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Question 1
A generative AI engineer needs to implement a Large Language Model (LLM) application that retrieves relevant context from a company’s documentation before generating responses. Which architectural pattern is most appropriate for this use case?
A) Retrieval Augmented Generation (RAG)
B) Simple prompt engineering without context
C) Fine-tuning on all possible questions
D) Random text generation
Answer: A
Explanation:
Retrieval Augmented Generation is the most appropriate pattern for grounding LLM responses in company-specific documentation. RAG combines information retrieval with text generation by first searching relevant documents using vector similarity search, then including retrieved context in prompts to guide LLM responses. This architecture enables LLMs to provide accurate, up-to-date information from specific knowledge bases without requiring model retraining. RAG reduces hallucinations by anchoring responses in actual documentation, supports dynamic knowledge updates without model changes, and scales efficiently for large document collections. This pattern is fundamental for enterprise LLM applications needing domain-specific knowledge.
B is incorrect because simple prompt engineering without context cannot access company-specific documentation, forcing LLMs to rely solely on training data. Without retrieval, LLMs cannot incorporate recent information, company policies, or proprietary knowledge not present in training data. Responses would be generic rather than grounded in actual documentation. Simple prompts work for general knowledge but inadequate for applications requiring specific, current information from controlled knowledge bases.
C is incorrect because fine-tuning on all possible questions is impractical as question variations are infinite and documentation changes frequently. Fine-tuning requires expensive retraining for each documentation update, doesn’t scale for large evolving knowledge bases, and risks catastrophic forgetting of general capabilities. RAG provides more flexible, maintainable approach by separating knowledge retrieval from generation. Fine-tuning is better suited for adapting model behavior or style rather than incorporating dynamic factual knowledge.
D is incorrect because random text generation produces unreliable, ungrounded responses unsuitable for enterprise applications. The scenario specifically requires accurate responses based on company documentation, which random generation cannot provide. Modern LLM applications need deterministic, contextually appropriate responses grounded in authoritative sources. Random generation contradicts every principle of reliable AI system design and fails to meet basic accuracy requirements.
Question 2
An AI engineer is building a vector database to store document embeddings for semantic search in a RAG application. Which Databricks component is designed for storing and querying vector embeddings?
A) Vector Search in Databricks
B) Standard SQL tables only
C) Text files in DBFS
D) Email storage
Answer: A
Explanation:
Vector Search in Databricks is specifically designed for storing, indexing, and querying high-dimensional vector embeddings at scale. Vector Search creates optimized indexes supporting approximate nearest neighbor search, enabling fast similarity queries across millions of embeddings. Integration with Delta Lake provides ACID transactions, versioning, and unified governance for vector data. Vector Search automatically syncs with Delta tables, updates indexes as data changes, and integrates with MLflow for tracking embedding models. This managed service handles the complexity of vector similarity search while providing enterprise features like security, monitoring, and scalability.
B is incorrect because standard SQL tables store structured data but lack specialized indexing and query capabilities for high-dimensional vectors. While embeddings can be stored as arrays in SQL tables, querying for nearest neighbors requires full table scans or inefficient computations. Standard SQL lacks vector similarity functions and distance metrics needed for semantic search. Vector Search provides purpose-built infrastructure for embedding storage and retrieval that standard tables cannot efficiently deliver.
C is incorrect because text files in DBFS provide unstructured storage without indexing, querying capabilities, or integration with search infrastructure. Text files require custom code for loading, parsing, and searching embeddings. File-based storage lacks transaction support, concurrent access control, and incremental updates. Vector Search provides managed infrastructure with APIs, query optimization, and production features that text files cannot offer. File storage is appropriate for archival, not operational vector search.
D is incorrect because email storage is designed for message communication, completely unrelated to vector database requirements. Email systems lack vector indexing, similarity search, or integration with LLM applications. This answer demonstrates fundamental confusion between communication tools and specialized database infrastructure. Vector embeddings require purpose-built search systems like Vector Search, not communication platforms.
Question 3
A team needs to create embeddings from text documents to enable semantic search. Which type of model converts text into numerical vector representations?
A) Embedding model (e.g., sentence transformers, OpenAI embeddings)
B) Image classification model
C) Audio transcription model
D) Random number generator
Answer: A
Explanation:
Embedding models transform text into dense numerical vector representations capturing semantic meaning in high-dimensional space. Models like sentence transformers, OpenAI text-embedding-ada-002, or BGE encoders are specifically trained to encode text such that semantically similar texts have similar vector representations. These models enable semantic search by allowing similarity comparisons through vector distance metrics like cosine similarity or Euclidean distance. Pre-trained embedding models work across domains while domain-specific fine-tuning improves performance for specialized vocabularies. Embedding quality directly impacts RAG system effectiveness.
