AI0-001 Practice Test 16 — 15 Questions

Question 1

A data engineer is building a pipeline to process streaming clickstream data and feed it into a real-time ML feature store. Which tool is BEST suited for the streaming ingestion?

Accepted Answer

Apache Kafka. Apache Kafka is the industry standard for high-throughput, fault-tolerant streaming data ingestion. It can handle real-time clickstream data and integrate with feature stores.

Answer

Amazon S3

Answer

Apache Airflow

Answer

Apache Spark (batch mode)

Question 2

A company is deploying a text generation model for customer service emails. They want to ensure the model's responses are factual and based on internal knowledge bases. Which technique is most effective?

Accepted Answer

Use Retrieval-Augmented Generation (RAG). RAG retrieves relevant documents from the knowledge base at inference time, grounding the model's responses in facts. Prompt engineering alone can't ensure factual accuracy; fine-tuning may still hallucinate; temperature reduction only reduces randomness.

Answer

Fine-tune the model on historical customer service emails

Answer

Write a detailed system prompt

Answer

Set the temperature to 0

Question 3

A developer is building a mobile app that uses a pre-trained image classification model on-device. Which framework should they use to run the model on iOS devices?

Accepted Answer

Core ML. Core ML is Apple's framework for on-device machine learning inference on iOS. TensorFlow Lite is for mobile and embedded, but Core ML is native to iOS and optimized.

Answer

Hugging Face Transformers

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TensorFlow Lite

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PyTorch Mobile

Question 4

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 5

During the evaluation phase of an AI project, the team measures the model's F1 score on a held-out test set. They find the F1 score is 0.92, but when deployed in production, the model performs poorly on new data. What is the MOST likely cause of this discrepancy?

Accepted Answer

Data leakage occurred between the training and test sets during preparation. Data leakage during preparation can cause overly optimistic evaluation scores. If the test set contains information from the training set, the model appears better than it really is. Overfitting is possible but less likely with a proper hold-out. Concept drift occurs over time, not immediately. Poor hyperparameter tuning usually yields lower scores, not inflated ones.

Answer

The production data has a different distribution than the training data (concept drift)

Answer

The model's hyperparameters were not properly tuned

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The model is overfitting to the training data

Question 6

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.

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It improves model generalization

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It causes the model to overfit the training data

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It reduces the variance of the features

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

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Stream results directly from the model to the client using WebSockets

Question 9

A company uses an LLM API to generate customer support responses. They want to prevent the LLM from generating harmful content, even when users attempt jailbreaking. Which defense is MOST effective at the application layer?

Accepted Answer

Output filtering and content moderation. Output filtering and content moderation is the most effective defense at the application layer because it directly inspects the LLM's generated response before it reaches the user. This approach can catch and block harmful content that results from successful jailbreaking attempts, which input validation alone cannot prevent since the model may still produce undesirable outputs even with sanitized inputs.

Answer

Input validation and sanitization

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Robust training techniques

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Rate limiting

Question 10

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 11

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 12

A data scientist is fine-tuning a large language model for a domain-specific task using QLoRA. Which TWO statements correctly describe QLoRA's advantages?

Accepted Answer

It enables fine-tuning on consumer-grade GPUs by reducing memory requirements. QLoRA uses 4-bit quantization to reduce memory, and freezes the base model while training low-rank adapters. It does not reduce inference latency (base model still runs), and does not require more data than full fine-tuning.

Answer

It requires more training data than full fine-tuning to achieve comparable accuracy

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It trains the full model parameters with low precision

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It increases inference speed compared to the base model

Question 13

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

Answer

Few-shot prompting with examples of API calls

Question 14

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 15

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)

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