AI0-001 Implementing AI Solutions Practice Test 3 — 15 Questions

Question 1

A data scientist is preparing a dataset for a regression model. The dataset contains 100 features, some of which are highly correlated. To improve model performance and reduce overfitting, which TWO techniques should the data scientist apply? (Select TWO)

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

Accepted Answer

Feature selection. Feature selection reduces the number of features, and dimensionality reduction (e.g., PCA) handles multicollinearity, both helping to reduce overfitting.

Answer

Data augmentation

Answer

Adding more hidden layers to the neural network

Answer

Increasing the learning rate

Question 2

A company is deploying a generative AI application that produces structured JSON output for downstream processing. They want to ensure the output is consistently valid JSON and matches a specific schema. Which THREE techniques should they use? (Select THREE)

Accepted Answer

Provide few-shot examples of the desired output. Structured output (JSON mode) forces valid JSON, system prompts instruct the model, and few-shot examples demonstrate the required schema.

Answer

Fine-tune the model on a dataset of JSON outputs

Answer

Increase the temperature parameter to 1.5

Question 3

A team is building an AI-powered recommendation system for an e-commerce platform. They want to test the system before deployment. Which TWO types of testing are MOST relevant for this AI system? (Select TWO)

Accepted Answer

Evaluation frameworks for model output quality. Unit tests for data pipelines ensure data integrity, and evaluation frameworks for LLM output quality (or model evaluation) test recommendation relevance.

Answer

Load testing the web server

Answer

Integration tests for API calls

Answer

Regression testing on the UI

Question 4

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 (Retrieval-Augmented Generation) 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.

Answer

Fine-tune a base LLM on the policy documents monthly

Answer

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

Answer

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

Question 5

A data scientist is preparing a dataset for a regression model. The dataset contains 100 features, some of which are highly correlated. To improve model performance and reduce overfitting, which TWO techniques should the data scientist apply? (Select TWO)

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

Accepted Answer

Apply SMOTE (Synthetic Minority Oversampling Technique) to generate synthetic fraud examples. Handling imbalanced data typically requires resampling. For recall maximization with moderate precision constraint, oversampling the minority class (e.g., SMOTE) is effective. Undersampling loses too many majority samples, and using raw data leads the model to predict the majority class.

Answer

Apply random undersampling of the majority class until the dataset is balanced

Answer

Remove all duplicate transactions from the dataset

Answer

Use the raw dataset without any resampling, relying on class weights during training

Question 6

A team is implementing a document intelligence solution to extract key-value pairs from invoices. They plan to use a pre-trained vision-language model with a RAG pipeline that indexes invoice images. Which chunking strategy is BEST suited for invoice documents that have a consistent layout but vary in length?

Accepted Answer

Hierarchical chunking that groups lines into logical sections (header, line items, totals). Invoices typically have sections (header, line items, totals). Hierarchical chunking preserves this structure, enabling retrieval at the section level. Fixed-size may split important fields, semantic chunking is less predictable on structured documents.

Answer

Semantic chunking based on sentence boundaries

Answer

Fixed-size chunking with 512 tokens per chunk

Answer

No chunking; pass the entire invoice as one document per query

Question 7

Which similarity metric is MOST appropriate for comparing dense vector embeddings in a vector store used for document retrieval, when the embeddings are normalized to unit length?

Accepted Answer

Cosine similarity. Cosine similarity is equivalent to dot product for normalized vectors and is the most common metric for semantic similarity. Euclidean distance is sensitive to magnitude and not ideal for normalized vectors.

Answer

Jaccard similarity

Answer

Manhattan distance

Answer

Euclidean distance

Question 8

A developer is integrating an AI microservice that accepts image uploads and returns classification labels. The service must handle spikes of up to 1,000 requests per minute but average 100 requests per minute. Which deployment architecture BEST meets these requirements with cost efficiency?

Accepted Answer

Use an async processing queue (e.g., RabbitMQ) with a pool of worker instances that auto-scale based on queue depth. Async processing with a queue allows buffering during spikes, scaling workers as needed. A synchronous always-on service would be over-provisioned for average load. Serverless with auto-scaling offers cost efficiency.