B is incorrect because image classification models process visual data to categorize images, not convert text to vectors. Image models operate on pixel data using convolutional or vision transformer architectures completely different from text encoding. While both produce embeddings, image models cannot process text input. The task specifically requires text-to-vector conversion which image classifiers don’t provide. Using wrong model type would fail to process text inputs entirely.
C is incorrect because audio transcription models convert speech to text but don’t create semantic embeddings. Transcription models like Whisper perform speech recognition, outputting text transcripts rather than embedding vectors. While transcription could be a preprocessing step before embedding, transcription models alone don’t create the vector representations needed for semantic search. The scenario requires text embeddings, not speech-to-text conversion.
D is incorrect because random number generators produce arbitrary values without semantic meaning or consistency. Random vectors don’t capture text semantics, making similar texts have completely different representations. Semantic search requires embeddings where similar content produces similar vectors, which randomness cannot provide. Random generation contradicts the fundamental requirement for meaningful semantic representations that enable similarity-based retrieval.
Question 4
An organization needs to evaluate a generative AI application’s response quality. Which metric measures how well generated responses are grounded in provided source documents?
A) Faithfulness or groundedness
B) Token count only
C) Response length
D) Random scoring
Answer: A
Explanation:
Faithfulness or groundedness measures whether generated responses accurately reflect information from provided source documents without hallucination or fabrication. This metric evaluates if claims in responses can be verified against retrieved context, ensuring LLM doesn’t introduce unsupported information. Faithfulness is critical for RAG applications where accuracy and trustworthiness depend on staying true to source material. Evaluation can use automated methods like natural language inference models checking entailment between responses and sources, or human evaluation assessing factual consistency. High faithfulness indicates reliable, trustworthy AI systems.
B is incorrect because token count measures response length in tokens but says nothing about response accuracy, relevance, or groundedness in source material. A response could have appropriate token count while being completely inaccurate or hallucinated. Token count is a technical metric for API costs and context limits but doesn’t evaluate quality or faithfulness. The scenario specifically asks about response quality relative to source documents, which token count cannot assess.
C is incorrect because response length measures how many words or characters are generated but doesn’t evaluate whether content is accurate or grounded in sources. Long responses can be verbose and unfaithful while short responses might be concise and accurate. Length is a superficial metric that doesn’t capture semantic quality or factual correctness. Evaluating generative AI requires content-based metrics like faithfulness, not just length measurements.
D is incorrect because random scoring provides no meaningful evaluation of response quality, groundedness, or any other performance metric. Evaluation must be systematic and criterion-based to provide actionable insights. Random scores can’t distinguish good from bad responses, preventing improvement and quality assurance. Professional AI evaluation requires rigorous metrics like faithfulness that systematically assess specific quality dimensions. Random scoring represents complete absence of meaningful evaluation methodology.
Question 5
A data scientist needs to fine-tune a foundation model on company-specific data to adapt its behavior and outputs. Which statement about fine-tuning is correct?
A) Fine-tuning updates model weights using supervised learning on domain-specific data
B) Fine-tuning requires no training data
C) Fine-tuning makes models larger automatically
D) Fine-tuning only changes prompt templates
Answer: A
Explanation:
Fine-tuning updates pre-trained model weights through supervised learning on domain-specific datasets, adapting model behavior for specific tasks, styles, or domains. Fine-tuning typically uses smaller learning rates than pre-training to avoid catastrophic forgetting while adjusting model parameters to improve performance on target tasks. This process requires labeled examples showing desired inputs and outputs. Fine-tuning is effective for teaching models domain vocabulary, specific output formats, or task-specific behaviors that differ from general pre-training objectives. Modern approaches like LoRA make fine-tuning more efficient by updating only subset of parameters.
B is incorrect because fine-tuning fundamentally requires training data showing desired model behavior. Without examples of correct inputs and outputs, there’s no signal for adjusting model weights. Supervised fine-tuning needs labeled pairs demonstrating what responses model should generate for given inputs. The quantity and quality of training data directly impact fine-tuning effectiveness. Data-free adaptation exists as separate techniques like prompt engineering but fine-tuning specifically involves training on examples.
C is incorrect because fine-tuning doesn’t automatically increase model size, rather it adjusts existing parameters or adds small adapter modules. Traditional fine-tuning maintains model architecture while updating weights. Modern efficient fine-tuning methods like LoRA actually add minimal parameters compared to full model size. Model size is determined by architecture design, not fine-tuning process. Fine-tuning focuses on parameter values rather than model capacity changes.
