Visit here for our full Databricks Certified Generative AI Engineer Associate exam dumps and practice test questions.
Question 121
Which Databricks feature enables collaborative development of generative AI applications with version control?
A) Standalone notebooks only
B) Databricks Repos with Git integration
C) Local file system only
D) Manual file copying
Answer: B
Explanation:
Databricks Repos provides Git integration enabling collaborative development with version control, branching, merging, and pull requests directly within the Databricks workspace. Repos synchronizes notebooks, scripts, and configuration files with Git repositories like GitHub, GitLab, or Azure DevOps, supporting modern software development workflows.
Repos enables teams to work on feature branches, review changes through pull requests, track modification history, and maintain separate development, staging, and production code versions. Integration with CI/CD pipelines automates testing and deployment. Repos supports both notebooks and Python files, enabling professional development practices for generative AI projects.
Option A is incorrect because standalone notebooks without version control lack collaboration capabilities, change tracking, branching support, and recovery mechanisms essential for team-based development of production applications.
Option C is incorrect because local file systems provide isolated development environments without collaboration features, centralized version history, conflict resolution, or integration with team workflows and deployment pipelines.
Option D is incorrect because manual file copying is error-prone, lacks version tracking, provides no merge capabilities, and creates synchronization challenges making it unsuitable for collaborative development.
Git integration through Databricks Repos is fundamental for professional generative AI application development following software engineering best practices.
Question 122
What is the primary purpose of implementing chunking strategies in RAG systems?
A) To increase storage costs
B) To divide documents into optimal segments balancing context and retrieval precision
C) To remove all content
D) To disable search functionality
Answer: B
Explanation:
Chunking strategies divide documents into segments that balance providing sufficient context for understanding while maintaining retrieval precision by avoiding overly large chunks that dilute relevance signals. Optimal chunk size depends on content type, query patterns, model context limits, and retrieval quality requirements.
Effective chunking preserves semantic coherence by avoiding splits mid-sentence or mid-paragraph, maintains context through overlapping chunks, considers document structure like sections or topics, and sizes chunks appropriate for embedding models and retrieval algorithms. Common strategies include fixed-size with overlap, sentence-based, paragraph-based, and semantic chunking using topic boundaries.
Option A is incorrect because chunking optimizes retrieval quality rather than increasing storage costs, though smaller chunks do create more index entries compared to storing whole documents.
Option C is incorrect because chunking divides content into useful segments rather than removing content, maintaining all information while improving accessibility through better-sized retrieval units.
Option D is incorrect because chunking enhances search functionality by creating appropriately-sized segments for retrieval rather than disabling search, which would eliminate core RAG system capabilities.
Chunking strategy significantly impacts RAG system performance and requires tuning based on specific content characteristics and use cases.
Question 123
Which evaluation approach helps assess generative AI model performance across multiple dimensions?
A) Single metric only
B) Multi-dimensional evaluation with multiple metrics and human assessment
C) No evaluation
D) Random assessment
Answer: B
Explanation:
Multi-dimensional evaluation assesses generative AI systems across multiple quality dimensions including relevance, accuracy, coherence, safety, groundedness, and helpfulness using combinations of automated metrics, LLM judges, and human evaluation. Comprehensive evaluation prevents optimizing for single metrics while degrading other important quality aspects.
Multi-dimensional approaches define evaluation frameworks with weighted dimensions reflecting business priorities, use diverse evaluation methods appropriate for each dimension, aggregate scores into composite quality measures, and identify trade-offs between competing objectives. This provides holistic system assessment supporting informed decisions about model selection, deployment, and improvement priorities.
Option A is incorrect because single metrics capture limited quality aspects, miss important failure modes, enable gaming through narrow optimization, and provide incomplete pictures of system performance.
Option C is incorrect because skipping evaluation prevents quality assurance, makes model comparison impossible, provides no improvement guidance, and creates risks when deploying systems without performance understanding.
Option D is incorrect because random assessment provides no meaningful signal, prevents identifying quality issues, offers no basis for improvement, and fails to ensure system reliability.
Production generative AI systems require rigorous multi-dimensional evaluation frameworks aligned with business requirements and user expectations.
Question 124
What is the primary benefit of using Delta Lake for managing generative AI training data?
