Edge RAG Production - Knowledge Check

Test your expertise in production Edge RAG deployment, MLOps workflows, performance optimization, and enterprise operations.

Quiz Instructions

Total Questions: 18
Passing Score: 14/18 (78%)
Time Estimate: 30-40 minutes
Format: Expert-level scenario-based questions

This assessment covers:

Production RAG architecture and deployment patterns
MLOps workflows for edge AI models
Performance optimization techniques
Knowledge base management at scale
Enterprise operations and SLA management

Question 1: Production RAG Architecture

Designing production RAG for financial services with strict latency requirements (< 500ms). What architecture provides BEST balance of performance and cost?

A) Cloud-only RAG with GPU instances
B) Hybrid: Edge inference + cloud knowledge base
C) Full edge deployment: Local inference + local vector database
D) Edge inference with on-demand cloud retrieval

Click to reveal answer

Correct Answer: C

Explanation: Full edge deployment meets latency requirements and regulatory needs:

Architecture Components:

1. Edge Inference Engine:

✅ Local LLM (quantized for edge)
✅ < 100ms inference time
✅ No cloud dependency
✅ Data sovereignty maintained

2. Local Vector Database:

✅ Embeddings stored locally
✅ < 50ms retrieval time
✅ No network latency
✅ Works in disconnected scenarios

3. Knowledge Base:

✅ Financial documents locally indexed
✅ Regular updates via batch sync
✅ Version control
✅ Audit trail maintained

Latency Breakdown:

Retrieval: 30-50ms (local vector DB)
Inference: 80-120ms (quantized model)
Processing: 20-30ms (app logic)
Total: 130-200ms ✅ (well under 500ms target)

Why NOT Others:

A: Cloud latency variable; data sovereignty issues
B: Retrieval latency adds 50-150ms; sovereignty risk
D: On-demand cloud calls add unpredictable latency

Reference: Production Architecture

Question 2: Model Selection for Edge

Choosing LLM for edge RAG with 64GB RAM constraint. Which model type is MOST appropriate?

A) Full-precision 70B parameter model
B) INT8 quantized 13B parameter model
C) INT4 quantized 7B parameter model
D) Cloud API calls (no local model)

Click to reveal answer

Correct Answer: B

Explanation: INT8 quantized 13B model provides best quality/resource balance:

Model Sizing Analysis:

Full-Precision 70B (Option A):

Memory: 140GB+ (FP16) ❌
Cannot fit in 64GB constraint

INT8 Quantized 13B (Option B):

Memory: ~13-16GB ✅
Quality: Minimal degradation (< 3%)
Inference: 80-150ms
Supports 4-8 concurrent requests

INT4 Quantized 7B (Option C):

Memory: ~4-7GB ✅
Quality: Moderate degradation (5-10%)
Inference: 50-100ms
May lack reasoning capability

Cloud API (Option D):

Memory: Minimal
Latency: High and variable
Data sovereignty violated

Recommendation: INT8 13B

Optimal quality for RAG tasks
Fits constraint with room for vector DB
Production-grade performance
Good cost/performance ratio

Memory Budget:

Model: 16GB
Vector DB: 8GB
OS + Apps: 12GB
Buffer: 28GB
Total: 64GB ✅

Reference: Performance Optimization

Question 3: Vector Database Selection

Selecting vector database for 10M document enterprise knowledge base. What is the PRIMARY consideration?

A) Maximum query throughput
B) Balance of retrieval accuracy, latency, and memory footprint
C) Lowest cost per query
D) Easiest to configure

Click to reveal answer

Correct Answer: B

Explanation: Balanced trade-offs are critical for production success:

Key Evaluation Criteria:

1. Retrieval Accuracy:

✅ Recall@10: > 95% (find relevant docs)
✅ Precision: > 90% (avoid false positives)
✅ Supports approximate nearest neighbor (ANN)

2. Query Latency:

✅ P50: < 30ms
✅ P99: < 100ms
✅ Consistent performance under load

3. Memory Footprint:

✅ 10M vectors × 1536 dimensions × 4 bytes = 60GB base
✅ Index overhead: 1.2-2x multiplier
✅ Total: 72-120GB depending on algorithm

4. Update Performance:

✅ Incremental updates supported
✅ No full reindex for new documents
✅ < 100ms per document ingestion

Vector DB Options:

FAISS (Facebook AI):

Accuracy: Excellent (configurable)
Latency: 20-50ms
Memory: Efficient with compression
✅ Good balance for edge

Milvus:

Accuracy: Excellent
Latency: 30-80ms
Memory: Higher overhead
Better for distributed/cloud

Pinecone/Weaviate:

Cloud-native; not ideal for edge

Why NOT Others:

A: Throughput important but accuracy is critical
C: Cost secondary to performance/accuracy in production
D: Configuration ease tertiary concern

Reference: Production Architecture

Question 4: RAG Retrieval Strategy

Production RAG returns irrelevant results 15% of the time. What is the FIRST optimization to try?

A) Increase number of retrieved chunks (k) from 5 to 20
B) Implement hybrid retrieval: Dense vectors + keyword search
C) Switch to larger embedding model
D) Add reranking model after initial retrieval

Click to reveal answer

Correct Answer: D

Explanation: Reranking is the most effective single improvement for retrieval quality:

Retrieval Pipeline with Reranking:

Stage 1: Initial Retrieval (Fast & Broad)

Retrieve k=20 documents (over-retrieve)
Vector similarity search
Latency: 30-50ms

Stage 2: Reranking (Accurate & Focused)

Apply cross-encoder reranker model
Compare query to each retrieved doc
Score and re-rank
Select top 5 for LLM context
Latency: 50-100ms

Stage 3: LLM Generation

Use top 5 reranked documents
Generate response
Latency: 80-150ms

Why Reranking Works:

✅ Dense vectors miss semantic nuance: Reranker adds precision
✅ Cross-encoder models more accurate: See full query-doc interaction
✅ Reduces false positives dramatically: 15% → 3-5% error rate
✅ Latency acceptable: 50-100ms overhead worth accuracy gain

Reranker Options:

ms-marco-MiniLM-L-12-v2 (Fast, 12-layer)
cross-encoder/ms-marco-TinyBERT (Faster, less accurate)
Custom fine-tuned on domain data (Best)

Why NOT Others:

A: More chunks = more noise; makes problem worse
B: Hybrid helps but reranking more impactful as first step
C: Larger embedding expensive; marginal gain

Reference: Performance Optimization

Question 5: Knowledge Base Chunking Strategy

Ingesting technical documentation with complex tables and diagrams. What chunking strategy is BEST?

A) Fixed 512-token chunks with 50-token overlap
B) Semantic chunking based on document structure (sections, tables)
C) Sentence-level chunks (one sentence per chunk)
D) Full document as single chunk

Click to reveal answer

Correct Answer: B

Explanation: Semantic/structural chunking preserves context and meaning:

Chunking Strategy:

1. Respect Document Structure:

✅ Sections/headings as natural boundaries
✅ Tables kept intact (don’t split mid-table)
✅ Code blocks preserved
✅ Lists maintained

2. Target Chunk Size:

256-512 tokens (LLM context window consideration)
Flexible based on content boundaries
Overlap: 10-20% with adjacent chunks

3. Metadata Enrichment:

✅ Document title
✅ Section heading
✅ Page number
✅ Content type (text, table, code)
✅ Last updated date

Example: Technical Doc

Chunk 1: Section "Architecture Overview"
  - Full section text
  - Includes architecture diagram reference
  - Metadata: {section: "Architecture", type: "overview"}

Chunk 2: Section "Component Details" + Table
  - Section text + full component table
  - Table NOT split across chunks
  - Metadata: {section: "Components", type: "table"}

Why NOT Others:

A: Fixed chunks split tables, code blocks; loses context
C: Sentence-level too granular; loses paragraph context
D: Full doc too large; LLM cannot focus on relevant part

Benefits:

Better retrieval relevance
Preserved context
Easier to cite sources
Better LLM reasoning

Reference: Knowledge Base Management

Question 6: MLOps - Model Deployment Strategy

Deploying new RAG model version. What deployment pattern minimizes risk?