Answer

Expose the model via a serverless function (e.g., AWS Lambda) with synchronous invocation

Answer

Deploy the service as a synchronous REST API on a single always-on VM sized for peak load

Answer

Stream results directly from the model to the client using WebSockets

Question 9

A team fine-tunes a 7B parameter LLM using LoRA on a custom instruction dataset. After training, they observe that the model's outputs are only marginally different from the base model. Which is the MOST likely cause?

Accepted Answer

The LoRA rank was set too low (e.g., r=1), limiting the adapter's capacity to learn the task. LoRA has a rank hyperparameter that controls adapter expressiveness. If the rank is too low, the adapter cannot capture the desired task. Other hyperparameters like learning rate affect convergence but rank directly impacts capacity.

Answer

The dataset contained too many examples, overfitting the adapter

Answer

The base model was too small to benefit from fine-tuning

Answer

The learning rate was too high, causing the model to diverge

Question 10

A chatbot application uses a system prompt to set the assistant's behavior. The developer wants the LLM to output structured JSON for downstream processing. Which technique BEST ensures the output is valid JSON?

Accepted Answer

Use the LLM's built-in JSON mode (e.g., response_format='json_object'). Many LLMs support a JSON mode that constrains output to valid JSON. System prompts can request JSON but may be ignored. Few-shot examples help but are not foolproof. Chain-of-thought is for reasoning, not formatting.

Answer

Set the temperature to 0 to make output deterministic

Answer

Include a few-shot example showing a JSON output in the prompt

Answer

Add a chain-of-thought reasoning step before the output

Question 11

In the AI project lifecycle, which phase involves splitting the dataset into training, validation, and test sets while ensuring no data leakage?

Accepted Answer

Data preparation. Splitting the dataset into training, validation, and test sets is a core data preparation step that must be performed before any model training begins. This phase ensures that data leakage is prevented by keeping the test set completely isolated until final evaluation, which is critical for obtaining an unbiased estimate of model performance. In the AI project lifecycle, data preparation encompasses cleaning, transforming, and partitioning the data, making option A the correct phase.

Answer

Problem definition

Answer

Data acquisition

Answer

Model evaluation

Question 12

An AI system for detecting anomalies in manufacturing sensor data uses a model trained on normal operation data only. During monitoring, the model flags many false positives. Which adjustment is MOST likely to reduce false positives?

Accepted Answer

Adjust the anomaly detection threshold to be less sensitive (e.g., require a higher reconstruction error). Changing the anomaly detection threshold (e.g., lowering sensitivity) reduces false positives. Retraining with labeled anomalies is ideal but not always feasible. Using a different model type may not directly reduce false positives.

Answer

Switch from an autoencoder to a one-class SVM

Answer

Add synthetic anomalies to the training set and retrain as a supervised classifier

Answer

Increase the size of the training dataset with more normal operation data

Question 13

A company is deploying a code generation AI assistant for internal developers. They want to ensure the assistant does not generate code with security vulnerabilities. Which testing approach is MOST critical?

Accepted Answer

Integration tests that send security-focused prompts and validate the generated code against a static analysis tool. Integration tests that call the model with security-related prompts and scan outputs for vulnerabilities directly assess this requirement. Unit tests on data pipelines are for data correctness, not security of generated code.

Answer

Unit tests for the data pipeline that preprocesses prompts

Answer

Evaluation framework that measures BLEU score on a held-out set of code samples

Answer

Regression tests that compare outputs of new model versions against a golden dataset

Question 14

Which component in a RAG system is responsible for converting document chunks into numerical representations that enable similarity search?

Accepted Answer

Embedding model. An embedding model (or encoder) transforms text into dense vectors. The vector store indexes these vectors, and the LLM generates answers. Chunking is the splitting step.

Answer

Vector store index

Answer

Document chunker

Answer

Large language model (LLM)

Question 15

A team is building a recommendation system for an e-commerce platform. They want to use collaborative filtering but have a cold-start problem for new users. Which hybrid approach BEST addresses cold start while leveraging collaborative signals?

Accepted Answer

Implement a hybrid model that combines content-based features with collaborative filtering via a weighted ensemble. Content-based filtering uses user/item features to handle cold start, then combines with collaborative filtering for accuracy. Clustering or matrix factorization alone do not solve cold start.

Answer

Apply user clustering based on demographic data and then use collaborative filtering within clusters

Answer

Use only content-based filtering for all users

Answer

Use matrix factorization with implicit feedback only