RAG Implementation Quiz

Test your mastery of production RAG deployment, LLM optimization, vector database tuning, and operational excellence for edge scenarios.

Quiz Instructions

  • Total Questions: 18
  • Passing Score: 70% (13 of 18 correct)
  • Time Estimate: 30-40 minutes
  • Format: Scenario-based multiple choice (A/B/C/D)
  • Note: Focus on production-ready edge RAG deployments with real-world constraints.

Questions

Question 1: Choosing the Right LLM for Edge

Scenario: Your organization needs to deploy RAG on Azure Local with the following constraints:

  • 2x Intel Xeon CPUs (16 cores total)
  • 64 GB RAM
  • Single Tesla T4 GPU (16GB VRAM)
  • SLA requires <200ms p95 latency
  • Multi-language support needed

Which LLM model is optimal?

A) Llama 2 70B quantized to INT4 B) Mistral 7B quantized to INT8 C) Phi-3 14B quantized to INT4 D) GPT-4 via cloud API

Correct Answer: B

Explanation:

  • Llama 70B INT4 needs ~26GB, exceeds GPU VRAM (incorrect)
  • Mistral 7B INT8 needs ~8GB VRAM, leaves headroom for batch processing
  • Provides <200ms latency with single T4 GPU
  • Supports 84 languages
  • Phi-3 14B INT4 (~5GB) would also work but Mistral has better quality/latency trade-off
  • Cloud API violates edge/sovereignty requirement

Question 2: Vector Database Selection for Multi-Tenant

Scenario: You’re designing a multi-tenant edge RAG system with:

  • 10 tenants, each with 100K vectors
  • Need strict data isolation
  • Real-time search requirements (<50ms)
  • Limited GPU on edge device
  • Cost-sensitive environment

Which vector database architecture?

A) Single Weaviate cluster with namespace isolation B) Qdrant with collection-based multi-tenancy C) Separate Milvus instances per tenant (containerized) D) Chroma in-memory database per tenant

Correct Answer: C

Explanation:

  • Single Weaviate cluster has shared resource contention, violates SLA (incorrect)
  • Qdrant collections share memory pool, possible performance impact
  • Separate Milvus instances provide complete isolation and true multi-tenancy
  • Containerized allows efficient resource packing
  • Chroma in-memory would exceed RAM with 1M vectors
  • Cost-efficient when shared across multiple containers
  • Meets <50ms latency with proper indexing (HNSW)

Question 3: Quantization Strategy Trade-offs

Scenario: Your LLM inference is at 89% GPU utilization with p95 latency of 450ms. You need to support more concurrent users while keeping latency <300ms.

Current setup: Llama 2 7B in FP16 (7GB VRAM)

Which quantization approach?

A) Switch to INT4 and increase batch size to 8 B) Switch to INT8 and keep batch size at 4 C) Use dynamic quantization only during peak load D) Implement model splitting across CPU/GPU

Correct Answer: A

Explanation:

  • INT4 reduces VRAM to ~2GB, saving 5GB for batching
  • Batch size 8 improves throughput 3-4x without latency increase
  • INT4 has minimal quality loss (<2%) for most queries
  • INT8 only saves 3.5GB, less aggressive optimization
  • Dynamic quantization adds overhead during transitions
  • Model splitting increases latency (CPU communication)
  • Result: 450ms → 250-280ms latency, 4x throughput improvement

Question 4: Vector Search Optimization

Scenario: Your vector search has the following profile:

  • Database: 500K vectors (384-dim embeddings)
  • Current latency: 150ms p95
  • Recall: 91% (target 95%)
  • GPU memory: 32GB, currently 18GB used

You need to improve recall to 95% without exceeding 175ms latency.

Which optimization?

A) Increase HNSW m parameter from 12 to 24 B) Increase HNSW efSearch from 200 to 400 C) Switch from HNSW to IVF with 256 clusters D) Increase embedding dimension to 768

Correct Answer: B

Explanation:

  • Increasing m (12→24) requires data restructuring, adds >100ms
  • Increasing efSearch (200→400) adds only ~20-30ms
  • Improves recall from 91% to 96% with efSearch 400
  • Final latency: 150ms + 20ms = 170ms (meets 175ms target)
  • IVF with 256 clusters would actually decrease recall to 87%
  • Higher dimensions increase memory and latency (incorrect)
  • efSearch is the tuning knob for recall/latency trade-off

Question 5: Embedding Model Selection

Scenario: Your edge RAG system serves customers with:

  • 60% English queries, 20% French, 15% German, 5% Spanish
  • Embedding inference must be <50ms for real-time search
  • Limited GPU on edge device
  • Need 512-dim embeddings for vector DB

Which embedding model?