A) To reduce data quality
B) To provide ACID transactions, versioning, and time travel for data management
C) To remove all data governance
D) To increase data inconsistency
Answer: B
Explanation:
Delta Lake provides ACID transactions ensuring data consistency during concurrent writes, versioning enabling time travel to previous data states, and audit trails tracking all modifications. These capabilities are essential for managing training datasets, feature engineering, and maintaining reproducibility in generative AI workflows.
Delta Lake supports schema enforcement preventing data quality issues, schema evolution adapting to changing requirements, efficient upserts and deletes for data management, and optimizations like data skipping and Z-ordering improving query performance. Time travel enables reproducing training runs with exact data versions and recovering from errors through rollback.
Option A is incorrect because Delta Lake enhances data quality through schema validation, transaction guarantees, and consistency controls rather than reducing quality through uncontrolled modifications.
Option C is incorrect because Delta Lake strengthens data governance through audit logging, access controls, versioning, and data lineage rather than removing governance capabilities.
Option D is incorrect because Delta Lake enforces consistency through ACID transactions and schema validation rather than increasing inconsistency through uncontrolled concurrent modifications.
Delta Lake provides enterprise-grade data management capabilities essential for production generative AI systems requiring reliable, reproducible data pipelines.
Question 125
Which technique helps improve the factual accuracy of generative AI responses?
A) Removing all sources
B) Retrieval-Augmented Generation with source citation
C) Increasing hallucinations
D) Disabling verification
Answer: B
Explanation:
Retrieval-Augmented Generation with source citation grounds responses in retrieved factual documents while providing citations enabling verification, significantly improving factual accuracy and allowing users to validate claims. Citations include document references, quotes, or links to source material supporting generated content.
RAG with citations retrieves relevant documents, includes them in prompts with instructions to cite sources, generates responses referencing provided materials, and formats outputs with clear attribution. This approach enables fact-checking, builds user trust through transparency, reduces hallucinations by grounding in factual content, and provides accountability for information sources.
Option A is incorrect because removing sources eliminates factual grounding, increases hallucination risk, prevents verification, and reduces trust in system outputs lacking supporting evidence.
Option C is incorrect because increasing hallucinations degrades factual accuracy rather than improving it, creating unreliable systems that generate plausible-sounding but incorrect information.
Option D is incorrect because disabling verification eliminates quality controls, prevents accuracy assessment, increases misinformation risk, and removes mechanisms for ensuring response reliability.
Citation-based RAG systems balance generative capabilities with verifiable factual grounding essential for trustworthy applications.
Question 126
What is the primary purpose of implementing monitoring and observability for production generative AI systems?
A) To hide all system behavior
B) To track performance, quality, costs, and detect issues in real-time
C) To increase system opacity
D) To remove all metrics
Answer: B
Explanation:
Monitoring and observability track key metrics including latency, throughput, error rates, cost per request, quality scores, and user satisfaction, enabling real-time issue detection, performance optimization, and data-driven decision making. Comprehensive monitoring provides visibility into system behavior supporting operational excellence.
Monitoring systems collect metrics on response quality through automated evaluation, resource utilization including token usage and API costs, user interactions through feedback and behavior analytics, and system health through error tracking and availability monitoring. Dashboards visualize trends, alerts notify teams of anomalies, and logs enable debugging issues.
Option A is incorrect because monitoring increases visibility into system behavior rather than hiding it, providing transparency essential for maintaining reliable, high-quality production services.
Option C is incorrect because observability reduces system opacity by instrumenting applications, collecting telemetry, and providing insights into internal operations rather than obscuring system behavior.
Option D is incorrect because monitoring requires defining and collecting meaningful metrics that characterize system performance, quality, and health rather than removing measurement capabilities.
Production generative AI systems require robust monitoring infrastructure for maintaining reliability, quality, and cost efficiency at scale.
Question 127
Which Databricks component facilitates serving generative AI models with REST APIs?
A) Manual server configuration only
B) Databricks Model Serving
C) Unmanaged deployment only
D) Local hosting only
Answer: B
Explanation:
Databricks Model Serving provides managed infrastructure for deploying models as REST APIs with automatic scaling, version management, A/B testing, and monitoring. Model Serving handles infrastructure provisioning, load balancing, and scaling enabling teams to focus on model development rather than deployment engineering.