D is incorrect because fine-tuning modifies model weights through training, not just prompt templates. Prompt engineering changes inputs without training while fine-tuning performs actual supervised learning updating parameters. These are distinct adaptation techniques with different requirements, costs, and use cases. Prompt changes are immediate and require no training while fine-tuning is training-intensive but produces permanent model adaptations. Confusing these approaches misunderstands fundamental ML concepts.
Question 6
An AI engineer needs to implement a chain in LangChain that sequentially processes data through multiple steps including retrieval, summarization, and generation. What is the primary purpose of chains in LangChain?
A) Orchestrating multiple LLM calls and operations into sequential workflows
B) Physical metal chains for hardware
C) Blockchain cryptocurrency
D) Supply chain management
Answer: A
Explanation:
Chains in LangChain orchestrate multiple LLM calls and operations into sequential or conditional workflows, enabling complex multi-step reasoning and processing. Chains connect various components like prompts, LLMs, retrievers, and output parsers into pipelines where each step’s output feeds into subsequent steps. This abstraction simplifies building sophisticated applications like RAG systems, multi-hop reasoning, or document processing pipelines. LangChain provides pre-built chains for common patterns while supporting custom chain creation. Chains manage state passing, error handling, and control flow across multiple LLM interactions.
B is incorrect because LangChain chains refer to software abstractions for orchestrating LLM workflows, not physical metal chains for hardware. This interpretation represents fundamental misunderstanding of software terminology. LangChain is a Python framework for LLM application development, operating entirely in software. Physical chains have no relationship to LLM orchestration or generative AI engineering. The terminology is metaphorical describing linked computational steps.
C is incorrect because LangChain chains are LLM workflow orchestration components, completely unrelated to blockchain or cryptocurrency. While both use “chain” terminology, they refer to entirely different concepts. Blockchain chains are distributed ledgers while LangChain chains are sequential operation pipelines. This confusion conflates unrelated technologies. LangChain focuses on LLM application development, not distributed ledger technology or digital currency.
D is incorrect because supply chain management involves logistics and operations management, unrelated to LangChain’s software framework for LLM applications. While supply chains also use sequential process terminology, LangChain chains specifically orchestrate LLM operations. This answer confuses business operations with software engineering terminology. Understanding context distinguishes between homonyms in different domains – here referring to LLM workflow orchestration.
Question 7
A team is implementing prompt engineering to improve LLM outputs. Which technique provides examples of desired input-output pairs in the prompt to guide the model?
A) Few-shot learning
B) Zero-shot learning only
C) No examples needed
D) Random text insertion
Answer: A
Explanation:
Few-shot learning provides the LLM with a small number of example input-output pairs in the prompt, demonstrating the desired task format and behavior. Examples help models understand task requirements, output formatting, reasoning patterns, and domain-specific conventions without fine-tuning. Few-shot prompts typically include 1-10 examples showing various input cases and their corresponding correct outputs. This technique leverages LLM’s in-context learning capabilities where models adapt behavior based on provided context. Few-shot learning bridges zero-shot and fine-tuning, offering quick adaptation without training.
B is incorrect because the question specifically asks about providing examples, which zero-shot learning explicitly doesn’t include. Zero-shot learning gives only task instructions without examples, relying on model’s pre-existing knowledge. While zero-shot can work for clear, common tasks, few-shot generally improves performance by clarifying expectations through concrete examples. The scenario describes providing input-output pairs, which defines few-shot rather than zero-shot approach.
C is incorrect because the technique specifically involves providing examples, contradicting the “no examples needed” statement. Few-shot learning’s defining characteristic is including examples in prompts. While some tasks work without examples using zero-shot approaches, few-shot explicitly provides examples as described in the question. The scenario describes a technique using examples, making “no examples” factually incorrect for few-shot learning.
D is incorrect because random text insertion provides no meaningful guidance to LLMs and likely degrades performance. Few-shot learning uses carefully chosen, representative examples demonstrating correct task completion. Random text adds noise without useful signal. Effective prompt engineering requires intentional, structured examples that clarify task requirements. Random insertion contradicts systematic prompt engineering principles and fails to guide model behavior constructively.
Question 8
An organization needs to track experiments, log parameters, and version models when developing generative AI applications. Which Databricks component provides ML experiment tracking and model management?
A) MLflow
B) Email tracking
C) Spreadsheet logs
D) No tracking available
Answer: A
Explanation:
MLflow provides comprehensive ML experiment tracking, model versioning, and lifecycle management integrated with Databricks. MLflow tracks parameters, metrics, artifacts, and code versions across experiments, enabling comparison of different approaches and reproducibility. The Model Registry manages model versions, stages (staging, production), and lineage with governance controls. MLflow integrates with popular ML frameworks, supports custom metrics for generative AI evaluation, and provides APIs for programmatic access. For generative AI, MLflow tracks prompts, LLM configurations, embedding models, and custom evaluation metrics specific to text generation quality.
