifically designed to probe an AI system for vulnerabilities, including generating harmful or unsafe outputs from adversarial prompts. This approach simulates real-world attacks to uncover edge cases that standard functional tests miss, making it the correct priority for catching harmful content in a chatbot.

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Unit tests for data pipeline functions

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Regression testing on previously fixed bugs

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Integration tests for API connectivity

Question 11

A company is fine-tuning an LLM for a domain-specific task using LoRA. They have limited GPU memory and need to reduce memory footprint without sacrificing fine-tuning quality. Which approach should they consider?

Accepted Answer

Use QLoRA with 4-bit quantized base model. QLoRA combines 4-bit NormalFloat quantization of the base model with LoRA adapters, drastically reducing GPU memory usage while preserving fine-tuning quality through techniques like double quantization and paged optimizers. This directly addresses the constraint of limited GPU memory without sacrificing the model's ability to learn domain-specific tasks effectively.

Answer

Use a larger batch size to speed up training

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Fine-tune all layers of the base model

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Increase the rank of LoRA adapters

Question 12

Which similarity measure is commonly used in vector search to find the angle between vectors, making it well-suited for high-dimensional embeddings?

Accepted Answer

Cosine similarity. Cosine similarity measures the cosine of the angle between two vectors, ranging from -1 to 1, and is robust to magnitude differences.

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Manhattan distance

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Euclidean distance

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Dot product

Question 13

A team is designing an AI agent that needs to interact with external APIs, search the web, and perform multi-step reasoning. Which TWO architectural components are essential for this agentic workflow? (Choose TWO.)

Accepted Answer

ReAct pattern (Reasoning + Acting). Tool use allows the agent to call external APIs, and the ReAct pattern (Reasoning + Acting) enables iterative reasoning and action steps.

Answer

Fine-tuning the base model

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Single-turn response generation

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Static prompt with no iterations

Question 14

An organisation is developing a document intelligence system that extracts information from scanned invoices. Which THREE data preparation steps are critical to ensure high extraction accuracy? (Choose THREE.)

Accepted Answer

Cleaning and correcting OCR output. Image preprocessing (like skew correction), OCR cleaning, and field annotation are essential for accurate extraction.

Answer

Removing punctuations and stopwords

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Normalising all text to lowercase

Question 15

A machine learning engineer is deploying a production model that requires strict monitoring. Which TWO monitoring strategies should be implemented to detect data drift and model degradation? (Choose TWO.)

Accepted Answer

Monitoring the distribution of model predictions. Tracking prediction distribution shifts and monitoring feature distribution over time help detect drift and degradation.

Answer

Logging all input data for manual review

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Monitoring prediction confidence scores over time

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Retraining the model weekly as a routine

Question 16

A company is building an AI-powered document intelligence system to extract key fields from scanned invoices. The data contains 95% of invoices from one vendor and 5% from others. During model training, the F1 score is 0.95 on the overall test set, but the performance on the minority vendor invoices is very poor. What is the MOST likely cause?

Accepted Answer

The dataset is imbalanced, and the model is biased toward the majority class. The imbalanced dataset causes the model to learn mostly from the majority class, leading to poor performance on the minority class. The other options are less likely given the high overall F1 score.

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The model is overfitting on the minority class

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The data has a train/test leakage problem

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The feature extraction is incorrect for the minority vendor invoices

Question 17

A data scientist is preparing a dataset for a classification model. The dataset has missing values in several features and features with very different scales. Which two data preparation steps should be applied?

Accepted Answer

Cleaning and normalization. Cleaning handles missing values (e.g., imputation), and normalization scales features to a similar range, which is important for many ML algorithms.

Answer

Outlier removal and binning

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Feature selection and dimensionality reduction

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Data augmentation and one-hot encoding

Question 18

A team is implementing a RAG system for legal document retrieval. The documents are long (50-100 pages) with clear section headings. They want to ensure that retrieved chunks are semantically coherent and respect document structure. Which chunking strategy is MOST appropriate?

Accepted Answer

Hierarchical chunking: first split by sections, then further split each section into fixed-size chunks with overlap. Hierarchical chunking preserves document structure by first splitting into sections, then further into chunks, maintaining semantic coherence.

Answer

Semantic chunking based on sentence embeddings

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Fixed-size chunking with 256 tokens and no overlap

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Recursive character text splitting with chunk size 1000 and chunk overlap 200

Question 19

A developer is building an AI agent that needs to call external APIs to complete user requests. The agent must decide which API to call based on the user's natural language input. Which technique should the developer use to enable the agent to invoke APIs?

Accepted Answer

Function calling. Function calling is a technique where the LLM outputs a structured request to call a predefined function/API, enabling tool use.

Answer

Fine-tuning the LLM on API documentation

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Chain-of-thought reasoning

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Few-shot prompting with examples of API calls

Question 20

A machine learning engineer is deploying a real-time anomaly detection system for manufacturing sensor data. The system must process thousands of readings per second with minimal latency. Which deployment architecture is BEST suited?

Accepted Answer

AI microservices with an async processing queue and streaming responses. AI microservices with async processing queues and streaming responses can handle high throughput and low latency for real-time data.

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Batch processing using Apache Spark jobs triggered hourly

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Serverless functions deployed on a CDN

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A monolithic web application with a relational database