Model Serving supports deploying models from MLflow registry, configuring compute resources and scaling policies, implementing canary deployments and A/B testing, monitoring endpoint performance and quality, and managing multiple model versions. Integration with Unity Catalog enables governance and access controls for deployed models.
Option A is incorrect because manual server configuration requires significant DevOps effort for infrastructure management, scaling configuration, security hardening, and monitoring setup compared to managed serving platforms.
Option C is incorrect because unmanaged deployment places full operational burden on teams including infrastructure maintenance, scaling implementation, security management, and monitoring setup.
Option D is incorrect because local hosting limits accessibility, lacks production-grade reliability and scaling, provides no managed features, and requires custom infrastructure for real-world deployment.
Managed model serving significantly reduces operational complexity and time-to-production for generative AI applications.
Question 128
What is the primary benefit of using feature stores in generative AI applications?
A) To increase feature inconsistency
B) To centralize feature definitions ensuring consistency between training and serving
C) To remove all feature engineering
D) To disable feature usage
Answer: B
Explanation:
Feature stores centralize feature definitions, transformations, and storage ensuring consistency between training and serving environments, preventing training-serving skew that degrades model performance. Feature stores enable feature reuse across projects, maintain feature versioning, and provide monitoring capabilities.
Feature stores compute features using consistent logic regardless of context, cache computed features for efficient serving, track feature lineage and metadata, enable point-in-time correct training data retrieval, and support online and offline feature access. This eliminates duplicated feature engineering code and ensures deployed models receive identical features to training.
Option A is incorrect because feature stores ensure consistency through centralized definitions and transformations rather than increasing inconsistency through fragmented feature engineering.
Option C is incorrect because feature stores systematize and centralize feature engineering rather than removing it, making feature development more efficient and reliable through reusable components.
Option D is incorrect because feature stores enhance feature usage through centralized management, versioning, and serving rather than disabling feature capabilities needed for model development.
Feature stores are essential infrastructure for maintaining reliable, consistent features across the model lifecycle.
Question 129
Which technique helps reduce latency in generative AI inference?
A) Increasing model size only
B) Model quantization, caching, and batching
C) Adding unnecessary processing
D) Removing all optimizations
Answer: B
Explanation:
Model quantization reduces computational requirements through lower precision, caching reuses results for repeated queries, and batching processes multiple requests together improving throughput and reducing per-request latency. Combining these techniques significantly improves inference performance while maintaining acceptable quality.
Quantization converts models to int8 or lower precision reducing memory bandwidth and computational requirements. Caching stores responses or intermediate results for quick retrieval. Request batching amortizes model loading and initialization costs across multiple requests. Additional optimizations include model compilation, hardware acceleration, and prompt optimization.
Option A is incorrect because increasing model size raises latency through more computations and memory transfers rather than reducing latency, though larger models may provide better quality.
Option C is incorrect because adding unnecessary processing increases latency through wasted computation rather than optimizing critical paths for performance improvement.
Option D is incorrect because removing optimizations eliminates performance improvements from techniques specifically designed to reduce latency, increasing response times.
Production systems typically implement multiple complementary latency optimization techniques based on specific requirements and constraints.
Question 130
What is the primary purpose of implementing data governance in generative AI pipelines?
A) To remove all data controls
B) To ensure data quality, lineage, access control, and compliance
C) To increase data chaos
D) To disable data management
Answer: B
Explanation:
Data governance ensures data quality through validation and monitoring, maintains data lineage tracking transformations and dependencies, enforces access controls protecting sensitive information, and ensures compliance with regulations like GDPR or HIPAA. Governance provides accountability and trust essential for production AI systems.
Governance frameworks define data ownership and stewardship, implement policies for data usage and retention, track data lineage through pipelines, enforce security and privacy controls, maintain audit logs of data access, and ensure regulatory compliance. Unity Catalog provides comprehensive governance capabilities integrated with Databricks workspaces.
Option A is incorrect because governance implements necessary controls for security, compliance, and quality rather than removing controls that protect organizations and users.
Option C is incorrect because governance reduces chaos through systematic policies, controls, and documentation rather than increasing disorder through uncontrolled data management.