B is incorrect because email provides informal communication but lacks structured experiment tracking, versioning, or programmatic access needed for ML development. Email doesn’t capture parameter sweeps, metric histories, or artifact storage in queryable formats. Professional ML development requires purpose-built tools like MLflow offering systematic tracking, comparison capabilities, and integration with ML workflows. Email is inappropriate for technical experiment management requiring precision and automation.
C is incorrect because spreadsheets don’t scale for complex experiment tracking, lack integration with ML code, and don’t support artifact storage or model versioning. Manual spreadsheet entry is error-prone, doesn’t capture automated metrics, and becomes unmaintainable as experiments multiply. Spreadsheets lack APIs for programmatic logging from training code. MLflow provides automated tracking with richer metadata capture and better collaboration features than spreadsheets can offer.
D is incorrect because Databricks explicitly provides MLflow as its primary experiment tracking and model management platform. MLflow is deeply integrated with Databricks, offering managed tracking servers and Model Registry. This statement contradicts core Databricks capabilities that are fundamental to its ML platform. Microsoft acquired MLflow’s company specifically to enhance Azure Databricks’ ML capabilities, demonstrating strategic commitment to experiment tracking.
Question 9
A generative AI engineer needs to implement guardrails to prevent the LLM from generating harmful, biased, or inappropriate content. Which approach helps filter or modify potentially problematic outputs?
A) Output validation and content filtering using safety classifiers
B) No safety measures
C) Encouraging harmful outputs
D) Disabling all output generation
Answer: A
Explanation:
Output validation and content filtering using safety classifiers implement guardrails by checking generated content against policies before returning responses. Safety classifiers detect harmful content categories like hate speech, violence, sexual content, or personally identifiable information. Approaches include using dedicated safety models, keyword filtering, prompt injection detection, and semantic analysis. When problematic content is detected, systems can reject outputs, request regeneration with modified prompts, or return default safe responses. Layered safety approaches combine multiple detection methods for robust protection. Guardrails are essential for responsible AI deployment.
B is incorrect because operating without safety measures exposes users to harmful content and creates legal, ethical, and reputational risks. Responsible AI development requires proactive safety measures protecting users and organizations. Unfiltered LLM outputs can include biases, harmful suggestions, or inappropriate content from training data. Professional AI systems implement comprehensive safety frameworks. No safety measures violates AI ethics principles and acceptable deployment standards.
C is incorrect because encouraging harmful outputs directly contradicts responsible AI principles and ethical guidelines. AI systems should actively prevent harm, not promote it. This approach violates professional standards, legal requirements in many jurisdictions, and basic ethical obligations. Organizations deploying AI bear responsibility for system behavior and must implement safety measures. Deliberately harmful systems would face severe consequences including legal liability.
D is incorrect because completely disabling output generation prevents the system from functioning, defeating its purpose. The goal is generating safe, useful outputs while filtering harmful content, not eliminating all outputs. Effective guardrails allow appropriate responses while blocking problematic ones. Total output disabling represents overreaction preventing legitimate use. Balanced safety approaches maintain utility while implementing necessary protections.
Question 10
A data engineer needs to prepare documents for a RAG system by splitting them into smaller chunks for embedding and retrieval. What is the primary reason for chunking documents?
A) Embedding models and LLM context windows have length limitations requiring smaller text segments
B) Larger chunks always perform better
C) Chunking is unnecessary
D) Random text division
Answer: A
Explanation:
Chunking addresses practical limitations of embedding models and LLM context windows which have maximum token lengths they can process. Embedding models typically handle 512-8192 tokens per input while LLM context windows range from 4K-128K tokens depending on model. Chunking also improves retrieval precision by creating focused segments where each chunk represents specific concepts, enabling more relevant matches. Chunk size balances retrieval granularity against context completeness – smaller chunks provide focused retrieval while larger chunks maintain more context. Optimal chunk size depends on document structure, embedding model limits, and retrieval requirements.
B is incorrect because larger chunks don’t always perform better and can actually degrade retrieval quality by mixing multiple topics in single chunks, making similarity matching less precise. Excessively large chunks may exceed embedding model limits or dilute semantic focus. Optimal chunk size balances multiple factors including semantic coherence, retrieval precision, and technical limits. Chunk size is a hyperparameter requiring tuning based on specific use case and evaluation metrics.
C is incorrect because chunking is essential when documents exceed embedding model or LLM context limits, and even for shorter documents, chunking often improves retrieval precision. Without chunking, long documents cannot be processed by length-limited models, and retrieval would operate on entire documents rather than relevant sections. Professional RAG implementations carefully design chunking strategies considering document structure, semantic boundaries, and performance requirements. Chunking is fundamental preprocessing for vector search.