A) all-MiniLM-L6-v2 (384-dim, 22M params, 5ms) B) multilingual-e5-base (768-dim, 110M params, 15ms) C) bge-base-en (768-dim, 109M params, 12ms) D) OpenAI text-embedding-3-small via API

Correct Answer: A

Explanation:

  • all-MiniLM-L6-v2 covers 100+ languages despite English-focused name
  • 5ms inference fits <50ms SLA budget with headroom
  • 384-dim is sufficient (studies show minimal quality loss vs 768-dim)
  • multilingual-e5-base slower (15ms) and higher dimension
  • bge-base-en is English-only (incorrect for multilingual)
  • API violates edge/sovereignty requirements
  • Total flow: query→embedding (5ms) + search (40ms) + reserve (5ms) = 50ms

Question 6: Deployment Strategy Selection

Scenario: Your organization has:

  • 3 edge locations (factory floors in Germany, France, Italy)
  • Each location has local data (100K documents)
  • European data residency requirement
  • Network between locations: <50ms latency
  • Recovery target: RPO <1 hour, RTO <5 minutes

Which deployment strategy?

A) Single centralized RAG in Germany with replication to other sites B) Active-active deployment in all 3 locations with async replication C) Active in 2 locations, standby in 1 with multi-master replication D) Active-passive with all 3 locations syncing to cloud backup

Correct Answer: B

Explanation:

  • Single centralized violates data residency (incorrect)
  • Active-active in all 3 maintains local processing, fast failover
  • Each location serves local queries (<10ms latency improvement)
  • Async replication meets <1 hour RPO
  • Multi-region failover achieves <5 minute RTO
  • No single point of failure
  • Network latency <50ms allows eventual consistency
  • Standby model would require failover orchestration (longer RTO)
  • Cloud backup violates sovereignty requirement

Question 7: CI/CD Pipeline for Model Updates

Scenario: You need to update the LLM from Llama 2 7B to Mistral 7B with zero downtime.

Current setup:

  • 3 LLM service replicas running Llama 2 7B
  • Single vector database (no changes needed)
  • Kubernetes environment
  • Average query load: 100 queries/second

Which deployment strategy and tooling?

A) Kubernetes rolling update with 1 replica surge B) Blue-green deployment with traffic switch after validation C) Canary: 10% traffic to Mistral, monitor, increment to 100% D) Shadow deployment: run Mistral in parallel for 24 hours

Correct Answer: C

Explanation:

  • Rolling update with surge could impact latency during transition
  • Blue-green requires capacity for 2 full replica sets (wasteful)
  • Canary (10%→25%→50%→100%) allows incremental validation
  • At 10%: 10 queries/sec to Mistral, catches anomalies early
  • If error rate increases >1%, automatic rollback
  • Reduces blast radius and validates quality before full deployment
  • Shadow deployment adds cost without catching customer impact
  • Final approach: kubectl traffic split 10% to new, monitor 30min, increment

Question 8: Batch Processing Optimization

Scenario: Your RAG system has a bulk processing workload:

  • Process 10,000 customer support tickets
  • Each needs embedding generation + retrieval + LLM response
  • Window: Complete within 4 hours
  • Available resources: 1x T4 GPU, 64GB RAM

Current approach (1 query at a time):

  • Embedding: 5ms
  • Search: 40ms
  • LLM: 350ms
  • Total per query: 395ms
  • 10K queries: 4,283 seconds ≈ 71 minutes

How to optimize for 4-hour window while reducing GPU cost?

A) Increase batch size to 256 queries B) Batch embeddings (256), sequential LLM (1), search in parallel C) Move to CPU-only mode with batch size 32 D) Switch to streaming API with async processing

Correct Answer: B

Explanation:

  • Batch embeddings (256): 5ms → 2ms per query (2.5x speedup)
  • Keep LLM batch size 1 (quality + latency sensitive)
  • Parallel vector searches (no contention)
  • Optimized flow:
    • Batch 256: Generate embeddings in ~2ms per query
    • Parallel: Search all 256 (~40ms total, GPU idle)
    • Sequential: LLM responses (350ms × 256 = 90s per batch)
    • Rate: 256 queries / 92ms = 2,800 queries/hour
    • Total: 10K queries / 2,800 = 3.6 hours ✓
  • Pure CPU (C) would take 8+ hours
  • Batch 256 LLM would use 256 × 7GB = not enough memory
  • API streaming adds cost and latency

Question 9: Monitoring & Alerting

Scenario: Your production RAG system has these metrics:

Metric Threshold Current
Query latency p95 <200ms 185ms
Error rate <1% 0.8%
Vector recall >92% 94%
Hallucination rate <3% 2.1%
GPU utilization 60-85% 92%
Data freshness <24h 3h

Which metric should trigger an alert immediately?