Option D is incorrect because governance enables effective data management through policies, controls, and accountability rather than disabling capabilities essential for trustworthy systems.
Robust data governance is mandatory for production generative AI systems handling sensitive data or operating in regulated industries.
Question 131
Which evaluation metric is most appropriate for measuring semantic similarity between generated and reference text?
A) Character count only
B) Embedding-based cosine similarity or BERTScore
C) Random comparison
D) File size
Answer: B
Explanation:
Embedding-based metrics like cosine similarity between sentence embeddings or BERTScore using contextual embeddings capture semantic similarity better than surface-level metrics, recognizing paraphrases and synonyms that lexical overlap misses. These metrics correlate better with human judgments for tasks where meaning matters more than exact wording.
Embedding similarity computes cosine similarity between vector representations of generated and reference texts, with higher values indicating semantic alignment. BERTScore matches tokens between texts using contextual embeddings, computing precision, recall, and F1 based on semantic similarity rather than exact matches. These approaches recognize equivalent meanings expressed differently.
Option A is incorrect because character count measures text length without considering content, meaning, or semantic relationship between generated and reference texts.
Option C is incorrect because random comparison provides no meaningful quality signal, prevents identifying good or poor outputs, and offers no basis for model improvement.
Option D is incorrect because file size indicates storage requirements without measuring semantic content, accuracy, relevance, or quality of generated text.
Semantic similarity metrics are essential for evaluating generation quality in tasks like summarization, paraphrasing, or question answering.
Question 132
What is the primary benefit of using AutoML for generative AI model selection?
A) To eliminate all automation
B) To automatically explore model options, hyperparameters, and configurations
C) To remove all model choices
D) To increase manual effort
Answer: B
Explanation:
AutoML automatically explores model architectures, hyperparameters, and configurations through systematic search or optimization algorithms, identifying high-performing options without manual trial-and-error. This accelerates development, discovers non-obvious configurations, and democratizes ML by reducing expertise requirements.
AutoML frameworks define search spaces over models and hyperparameters, implement search strategies like grid search, random search, or Bayesian optimization, evaluate candidate configurations using cross-validation or holdout sets, and select best-performing options based on defined metrics. This systematic exploration often finds better configurations than manual tuning.
Option A is incorrect because AutoML increases automation for model selection and tuning rather than eliminating automation that provides significant efficiency and quality benefits.
Option C is incorrect because AutoML explores multiple model options to identify best choices rather than removing options, providing broader coverage than manual approaches.
Option D is incorrect because AutoML reduces manual effort required for model selection and tuning through automated exploration rather than increasing manual work.
AutoML is particularly valuable when exploring foundation models, embeddings, or chunking strategies for RAG systems.
Question 133
Which technique helps maintain conversation context in multi-turn generative AI interactions?
A) Treating each query independently
B) Conversation history management with context windowing
C) Discarding all previous context
D) Random memory selection
Answer: B
Explanation:
Conversation history management maintains context across turns by including relevant prior messages in prompts, enabling coherent multi-turn interactions where models reference previous statements, answer follow-up questions, and maintain consistent personas. Context windowing manages history within token limits through summarization, selection, or pruning strategies.
History management stores conversation turns with timestamps and metadata, implements strategies for selecting relevant history within context limits, maintains separate system and user message tracking, and handles context overflow through summarization or intelligent pruning. This enables natural conversations with context awareness across multiple exchanges.
Option A is incorrect because treating queries independently loses conversation continuity, prevents follow-up questions, creates repetitive interactions, and eliminates context that enables natural dialogue.
Option C is incorrect because discarding context eliminates conversation coherence, prevents reference to prior statements, requires repeating information, and degrades user experience in multi-turn interactions.
Option D is incorrect because random memory selection provides inconsistent context, may include irrelevant information while excluding important details, and creates unpredictable conversation behavior.
Effective context management is essential for conversational AI applications providing natural, coherent multi-turn interactions.
Question 134
What is the primary purpose of implementing circuit breakers in generative AI applications?