D is incorrect because random text division creates chunks breaking semantic boundaries, separating related content, and degrading retrieval quality. Effective chunking considers document structure like paragraphs, sections, or sentences, maintaining semantic coherence within chunks. Random division could split sentences mid-way or separate tightly coupled content. Thoughtful chunking strategies improve RAG performance while random division harms it. Chunking requires intentional design considering both semantic and technical constraints.
Question 11
An AI engineer is implementing a conversational AI application that needs to maintain context across multiple turns. Which component stores and manages conversation history?
A) Memory modules in conversational frameworks
B) No memory capability exists
C) Each message is completely independent
D) Random conversation generation
Answer: A
Explanation:
Memory modules in conversational frameworks like LangChain manage conversation history by storing previous messages and including relevant context in subsequent prompts. Memory implementations range from simple buffer memory storing recent messages to more sophisticated approaches like summary memory, vector store memory for semantic retrieval of relevant past interactions, or entity memory tracking mentioned entities. Memory enables coherent multi-turn conversations where LLMs can reference earlier discussion points, maintain consistent context, and provide relevant responses based on conversation flow. Effective memory management balances context richness against token limits.
B is incorrect because modern conversational AI frameworks explicitly provide memory capabilities as core features. LangChain, Semantic Kernel, and other frameworks include various memory implementations. Stateless LLM APIs require external memory management, which frameworks provide. Conversation applications fundamentally require memory to maintain coherence across turns. Claiming no memory capability contradicts both technical reality and functional requirements for conversational AI.
C is incorrect because treating each message independently prevents coherent conversations where users reference previous topics, ask follow-up questions, or build complex multi-turn interactions. While base LLM APIs are stateless, application frameworks maintain state by including conversation history in prompts. Completely independent messages would make chatbots unable to handle “What about X?” style follow-ups or maintain topic continuity. Conversational AI requires contextual memory.
D is incorrect because random conversation generation would produce incoherent dialogues ignoring user inputs and previous context. Conversational AI must maintain logical flow responding appropriately to user messages considering conversation history. Random generation contradicts the purpose of interactive dialogue systems. Professional conversational applications use structured memory management ensuring consistency and relevance across conversation turns.
Question 12
A team needs to compare different prompting strategies and LLM configurations to optimize application performance. Which practice systematically evaluates multiple approaches?
A) A/B testing or prompt experimentation with evaluation metrics
B) Using only first attempt without comparison
C) Random selection without measurement
D) No evaluation methodology
Answer: A
Explanation:
A/B testing or prompt experimentation with evaluation metrics systematically compares different approaches by testing variations against consistent evaluation criteria. This methodology involves defining success metrics (accuracy, faithfulness, relevance, coherence), creating prompt variations or configuration changes, evaluating each variant against test sets, and selecting best-performing approaches. Systematic experimentation using statistical significance testing prevents anecdotal decisions. MLflow tracks experiments enabling comparison across parameters. Professional development requires data-driven optimization rather than intuition alone. Evaluation metrics must align with application requirements and user needs.
B is incorrect because using only first attempt without comparison provides no basis for knowing if better approaches exist. Different prompts, models, or configurations can dramatically affect performance. Professional development requires exploring solution space, comparing alternatives, and selecting optimal approaches based on evidence. First attempts often have room for improvement discovered through systematic experimentation. Data-driven optimization consistently outperforms single-attempt approaches.
C is incorrect because random selection without measurement provides no learning or optimization. Effective development requires intentional variation testing and objective measurement. Random choices without evaluation can’t distinguish better from worse approaches, preventing quality improvement. Systematic experimentation with metrics enables identifying what works and why. Random approaches waste resources testing variations without capturing insights. Professional engineering requires methodical evaluation.
D is incorrect because lack of evaluation methodology prevents quality assurance, optimization, or confidence in deployed systems. Professional AI development requires systematic evaluation comparing approaches, measuring performance, and validating improvements. Without evaluation, teams cannot demonstrate value, identify regressions, or make informed decisions. Evaluation methodology is fundamental to engineering discipline and responsible AI development. Systematic evaluation distinguishes professional development from ad-hoc experimentation.
Question 13
An organization needs to serve a fine-tuned LLM model for real-time inference with low latency and high throughput. Which Databricks capability enables model serving?
A) Model Serving endpoints
B) Manual server configuration
C) Email-based inference
D) No serving capability
Answer: A
Explanation:
Model Serving endpoints in Databricks provide managed infrastructure for deploying ML models including LLMs with REST API access, automatic scaling, monitoring, and version management. Model Serving supports CPU and GPU instances optimized for inference, handles load balancing across replicas, and integrates with MLflow Model Registry for version control. For LLMs, serving endpoints manage model loading, tokenization, batching, and response generation with optimized inference engines. Monitoring dashboards track latency, throughput, and errors. Serverless serving options provide automatic scaling without infrastructure management.
