AI0-001 Implementing AI Solutions Practice Test 2 — 15 Questions

Question 1

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.

Answer

Manhattan distance

Answer

Euclidean distance

Answer

Dot product

Question 2

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

Answer

Single-turn response generation

Answer

Static prompt with no iterations

Question 3

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

Answer

Normalising all text to lowercase

Question 4

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

Answer

Monitoring prediction confidence scores over time

Answer

Retraining the model weekly as a routine

Question 5

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.

Answer

The model is overfitting on the minority class

Answer

The data has a train/test leakage problem

Answer

The feature extraction is incorrect for the minority vendor invoices

Question 6

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

Answer

Feature selection and dimensionality reduction

Answer

Data augmentation and one-hot encoding

Question 7

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

Answer

Fixed-size chunking with 256 tokens and no overlap

Answer

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

Question 8

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

Answer

Chain-of-thought reasoning

Answer

Few-shot prompting with examples of API calls

Question 9

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.

Answer

Batch processing using Apache Spark jobs triggered hourly

Answer

Serverless functions deployed on a CDN

Answer

A monolithic web application with a relational database

Question 10

A data scientist is building a binary classification model to predict customer churn. The dataset has 90% non-churn and 10% churn. After training, the model achieves 90% accuracy, but the recall for the churn class is only 20%. Which metric should the team primarily focus on to evaluate the model's effectiveness?

Accepted Answer

Recall for the churn class. When classes are imbalanced, accuracy is misleading. Recall (or F1) for the minority class is more informative.

Answer

Accuracy

Answer

Area Under the ROC Curve (AUC-ROC)

Answer

Precision for the non-churn class

Question 11

A developer is fine-tuning a large language model for a code generation task. The available GPU has only 8GB of VRAM, and the base model is 7B parameters. Which fine-tuning technique is MOST feasible?

Accepted Answer

QLoRA (Quantized Low-Rank Adaptation). QLoRA (Quantized Low-Rank Adaptation) combines quantization and LoRA to fine-tune large models on limited VRAM.

Answer

LoRA (Low-Rank Adaptation)

Answer

Instruction tuning with a smaller model

Answer

Full fine-tuning of all parameters

Question 12

A company has an existing AI chatbot that uses a fine-tuned LLM to answer customer queries. They want to add the ability to retrieve real-time order status from their database. Which integration pattern should they use?

Accepted Answer

Implement function calling so the model can trigger a database query and receive the result. Function calling allows the LLM to request database queries, and the application can execute them and return results.

Answer

Prompt the user to check the order status manually

Answer

Use RAG to retrieve order status from a vector store

Answer

Embed the database query results directly into the model's training data

Question 13

A data science team is preparing a dataset for a supervised learning task. They split the data into training and test sets. The team then normalizes the features using the mean and standard deviation calculated from the entire dataset before splitting. What issue does this introduce?

Accepted Answer

It introduces train/test leakage. Using statistics from the entire dataset before splitting causes test data information to influence the training process, leading to train/test leakage and overly optimistic performance estimates.

Answer

It improves model generalization

Answer

It causes the model to overfit the training data

Answer

It reduces the variance of the features

Question 14

A developer is building a RAG system and needs to choose a similarity metric for retrieving document chunks. The embedding model they use produces normalized vectors (unit vectors). Which similarity metric is equivalent to cosine similarity in this case?

Accepted Answer

Dot product. For normalized vectors, cosine similarity and dot product are equivalent because the dot product equals the cosine of the angle times the product of magnitudes (which are 1).

Answer

Jaccard similarity

Answer

Euclidean distance

Answer

Manhattan distance

Question 15

An organization wants to implement an AI system to automatically categorize support tickets into predefined categories. They have a labeled dataset of 10,000 tickets. Which approach is MOST appropriate?

Accepted Answer

Fine-tune a pre-trained text classification model. Fine-tuning a pre-trained text classification model is a standard and effective approach for supervised classification when labeled data is available.

Answer

Use a rule-based system with keyword matching

Answer

Use a prompt-based LLM with few-shot examples

Answer

Train a custom neural network from scratch