A) To ensure system failure
B) To prevent cascading failures by stopping requests to failing services
C) To remove all resilience
D) To guarantee downtime
Answer: B
Explanation:
Circuit breakers prevent cascading failures by detecting when downstream services are failing and temporarily stopping requests, allowing failed services to recover while preventing resource exhaustion from repeated failed requests. This pattern improves overall system resilience and user experience during partial outages.
Circuit breakers monitor error rates and timeouts, transition to open state blocking requests when thresholds are exceeded, periodically attempt requests in half-open state to detect recovery, and return to closed state allowing normal traffic when services recover. This prevents overwhelming failed services and provides graceful degradation.
Option A is incorrect because circuit breakers prevent system failures through resilience patterns rather than ensuring failure, protecting overall system health during component issues.
Option C is incorrect because circuit breakers add resilience through failure detection and isolation rather than removing resilience mechanisms essential for reliable distributed systems.
Option D is incorrect because circuit breakers reduce downtime impact through graceful degradation and recovery detection rather than guaranteeing downtime through poor failure handling.
Circuit breakers are essential resilience patterns for production systems depending on external APIs like foundation model providers.
Question 135
Which Databricks feature enables scheduling and orchestration of generative AI workflows?
A) Manual execution only
B) Databricks Workflows with jobs and task orchestration
C) No scheduling available
D) Random execution
Answer: B
Explanation:
Databricks Workflows provides job scheduling and task orchestration enabling automation of data pipelines, model training, evaluation, and deployment processes. Workflows supports defining multi-task jobs with dependencies, scheduling triggers, monitoring execution, and handling errors through retries and alerting.
Workflows enables creating directed acyclic graphs of tasks including notebooks, Python scripts, JAR files, or SQL queries with defined dependencies and execution order. Jobs can run on schedules, be triggered by events, or invoked through APIs. Monitoring dashboards show execution history, task durations, and failure details enabling operational excellence.
Option A is incorrect because manual execution doesn’t scale for production systems requiring regular updates, lacks reliability guarantees, provides no audit trail, and requires constant human intervention.
Option C is incorrect because Databricks provides comprehensive scheduling capabilities through Workflows enabling automated execution of data pipelines, training jobs, and deployment processes.
Option D is incorrect because random execution provides no reliability, prevents dependent tasks from completing properly, creates unpredictable behavior, and makes production operations impossible.
Workflow automation is essential for production generative AI systems requiring regular data updates, model retraining, and continuous evaluation.
Question 136
What is the primary benefit of using streaming data pipelines for generative AI applications?
A) To delay all processing
B) To enable real-time data ingestion and model updates with low latency
C) To batch process only
D) To increase data staleness
Answer: B
Explanation:
Streaming pipelines enable real-time data ingestion, processing, and model updates with low latency, supporting use cases requiring current information like real-time recommendations, fraud detection, or content moderation. Structured Streaming provides scalable stream processing with exactly-once semantics and fault tolerance.
Streaming enables continuous ingestion from sources like Kafka or event hubs, real-time feature computation and aggregation, incremental model updates with fresh data, low-latency serving with current information, and event-driven processing responding immediately to new data. This supports applications where data freshness is critical.
Option A is incorrect because streaming reduces processing delay through continuous near-real-time processing rather than delaying processing through batch intervals.
Option C is incorrect because while batch processing suits many scenarios, streaming enables use cases requiring low latency that batch processing cannot support effectively.
Option D is incorrect because streaming reduces data staleness through continuous processing rather than increasing staleness through delayed batch intervals.
Streaming architectures are essential for generative AI applications requiring real-time responsiveness to current information.
Question 137
Which technique helps ensure responsible AI practices in generative systems?
A) Ignoring all ethical considerations
B) Implementing bias detection, fairness metrics, and safety filters
C) Removing all quality controls
D) Maximizing harmful outputs
Answer: B
Explanation:
Responsible AI practices include bias detection identifying discriminatory patterns, fairness metrics measuring equitable treatment across groups, and safety filters preventing harmful outputs. These practices ensure AI systems benefit all users, avoid perpetuating biases, and operate within ethical boundaries.
Responsible AI frameworks establish principles for ethical development, implement technical controls for bias mitigation and safety, monitor systems for fairness across demographic groups, provide transparency through documentation and explainability, enable user feedback and appeal mechanisms, and ensure human oversight for high-stakes decisions. These practices build trustworthy systems.
