AI0-001 Implementing AI Solutions Practice Test 8 — 15 Questions

Question 1

During the data preparation phase of an AI project, a data scientist discovers that the target variable in a binary classification dataset is heavily imbalanced: 95% negative class and 5% positive class. Which technique should be applied to improve model performance on the minority class?

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

Accepted Answer

Apply oversampling of the minority class using techniques like SMOTE. The dataset is heavily imbalanced (95% negative, 5% positive), which can cause models to be biased toward the majority class. Oversampling the minority class using techniques like SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic examples of the minority class, balancing the class distribution and improving the model's ability to learn the minority class patterns. This is a standard approach to address class imbalance.

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Remove all samples from the majority class to balance the dataset

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Normalize all numerical features to have zero mean and unit variance

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Use a train-test split of 80-20 without any modification

Question 2

A data science team is training an image classification model for a medical imaging application. To prevent data leakage, they must partition the dataset correctly. Which approach ensures that no patient images appear in both training and test sets?

Accepted Answer

Split by patient ID so that all images of a patient go to one set only. Data leakage occurs when information from the test set leaks into training. Splitting by patient ID ensures that all images from the same patient are kept together in one partition.

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Shuffle the dataset and take the first 80% for training and last 20% for testing

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Use k-fold cross-validation without grouping

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Randomly split all images into training and test sets

Question 3

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

Question 4

In the AI project lifecycle, after a model is trained and evaluated, it is deployed to a production environment. What is the NEXT critical step to ensure the model continues to perform well over time?

Accepted Answer

Monitoring the model's performance and data drift. After deployment, the next critical step is monitoring the model's performance and detecting data drift, because production data distributions change over time and model accuracy degrades silently. Monitoring closes the MLOps loop by feeding real-world performance signals back to the team, enabling timely retraining or rollback. Without monitoring, degradation goes unnoticed until business impact occurs.

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Collect more training data

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Archive the model and start a new project

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Re-train the model from scratch

Question 5

A developer is building an AI microservice that processes document intelligence requests asynchronously. Users upload PDFs, and the service extracts text and analyzes it with an LLM. The processing time per document can be up to 5 minutes. Which integration pattern is MOST appropriate?

Accepted Answer

Async processing with a message queue and separate worker service. Async processing with a message queue and separate worker service is the correct pattern because document processing can take up to 5 minutes, which exceeds typical synchronous HTTP timeout limits (often 30-60 seconds). The API accepts the upload, enqueues a job, and returns immediately; a worker service consumes the queue, performs text extraction and LLM analysis, and stores results for later retrieval. This decouples request handling from long-running work and provides resilience against worker failures.

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Synchronous REST API call that waits for the LLM response

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WebSocket connection for real-time streaming

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Serverless function triggered by HTTP request

Question 6

When implementing a vector store for a RAG system, which similarity search metric is MOST commonly used to find the most relevant document chunks for a given query embedding?

Accepted Answer

Cosine similarity. Cosine similarity is the most common metric for comparing embedding vectors in RAG because it measures the angle between vectors, which works well for high-dimensional semantic embeddings.

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Manhattan distance

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Euclidean distance

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Dot product

Question 7

A recommendation system for an e-commerce platform is experiencing a high false positive rate in its anomaly detection module, causing legitimate transactions to be flagged as fraudulent. The team wants to reduce false positives without significantly increasing false negatives. Which action is MOST effective?

Accepted Answer

Increase the anomaly detection threshold. Increasing the anomaly detection threshold makes the model more conservative about flagging transactions as anomalous, which directly reduces false positives (legitimate transactions incorrectly flagged). Because the threshold is raised, only more extreme deviations trigger an alert, so some true anomalies may be missed, but the question explicitly accepts a small increase in false negatives as a tradeoff.

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Decrease the anomaly detection threshold

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Use a different anomaly detection algorithm

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Increase the size of the training dataset

Question 8

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. Option B is correct because for temporal data, a time-based split (e.g., training on earlier timestamps and testing on later ones) prevents future information from leaking into the training set, which a random split would allow. Option C is correct because performing the train/test split before any cleaning, normalization, or other preprocessing ensures that statistics and transformations are learned only from the training data and not influenced by the test set. Option E is correct because removing duplicate samples and keeping all text from the same document in a single split prevents identical or near-identical content from appearing in both training and test sets, which would inflate performance estimates. Option A does not belong because shuffling the entire dataset before splitting is not inherently leakage-preventing and can actually cause leakage with temporal or grouped data. Option D does not belong because applying feature scaling to the entire dataset before splitting leaks test-set statistics (mean, variance) into training, which is a classic preprocessing leakage error.

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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 9

A developer is building an AI agent that needs to call external tools (e.g., weather API, database) and reason about the results to answer user queries. Which THREE components are essential for implementing this agentic workflow?