B is incorrect because manual server configuration requires managing infrastructure, scaling, monitoring, and maintenance manually rather than leveraging managed services. Manual approaches increase operational overhead, require DevOps expertise, and lack integrated monitoring and management features. For production LLM serving, managed platforms like Databricks Model Serving provide better reliability, scaling, and integration with ML workflows. Manual configuration is more complex and error-prone than managed serving.
C is incorrect because email-based inference would introduce latency measured in seconds to minutes, completely unsuitable for real-time applications requiring millisecond to second response times. Email is asynchronous communication mechanism, not synchronous API for real-time inference. The scenario specifically requires low latency and high throughput which email cannot provide. This answer confuses communication protocols with inference serving infrastructure.
D is incorrect because Databricks explicitly provides Model Serving as a core platform capability. Model Serving is fundamental to ML deployment workflows that Databricks supports end-to-end from development through production. This statement contradicts documented platform features that Microsoft heavily promotes. Model serving is critical infrastructure that managed ML platforms must provide, and Databricks offers comprehensive serving capabilities.
Question 14
A generative AI application needs to include source citations showing which documents were used to generate responses. Why are source citations important in RAG applications?
A) Citations enable users to verify information, build trust, and trace response origins
B) Citations make responses longer unnecessarily
C) Citations are never useful
D) Random document references
Answer: A
Explanation:
Citations enable users to verify generated information against original sources, building trust through transparency and enabling validation of factual claims. Source attribution shows which retrieved documents influenced responses, allowing users to assess source authority and relevance. Citations support accountability by tracing response origins, which is critical for enterprise and regulated applications. When LLMs make mistakes, citations help users identify issues by checking source material. Professional RAG implementations return both generated text and source metadata including document titles, passages, and relevance scores.
B is incorrect because while citations add length, they provide valuable verification capability rather than unnecessary verbosity. The additional length serves important purposes including trust-building, verification, and accountability. Users benefit from knowing response origins even if it makes outputs slightly longer. Professional applications prioritize correctness and trustworthiness over brevity. Citations represent essential metadata rather than wasteful additions.
C is incorrect because citations are extremely useful for verification, trust, and accountability as explained above. This statement contradicts best practices in RAG system design and responsible AI deployment. Organizations deploying AI systems for important decisions need traceability and verification capabilities. Research and enterprise applications particularly value citations enabling validation of AI-generated content against authoritative sources.
D is incorrect because citations should reference actual documents used in generation, not random documents. Accurate citations are essential for verification purpose – misleading citations undermine trust worse than no citations. Professional RAG systems track which retrieved chunks influenced generation and return accurate source references. Random references would be deliberately misleading, violating trust and professional ethics. Citation accuracy is fundamental to their value proposition.
Question 15
A data scientist needs to evaluate whether generated responses answer user questions correctly and completely. Which evaluation metric assesses response relevance and completeness?
A) Answer relevancy
B) Model size only
C) Training time
D) Random scoring
Answer: A
Explanation:
Answer relevancy evaluates whether generated responses directly address user questions with appropriate, complete information. This metric assesses if responses stay on topic, cover key aspects of questions, and provide sufficient detail without excessive tangential information. Answer relevancy can be measured using semantic similarity between questions and responses, LLM-as-judge approaches where another LLM rates relevance, or human evaluation assessing whether responses satisfy information needs. High relevancy indicates responses provide value to users rather than generic or off-topic content.
B is incorrect because model size measures parameter count but doesn’t indicate response quality, relevance, or any output characteristics. Large models can produce irrelevant responses while smaller models might generate highly relevant outputs. Model size relates to capacity and computational requirements, not directly to response quality metrics. The question asks about evaluating response quality, which requires output-focused metrics rather than model architecture measurements.
C is incorrect because training time measures computational resources spent during training but provides no information about response relevance or quality. Training time is an operational metric for development efficiency, not an output quality assessment. Longer training doesn’t guarantee better relevance nor does shorter training indicate worse responses. The scenario requires evaluating generated outputs, not training processes. Training time and output quality are separate concerns.
D is incorrect because random scoring provides no meaningful evaluation of response relevance, preventing quality assessment, optimization, or validation. Evaluation must systematically measure actual response characteristics against defined criteria. Random scores don’t distinguish relevant from irrelevant responses, offering no actionable insights. Professional evaluation requires rigorous metrics measuring specific quality dimensions. Random scoring represents absence of evaluation methodology rather than valid assessment approach.
Question 16
An AI engineer needs to implement retrieval that finds semantically similar documents even when exact keywords don’t match. Which search approach enables semantic similarity matching?