A) GPU utilization 92% B) Vector recall 94% C) Data freshness 3h D) Error rate trending toward 2%

Correct Answer: A

Explanation:

  • GPU utilization 92% indicates capacity constraint approaching
  • Trending toward overload (threshold violation imminent)
  • Requires immediate action: scale up or optimize
  • Other metrics are healthy and not trending negatively:
    • Query latency 185ms has 15ms buffer to threshold
    • Error rate 0.8% has buffer, not trending
    • Recall and hallucination are excellent
    • Data freshness is optimal
  • Alert strategy: focus on resource constraints and SLA risks
  • GPU 92% → next spike could exceed latency SLA

Question 10: Root Cause Analysis - High Latency

Scenario: Production incident: Query latency jumped from 200ms avg to 600ms avg in 2 minutes.

Symptoms observed:

  • Error rate: unchanged (0.8%)
  • Vector search latency: unchanged (45ms)
  • LLM generation time: increased from 150ms to 450ms
  • GPU memory: 92% utilization (up from 75%)
  • GPU compute: 85% utilization (normal)
  • New LLM model deployed 5 minutes ago

What’s the likely cause?

A) Vector database running out of memory B) New LLM model is slower/larger than previous C) Network congestion between services D) Vector search parameters need tuning

Correct Answer: B

Explanation:

  • LLM generation time tripled (150ms→450ms), points to model issue
  • GPU memory up (suggests larger model loaded)
  • Vector search unchanged (rules out retrieval bottleneck)
  • Error rate unchanged (no hardware failure)
  • Timing: incident 5 minutes after new model deployment
  • LLM latency breakdown: likely model inference slower
  • Root cause: new model either larger (INT8 instead of INT4) or less optimized
  • Mitigation: rollback to previous model or check model quantization
  • Network/vector DB would show different symptoms

Question 11: Multi-Language RAG Challenges

Scenario: Your RAG system needs to handle:

  • Queries in 5 languages (English, German, French, Italian, Spanish)
  • Documents in same 5 languages
  • Users expect response in same language as query
  • System has performance constraints (edge device)

Which approach handles language switching?

A) Single multilingual embedding model for all languages B) Language detection → language-specific embedding models C) Translate all documents to English → single English model D) Deploy 5 separate RAG systems (one per language)

Correct Answer: A

Explanation:

  • Single multilingual embedding model (e.g., multilingual-e5-base):
    • All 5 languages in same vector space
    • Query in German matches documents in German or other languages
    • LLM generates response in same language as query
    • Minimal overhead (no language detection needed)
  • Language-specific models (B) would require detecting language + loading right model (complexity)
  • Translation to English (C) adds 500-1000ms latency per query
  • 5 separate systems (D) multiplies storage/compute (wasteful)
  • Multilingual models designed for cross-lingual retrieval
  • LLM handles response translation naturally via context

Question 12: Cost Optimization for Edge RAG

Scenario: Your organization is deciding between edge vs cloud for RAG:

Factor Edge Cloud API
Hardware $10K one-time -
Operation $200/month -
LLM inference unlimited $0.001/100 tokens
Query volume 1M queries/month 1M queries/month
Response tokens avg 150 150

Cloud API annual cost:

  • 1M queries × 150 tokens × $0.001/100 = $1,500/month = $18K/year

Which scenario justifies edge deployment?

A) 3-year outlook: Edge wins immediately B) 5-year outlook: Edge TCO lower despite upfront cost C) Only if compliance/sovereignty requirements exist D) When latency <100ms is required

Correct Answer: B

Explanation:

  • 3-year cost comparison:
    • Edge: $10K + ($200 × 36 months) = $17.2K
    • Cloud: $18K/year × 3 = $54K
    • Edge saves: $36.8K
  • 5-year comparison:
    • Edge: $10K + ($200 × 60) = $22K
    • Cloud: $18K × 5 = $90K
    • Edge saves: $68K
  • Break-even: ~8 months for hardware payback
  • Cloud API cost scales linearly with volume
  • Sovereignty requirement strengthens case but ROI justifies alone
  • Latency (D) is benefit but doesn’t affect cost calculation
  • Edge wins on 3-year and 5-year TCO

Question 13: Hallucination Detection & Mitigation

Scenario: You’re seeing 5.2% hallucination rate in generated responses. Target is <2%.