Accepted Answer

Planning capability (e.g., step-by-step decomposition). Option A (Planning capability) is correct because an agentic workflow requires the model to decompose a complex user query into an ordered sequence of steps, deciding which sub-tasks to execute and in what order before invoking tools. Option B (ReAct loop) is correct because the Reasoning + Acting pattern interleaves thought, action, and observation cycles, letting the agent call a tool, reason over the returned result, and decide the next action until the query is resolved. Option E (Function calling or tool use interface) is correct because the agent must have a structured mechanism (e.g., JSON schema-based function/tool definitions) to invoke the weather API or database and receive machine-readable outputs. Option C (Fine-tuned domain-specific model) is not essential, since a general-purpose LLM with tool-calling and reasoning prompts can drive the workflow without domain fine-tuning. Option D (Vector store for long-term memory) is not essential either, as retrieval-based long-term memory is an optional enhancement rather than a required component for calling tools and reasoning over their results.

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Fine-tuned domain-specific model

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A vector store for long-term memory

Question 10

A data science team is building a binary classifier to detect fraudulent transactions. The dataset has only 2% fraud cases. Which data preparation technique is MOST critical to address this imbalance?

Accepted Answer

Apply synthetic minority oversampling (SMOTE) to the training set. With only 2% fraud cases, the dataset is severely imbalanced, which can cause the classifier to be biased toward the majority class (non-fraud) and achieve high accuracy without learning to detect fraud. SMOTE (Synthetic Minority Oversampling Technique) addresses this by generating synthetic examples of the minority class (fraud) in the training set, balancing the class distribution and improving the model's ability to generalize to fraud cases. This is the most critical technique among the options because it directly tackles the class imbalance problem, which is the primary challenge in this scenario.

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Use one-hot encoding on categorical features

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Remove outliers from the transaction amounts

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Normalize all numerical features to have zero mean and unit variance

Question 11

An AI system must extract text from scanned invoices and output structured fields (invoice number, date, total amount). Which type of AI application is this?

Accepted Answer

Document intelligence. Document intelligence (D) is the correct answer because it specifically refers to AI systems that extract, classify, and structure data from documents like invoices, receipts, and forms. This application uses optical character recognition (OCR) combined with natural language processing (NLP) to identify and output structured fields such as invoice number, date, and total amount, which is exactly what the question describes.

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Chatbot/virtual assistant

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Code generation

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Image classification/object detection

Question 12

A team is deploying a generative AI model for a real-time customer-facing application. They need to balance cost and latency. Which deployment strategy is MOST suitable?

Accepted Answer

AI microservices with streaming responses and async processing queues. AI microservices with streaming responses and async processing queues decouple inference from the request lifecycle, allowing the system to handle variable loads efficiently while maintaining low latency for real-time interactions. This architecture balances cost by scaling only the necessary components (e.g., GPU-backed inference services) and uses streaming (e.g., Server-Sent Events or WebSockets) to deliver partial results, reducing perceived latency for the customer.

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Monolithic API with serverless functions

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Edge deployment on user devices

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Batch processing with synchronous requests

Question 13

A team is evaluating an LLM-based chatbot that frequently hallucinates when answering questions about internal policies. Which testing approach would MOST effectively quantify this issue?

Accepted Answer

Evaluation frameworks for LLM output quality. Evaluation frameworks for LLM output quality, such as those using metrics like faithfulness, factuality, or ROUGE/BLEU scores, are specifically designed to detect and quantify hallucinations by comparing generated responses against a ground-truth knowledge base. This directly measures the rate at which the chatbot fabricates or misstates internal policy details, providing a quantitative baseline for improvement.

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Integration tests for API calls

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Unit tests for the data pipeline

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Regression testing of model accuracy over time

Question 14

An AI application needs to generate structured JSON output from an LLM. The development team wants to ensure the output always conforms to a specific schema. Which prompt engineering technique is MOST suitable?

Accepted Answer

System prompt with JSON schema and a 'respond only with valid JSON' instruction. Providing the JSON schema directly in the system prompt, combined with an explicit instruction to respond only with valid JSON, is the most direct and reliable way to constrain an LLM's output format. This technique leverages the model's instruction-following capability and schema awareness without requiring examples or retraining, ensuring strict adherence to the desired structure.

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Few-shot examples showing correct JSON

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Chain-of-thought prompting

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Fine-tuning the model on JSON datasets

Question 15

A company wants to build a system that automatically tags uploaded images with objects they contain (e.g., 'car', 'tree', 'person'). Which AI application type is this?

Accepted Answer

Image classification/object detection. The task of identifying and labeling objects (e.g., 'car', 'tree', 'person') within an image is a classic use case for image classification combined with object detection. Image classification assigns a single label to the entire image, while object detection localizes and classifies multiple objects within the image, which is exactly what the system requires.

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Recommendation system

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Anomaly detection

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Document intelligence