A) Vector similarity search using embeddings
B) Exact keyword matching only
C) Random document selection
D) Alphabetical sorting
Answer: A
Explanation:
Vector similarity search using embeddings enables semantic matching by representing documents and queries as vectors in high-dimensional space where semantic similarity corresponds to vector proximity. Similarity metrics like cosine similarity or Euclidean distance identify documents with similar meanings even when using different words. For example, “automobile” and “car” have similar embeddings despite different text. Vector search uses approximate nearest neighbor algorithms for efficient similarity queries across large collections. This approach powers modern semantic search systems, dramatically improving retrieval quality over keyword-based methods.
B is incorrect because exact keyword matching misses semantically related documents using synonyms, related concepts, or different phrasings. Keyword matching suffers from vocabulary mismatch where users and documents describe same concepts differently. Traditional keyword search cannot understand that “ML engineer” and “machine learning engineer” refer to the same concept. The question specifically asks for semantic similarity which keyword matching doesn’t provide. Modern information retrieval requires semantic understanding beyond surface text matching.
C is incorrect because random document selection ignores query content entirely, returning irrelevant results. Random selection cannot satisfy user information needs or provide relevant context for RAG applications. The purpose of retrieval is finding relevant documents, which randomness cannot achieve. Random selection would make RAG systems useless by providing unrelated context. Retrieval must be query-based and relevance-driven, opposite of random selection.
D is incorrect because alphabetical sorting organizes documents by title or content lexically but doesn’t assess semantic relevance to queries. Alphabetical order is arbitrary regarding query relevance – documents at beginning of alphabet aren’t more semantically similar to queries than later documents. The question specifically requires finding semantically similar documents, which alphabetical sorting cannot accomplish. Sorting is organizational, not retrieval or relevance assessment.
Question 17
A team needs to implement temperature parameter tuning for an LLM to control output randomness. What does increasing temperature do to model outputs?
A) Increases randomness and creativity in generated text
B) Makes outputs completely deterministic
C) Changes model architecture
D) Affects only input processing
Answer: A
Explanation:
Increasing temperature increases randomness and creativity in generated text by flattening the probability distribution over potential next tokens. Higher temperature values (above 1.0) make less probable tokens more likely to be selected, producing more varied, creative, sometimes unexpected outputs. Lower temperature (closer to 0) makes the model more deterministic, consistently selecting highest-probability tokens for conservative, focused outputs. Temperature is sampling hyperparameter affecting generation diversity without changing model weights. Creative applications use higher temperatures while factual applications use lower temperatures for consistency.
B is incorrect because increasing temperature makes outputs less deterministic, not more. Completely deterministic outputs require temperature near zero where the model consistently selects highest-probability tokens. The question asks about increasing temperature which moves away from determinism toward randomness. Temperature controls the exploration-exploitation tradeoff in token selection with higher values favoring exploration. This answer states the opposite effect of increasing temperature.
C is incorrect because temperature is inference-time sampling parameter that doesn’t modify model architecture or weights. Temperature affects how predictions are sampled during generation, not how the model computes those predictions. Model architecture remains unchanged regardless of temperature settings. Temperature adjustment is parameter tuning for generation control, not architectural modification. Confusing sampling parameters with architecture demonstrates misunderstanding of model components.
D is incorrect because temperature specifically affects output generation by controlling token sampling randomness, not input processing. Inputs are embedded and processed identically regardless of temperature. Temperature applies during autoregressive generation when selecting each output token. Input processing precedes temperature application in the generation pipeline. This answer reverses where temperature operates in the inference process.
Question 18
An organization needs to implement prompt templates with variable substitution to reuse prompt structures across different inputs. Which component enables parameterized prompts?
A) Prompt templates with variable placeholders
B) Hard-coded static prompts only
C) Random prompt generation
D) No prompt customization
Answer: A
Explanation:
Prompt templates with variable placeholders enable parameterized prompts by defining reusable structures with substitutable variables for inputs like user questions, retrieved context, or configuration values. Templates use placeholder syntax like {question} or {context} that get filled with actual values at runtime. This approach promotes consistency by standardizing prompt structure while allowing dynamic content substitution. Templates support versioning, A/B testing different structures, and managing prompts separately from code. Frameworks like LangChain provide PromptTemplate classes handling variable substitution and validation.
B is incorrect because hard-coded static prompts cannot adapt to different inputs, requiring code changes for each variation and preventing reusability across use cases. Static prompts don’t scale when applications need to process many different inputs with consistent formatting. Hard-coding makes prompt iteration difficult and couples prompts tightly to code. The question specifically asks about variable substitution which static prompts cannot provide. Dynamic applications require flexible prompt generation, making static approaches inadequate.