Observations:

  • Vector retrieval recall: 94% (excellent)
  • Query relevance matching: 92%
  • LLM model: Mistral 7B INT8
  • Temperature: 0.7 (creative)
  • Context window: last 3 documents

Which is most likely cause?

A) Vector database recall too low B) LLM temperature too high, generating creatively C) Need larger model (Mistral 13B) D) Embedding model not capturing nuance

Correct Answer: B

Explanation:

  • Recall 94% means documents found are relevant (not the issue)
  • Temperature 0.7 encourages model creativity → more hallucinations
  • Lower temperature (0.3-0.5) for factual responses
  • Context window sufficient (3 documents capture key info)
  • Mistral 7B is appropriate size (model size less relevant than temperature)
  • Embeddings capturing nuance (92% relevance matching confirms)
  • Mitigation steps:
    1. Reduce temperature from 0.7 to 0.4 (priority)
    2. Add constraint: “If not in context, say ‘Not found’”
    3. Extend context to 5 documents
    4. Result: hallucination rate drops to <2%

Question 14: Failover & Disaster Recovery

Scenario: Your active-active RAG deployment (2 regions) experiences:

  • Region 1 LLM service crashes (pod OOM)
  • Vector database still healthy in both regions
  • User traffic: 60% in Region 1, 40% in Region 2

What happens and recovery time?

A) Region 1 users immediately rerouted to Region 2 (cross-region latency +50ms) B) Partial failure: 60% of Region 1 users hit error, others succeed C) LLM service auto-scales to 2 replicas in Region 2 D) Kubernetes rescheduling finds capacity in Region 2 (2-5 min)

Correct Answer: D

Explanation:

  • Pod OOM in Region 1 triggers Kubernetes eviction
  • Pending pod cannot be scheduled in Region 1 (no capacity)
  • Kubernetes looks for capacity in Region 2
  • Pod rescheduled in Region 2 (typical: 2-5 minutes)
  • Meanwhile:
    • Active pods in Region 1 continue serving (others still healthy)
    • New requests to Region 1 rerouted to Region 2 by load balancer
    • Cross-region latency: +50ms but system operational
  • Key insight: stateless LLM service means easy failover
  • If stateful, recovery would take longer
  • Immediate reroute (A) would only happen with explicit failover
  • Partial errors (B) depends on which pod crashed
  • Option C assumes reserved capacity (incorrect)

Question 15: Performance Bottleneck Investigation

Scenario: Your RAG p99 latency is 1.2 seconds. p95 is 350ms (acceptable).

Breakdown analysis shows:

  • Vector search: 45ms (consistent)
  • LLM generation: 200ms (consistent)
  • Embedding generation: 8ms (consistent)
  • Other: 97ms (consistent)
  • Total: 350ms typical

But p99 queries show:

  • Vector search: 300ms (outlier, 6x slower)
  • LLM generation: 150ms (normal)
  • Other: 100ms
  • Total: 550ms

What’s causing p99 latency outliers?

A) Vector database experiencing memory pressure during peak queries B) Network packet loss causing timeout/retry C) CPU thermal throttling during high utilization D) Query complexity variation (some queries require deeper index search)

Correct Answer: D

Explanation:

  • Vector search varies from 45ms (median) to 300ms (p99) shows query-dependent behavior
  • NOT consistent hardware issue (A, B, C show consistent other metrics)
  • Complex queries might traverse more HNSW graph layers
  • Example: search for rare concept requires deeper graph traversal
  • Solution: analyze p99 queries for patterns
    • Do they have many semantic variations?
    • Are they about specific domains with sparser vector clusters?
  • Mitigation: adjust HNSW efSearch dynamically based on query complexity
  • Memory pressure (A) would show in all metrics
  • Network loss (B) would show in all service calls
  • Thermal throttling (C) would affect all components equally

Question 16: Security in RAG Deployments

Scenario: Your edge RAG system needs to prevent:

  • Prompt injection attacks
  • Data exfiltration via model responses
  • Unauthorized vector database access
  • Audit trail for compliance

Which security layers?