Option A is incorrect because ignoring ethical considerations creates systems that may harm users, perpetuate discrimination, generate dangerous content, and damage organizational reputation.
Option C is incorrect because quality controls including safety checks, bias detection, and output filtering are essential components of responsible AI rather than obstacles to remove.
Option D is incorrect because maximizing harmful outputs violates fundamental ethical principles, creates liability risks, harms users, and contradicts responsible AI objectives of beneficial technology.
Responsible AI practices are mandatory for ethical deployment of generative systems with societal impact.
Question 138
What is the primary purpose of implementing A/B testing for generative AI models?
A) To avoid all experimentation
B) To compare model versions through controlled experiments measuring real user impact
C) To deploy without testing
D) To remove all metrics
Answer: B
Explanation:
A/B testing compares model versions or configurations through controlled experiments where user traffic is split between variants, measuring real-world impact on metrics like user satisfaction, engagement, or task completion. This evidence-based approach identifies improvements before full deployment and quantifies business impact.
A/B testing requires defining success metrics aligned with business goals, randomly assigning users to variants ensuring unbiased comparison, collecting sufficient data for statistical significance, analyzing results accounting for confounding factors, and deciding on deployment based on evidence. Databricks Model Serving supports A/B testing through traffic splitting.
Option A is incorrect because avoiding experimentation prevents learning, continuous improvement, and evidence-based decision making essential for optimizing generative AI systems.
Option C is incorrect because deploying without testing creates risks of degrading user experience, missing improvement opportunities, and making decisions without understanding real-world impact.
Option D is incorrect because A/B testing requires defining and measuring meaningful metrics that capture user value and business impact rather than removing measurement capabilities.
A/B testing provides rigorous methodology for making data-driven decisions about model improvements and feature releases.
Question 139
Which approach helps manage prompt complexity in generative AI applications?
A) Creating unstructured prompts
B) Using prompt templates with modular components
C) Removing all structure
D) Maximizing prompt chaos
Answer: B
Explanation:
Prompt templates with modular components separate concerns like system instructions, context formatting, query processing, and output specifications, enabling reusable components, version control, and systematic optimization. Modular design simplifies maintenance, testing, and collaboration on complex prompt engineering.
Modular prompts define templates for different components like persona instructions, retrieval context formatting, reasoning instructions, and output structure requirements. Components can be independently versioned, tested, and optimized. This separation enables A/B testing specific prompt elements, reusing components across applications, and systematic prompt improvement.
Option A is incorrect because unstructured prompts without clear organization become difficult to maintain, test, and optimize as complexity grows beyond simple applications.
Option C is incorrect because removing structure increases maintenance difficulty, prevents systematic optimization, reduces prompt clarity, and makes collaboration harder.
Option D is incorrect because chaotic prompts without organization or consistency create unpredictable behavior, complicate debugging, and prevent effective prompt engineering.
Structured, modular prompt design enables maintainable, testable, and optimizable generative AI applications.
Question 140
What is the primary benefit of implementing feedback loops in generative AI systems?
A) To ignore all user input
B) To continuously improve models through user interactions and corrections
C) To prevent all learning
D) To increase system stagnation
Answer: B
Explanation:
Feedback loops collect user interactions, ratings, corrections, and preferences enabling continuous model improvement through fine-tuning, prompt refinement, or retrieval optimization based on real usage patterns. Active learning with human feedback accelerates improvement by focusing on challenging examples.
Feedback systems capture explicit signals like ratings and corrections, implicit signals through click behavior and engagement, collect demonstrations for few-shot examples or fine-tuning, identify failure modes and edge cases, and enable human-in-the-loop refinement. This creates virtuous cycles where usage improves system quality over time.
Option A is incorrect because ignoring user input wastes valuable signals for improvement, prevents addressing user needs, and eliminates opportunities for continuous system enhancement.
Option C is incorrect because feedback loops enable learning from real usage rather than preventing learning that drives continuous improvement and adaptation to user needs.
Option D is incorrect because feedback loops drive continuous improvement and adaptation rather than causing stagnation through lack of evolution and refinement.
Feedback loops transform generative AI systems from static deployments into continuously improving solutions aligned with user needs.