C is incorrect because random prompt generation produces inconsistent, unpredictable prompts that don’t systematically guide LLM behavior. Effective prompts are carefully designed to elicit desired responses through specific instructions, examples, and formatting. Random generation would create malformed prompts with inconsistent quality. Professional prompt engineering requires intentional design and testing, not randomness. Random approaches contradict the requirement for reusable structures across different inputs.
D is incorrect because modern LLM frameworks explicitly provide prompt customization through templates, enabling parameterization and reusability. Prompt templates are fundamental features in LangChain, Semantic Kernel, and similar frameworks. This statement contradicts available tooling designed specifically for prompt management. Lack of customization would severely limit application flexibility and maintainability. Professional LLM applications require robust prompt management capabilities.
Question 19
A generative AI application needs to prevent prompt injection attacks where users manipulate prompts to bypass safety measures or extract system prompts. Which defensive technique helps mitigate prompt injection?
A) Input validation, prompt sanitization, and instruction hierarchy
B) No security measures needed
C) Accepting all user inputs without validation
D) Encouraging injection attempts
Answer: A
Explanation:
Input validation, prompt sanitization, and instruction hierarchy implement defense-in-depth against prompt injection by validating user inputs for malicious patterns, sanitizing inputs to remove potential injection syntax, and structuring prompts so system instructions take precedence over user content. Techniques include using delimiters clearly separating instructions from user input, implementing output filters detecting leaked system prompts, employing separate models for input classification, and constraining model behavior through system-level instructions. Multiple defensive layers provide robust protection since no single technique is perfect. Regular security testing identifies vulnerabilities requiring additional controls.
B is incorrect because prompt injection poses real security risks enabling unauthorized actions, information disclosure, or safety bypass. Unprotected systems allow attackers to manipulate LLM behavior through crafted inputs. Security measures are essential for production deployments handling untrusted user input. No security approach invites exploitation and violates responsible AI principles. Professional systems implement comprehensive security controls protecting against known attack vectors like prompt injection.
C is incorrect because accepting all user inputs without validation enables prompt injection, jailbreaking, and other attacks. Unvalidated input is fundamental security vulnerability regardless of application type. User inputs must be validated, sanitized, and constrained to prevent malicious manipulation. Accepting everything contradicts basic security engineering principles applied across software development. Particularly for AI systems with broad capabilities, input validation is critical security control.
D is incorrect because encouraging injection attempts promotes system abuse and security breaches rather than protecting against them. Organizations must actively defend against attacks, not facilitate them. This approach violates security, legal, and ethical obligations to protect systems and users. Responsible AI deployment requires proactive security measures preventing harm. Deliberately encouraging attacks represents gross negligence and potential liability.
Question 20
A team needs to implement monitoring for a deployed generative AI application to track performance, costs, and quality metrics over time. Which practice ensures ongoing system health and quality?
A) Continuous monitoring with logging, metrics tracking, and alerting
B) Deploy once without monitoring
C) No tracking of system behavior
D) Random periodic checking
Answer: A
Explanation:
Continuous monitoring with logging, metrics tracking, and alerting ensures ongoing system health by capturing application behavior, performance metrics, costs, and quality indicators in real-time. Comprehensive monitoring includes request/response logging for audit trails, latency and throughput metrics for performance, token usage for cost tracking, and quality metrics like faithfulness or relevance for output assessment. Alerting on anomalies or threshold breaches enables rapid incident response. Monitoring dashboards provide visibility into system behavior and trends. MLflow and Databricks provide integrated monitoring for ML applications. Continuous monitoring is essential for maintaining production systems.
B is incorrect because deploying without monitoring creates blind spots where issues go undetected, degraded performance remains invisible, and costs accumulate unexpectedly. Production systems require ongoing observation to ensure they meet SLAs, identify drift or regressions, and optimize resource usage. Deploy-and-forget approaches fail in production environments where conditions change and issues emerge over time. Responsible operations require continuous monitoring providing visibility into system behavior and health.
C is incorrect because tracking system behavior is fundamental to operations, reliability, and continuous improvement. Without tracking, teams cannot detect issues, measure performance, optimize costs, or validate that systems serve users effectively. Untracked systems provide no basis for troubleshooting when problems occur. Monitoring is standard practice across software engineering and especially critical for AI systems where behavior can drift or degrade. No tracking represents operational negligence.
D is incorrect because random periodic checking provides inconsistent, unreliable monitoring that misses issues occurring between checks and lacks systematic coverage of important metrics. Production systems require continuous, automated monitoring with real-time alerting on important events. Random checking is ad-hoc and insufficient for ensuring system reliability. Professional monitoring uses systematic metric collection, automated alerting, and comprehensive coverage. Random approaches cannot provide the reliability assurance production systems require.