A) Network isolation + API authentication + rate limiting B) API authentication + prompt validation + data classification + audit logging C) Encryption at rest + network isolation + RBAC D) All of the above

Correct Answer: D

Explanation:

  • Network isolation: prevents external access
  • API authentication: validates caller identity
  • Rate limiting: prevents brute force
  • Prompt validation: sanitizes user input (prevents injection)
  • Data classification: marks sensitive data in embeddings
  • Audit logging: tracks all queries/responses for compliance
  • Encryption at rest: protects stored data
  • RBAC: role-based access control for vector DB
  • Each layer addresses different threat:
    • Injection → prompt validation (B component)
    • Exfiltration → data classification + response filtering (B component)
    • Unauthorized access → RBAC + authentication (A, C components)
    • Compliance → audit logging (B component)
  • Comprehensive security requires all layers

Question 17: Scalability Planning

Scenario: Current state:

  • 1M vectors in database
  • 100 queries/second average
  • p95 latency: 200ms
  • Growing 30% monthly

At what point (considering vector count + QPS) should you consider regional sharding?

A) 50M vectors or 500 QPS (approximately 6 months) B) 100M vectors or 1000 QPS (approximately 8 months) C) 200M vectors or 2000 QPS (approximately 10 months) D) Sharding not needed for edge deployments

Correct Answer: A

Explanation:

  • Current: 1M vectors, 100 QPS, 200ms latency (acceptable)
  • At 30% monthly growth:
    • Month 6: 4.8M vectors, 480 QPS
    • Month 7: 6.2M vectors, 624 QPS
    • Month 8: 8M vectors, 810 QPS
  • Scaling limits (single node):
    • Vector DB: ~50M vectors with HNSW (memory + latency)
    • LLM: ~500 QPS (5x throughput with proper optimization)
    • When either limit approached, latency degrades
  • Regional sharding strategy (Month 6):
    • Split vectors by geography/topic
    • Route queries to appropriate shard
    • Maintain <200ms latency SLA
  • Monitor: latency trends, GPU/CPU utilization
  • Alert: when p95 > 250ms or resource > 80%
  • Option B too late (1000 QPS would exceed infrastructure)

Question 18: Technology Selection for RAG Evolution

Scenario: Your organization is evaluating next-generation RAG improvements:

Tech Benefit Cost Complexity
Sparse-dense hybrid retrieval +5% recall +40% compute High
Reranking (smaller model) +3% recall +50ms latency Medium
Multi-hop retrieval Better context +100ms latency High
Query expansion +2% recall +30ms latency Low

Which should you prioritize given edge constraints?

A) All of them for maximum accuracy B) Query expansion (best ROI) C) Reranking (strong recall improvement) D) Skip improvements, maintain current performance

Correct Answer: B

Explanation:

  • Edge constraints: limited compute, latency-sensitive (<200ms p95)
  • Query expansion: +2% recall, +30ms latency, low complexity
    • 200ms p95 → 230ms (still acceptable)
    • ROI: highest impact-per-complexity ratio
  • Reranking: +3% recall but +50ms latency
    • 200ms p95 → 250ms (risks SLA)
    • Need to justify latency trade-off
  • Multi-hop: +100ms latency unacceptable on edge
  • Sparse-dense: +40% compute too costly
  • Prioritization:
    1. Query expansion (Month 1)
    2. Test reranking (Month 2, if latency budget available)
    3. Evaluate others for future versions
  • Best practice: incremental improvements validated for edge constraints

Answer Key Summary

Q Answer Topic
1 B LLM Model Selection
2 C Vector Database Architecture
3 A Quantization Optimization
4 B Vector Search Tuning
5 A Embedding Model Choice
6 B Deployment Strategy
7 C CI/CD Patterns
8 B Batch Optimization
9 A Monitoring & Alerting
10 B Root Cause Analysis
11 A Multilingual Handling
12 B Cost Optimization
13 B Hallucination Mitigation
14 D Disaster Recovery
15 D Bottleneck Analysis
16 D Security Architecture
17 A Scalability Planning
18 B Technology Prioritization

Scoring Guide

Calculate your score:

  • Count the number of correct answers
  • Divide by 18 and multiply by 100 for percentage

Score Interpretation:

18 correct (100%): 🏆 Master of Edge RAG Implementation

  • Perfect score! You have mastery-level RAG implementation expertise
  • Ready to architect and deploy production edge RAG systems
  • Qualified for principal AI engineer or solutions architect roles
  • Consider mentoring other engineers and creating best practice guides

16-17 correct (89-94%): ⭐ Expert RAG Practitioner

  • Excellent understanding of production RAG systems
  • Ready for complex edge AI deployments
  • Minor review recommended on missed topics
  • Consider advanced AI/ML certifications

14-15 correct (78-83%): ✅ Proficient Professional

  • Strong understanding of RAG implementation
  • Ready for production deployments with guidance
  • Review optimization and troubleshooting areas
  • Focus on performance tuning techniques

13 correct (72%): ✅ Ready for Production - Passing

  • PASSING - Ready for production implementation work
  • Solid foundational understanding
  • Review areas where you missed questions
  • Focus on monitoring and optimization strategies

11-12 correct (61-67%): ⚠️ Review Needed

  • Foundational understanding but gaps exist
  • Additional review needed before production deployment
  • Focus on LLM optimization and vector search tuning
  • Practice with hands-on RAG projects
  • Retake quiz after comprehensive review

Below 11 correct (<61%): ❌ Strong Review Recommended

  • Significant gaps in RAG implementation knowledge
  • Strong review recommended before production work
  • Study all module content thoroughly
  • Focus on fundamentals: LLM selection, vector databases, deployment patterns
  • Build hands-on experience with RAG systems
  • Retake quiz only after thorough study and practice

Study Recommendations by Topic

If you missed questions on LLM Optimization (Q1, Q3, Q8, Q13):

  • Review LLM Inference Optimization
  • Study quantization techniques (INT4, INT8)
  • Focus on batch optimization strategies
  • Review hallucination mitigation techniques

If you missed questions on Vector Databases (Q2, Q4, Q15):

  • Review Edge RAG Implementation
  • Study vector database architectures and indexing
  • Focus on HNSW parameter tuning (m, efSearch)
  • Review performance bottleneck analysis

If you missed questions on Embedding Models (Q5, Q11):

  • Study embedding model selection criteria
  • Focus on multilingual support and cross-lingual embeddings
  • Review model size vs quality trade-offs

If you missed questions on Deployment (Q6, Q7, Q14):

  • Review RAG Deployment Strategies
  • Study blue-green vs canary deployment patterns
  • Focus on CI/CD pipelines for ML models
  • Review disaster recovery strategies

If you missed questions on Operations (Q9, Q10, Q12, Q16, Q17):

  • Review RAG Operations & Monitoring
  • Study monitoring and alerting strategies
  • Focus on root cause analysis techniques
  • Review cost optimization and scalability planning
  • Study security architecture patterns

If you missed questions on Optimization (Q18):

  • Review technology prioritization frameworks
  • Study ROI analysis for RAG improvements
  • Focus on edge constraint management

Next Steps

After completing this assessment:

  1. ✅ Congratulations! You’re ready for production edge RAG implementation.

  2. 📚 Apply your knowledge:
    • Deploy a production RAG system
    • Implement monitoring and alerting
    • Practice LLM optimization techniques
    • Build CI/CD pipelines for ML models
  3. 🔗 Review related content:
  4. 🌐 Explore external resources:
  5. 💡 Consider hands-on practice:
    • Build end-to-end RAG system
    • Experiment with different LLM models and quantization
    • Tune vector database parameters
    • Implement production monitoring
    • Practice disaster recovery scenarios
    • Optimize for edge constraints

Key RAG Implementation Concepts

LLM Selection & Optimization:

  • Match model size to hardware constraints (VRAM)
  • Use quantization (INT4/INT8) for edge deployment
  • Optimize batch size for throughput vs latency
  • Monitor hallucinations and implement guardrails

Vector Database Mastery:

  • Choose architecture based on tenancy requirements
  • Tune HNSW parameters for recall/latency balance
  • Monitor and optimize query performance
  • Plan for scalability and growth

Production Operations:

  • Implement comprehensive monitoring (latency, quality, cost)
  • Set up alerting for anomalies and failures
  • Plan blue-green or canary deployments
  • Design disaster recovery strategies
  • Optimize costs continuously

Edge Constraints:

  • Limited compute and memory
  • Latency sensitivity (<200ms typical)
  • Offline operation requirements
  • Cost optimization critical

Quiz Version: 1.0
Last Updated: October 2025
Questions: 18
Passing Score: 70% (13 of 18 correct)

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