A) Replace all edge instances simultaneously
B) Blue-green deployment with instant cutover
C) Canary deployment: 5% → 25% → 100% with validation gates
D) A/B testing with random 50/50 split

Click to reveal answer

Correct Answer: C

Explanation: Canary deployment with gates provides safest rollout with early issue detection:

Canary Deployment Process:

Phase 1: Canary (5% - 2 hours)

✅ Deploy to 5% of edge nodes
✅ Monitor key metrics:
- Response quality (human eval sample)
- Latency (P50, P99)
- Error rate
- Resource utilization
✅ Gate: Manual approval after 2 hours if green

Phase 2: Expanded Canary (25% - 24 hours)

Deploy to 25% of nodes
Monitor same metrics at scale
Check for edge cases/rare issues
Gate: Auto-proceed if metrics within 5% of baseline

Phase 3: Full Rollout (100% - 48 hours)

Deploy to remaining 75%
Continue monitoring
Rollback plan ready

Validation Gates:

Gate Criteria:
- Error rate < 1%
- P99 latency < baseline + 20%
- Quality score > 95% of baseline
- No critical issues reported

Rollback Procedure:

Keep previous model version
One-command rollback
< 5 minute rollback time

Why NOT Others:

A: All-at-once risks widespread outage if issues
B: Blue-green good but canary provides more gradual validation
D: A/B testing for experimentation, not deployment

Reference: MLOps Workflows

Question 7: Model Monitoring - Data Drift Detection

Detecting when RAG model needs retraining. Which metric is the PRIMARY indicator of data drift?

A) Inference latency increasing
B) User query distribution shifting significantly from training data
C) Memory usage increasing
D) Error logs growing

Click to reveal answer

Correct Answer: B

Explanation: Query distribution shift indicates model sees data it wasn’t trained for:

Data Drift Detection:

1. Query Embedding Drift:

✅ Cluster user queries in embedding space
✅ Compare to training data distribution
✅ Measure: KL divergence, Wasserstein distance
✅ Threshold: > 0.3 KL divergence = significant drift

2. Topic Drift:

Track query topics over time
New topics emerging
Old topics declining
Example: Sudden surge in regulatory questions after law change

3. Vocabulary Drift:

New terminology appearing
Out-of-vocabulary rate increasing

Monitoring Approach:

# Collect query embeddings
current_embeddings = embed(queries_last_week)
training_embeddings = load(training_set_embeddings)

# Calculate drift
drift_score = kl_divergence(current_embeddings, training_embeddings)

if drift_score > 0.3:
    alert("Significant data drift detected")
    trigger_retraining_evaluation()

Retraining Triggers:

Drift score > threshold
User satisfaction < 85%
Manual reports of poor quality
New data domain added

Why NOT Others:

A: Latency drift indicates system issues, not data drift
C: Memory usage not related to data distribution
D: Errors indicate bugs, not drift

Reference: MLOps Workflows

Question 8: Knowledge Base Update Strategy

Knowledge base has 10M documents. 50K documents updated monthly. What is the MOST efficient update approach?

A) Full reindex of entire 10M documents
B) Incremental update: Re-embed and re-index only changed documents
C) Create separate index for new docs; query both
D) Manual merge of vectors

Click to reveal answer

Correct Answer: B

Explanation: Incremental updates provide efficiency and minimal disruption:

Incremental Update Process:

1. Identify Changed Documents:

✅ Track document modification timestamps
✅ Checksum/hash comparison
✅ 50K changed docs out of 10M = 0.5%

2. Re-embed Changed Documents:

Run embedding model on 50K docs
Generate new vectors
Time: ~1-2 hours (depending on hardware)

3. Update Vector Index:

Modern vector DBs support incremental updates
Delete old vectors for changed docs
Insert new vectors
Update index structure
Time: ~15-30 minutes

4. Validate:

Spot-check retrieval for updated docs
Ensure no index corruption
Monitor query performance

Total Time: 2-3 hours (vs. 24+ hours for full reindex)

Why NOT Others:

A: Full reindex wasteful; 9.95M docs unchanged
C: Multiple indexes complicate queries; merge complexity
D: Manual merge error-prone and not scalable

Benefits:

✅ Fast updates (hours vs. days)
✅ No service interruption
✅ Resource efficient
✅ Scalable approach

Reference: Knowledge Base Management

Question 9: Inference Optimization - Quantization

Model quantization from FP16 to INT8 reduces memory by 50%. What is the expected quality impact?

A) No quality degradation
B) 1-3% degradation in task performance
C) 10-15% degradation
D) Model becomes unusable

Click to reveal answer

Correct Answer: B

Explanation: INT8 quantization has minimal quality impact with proper calibration:

Quantization Analysis:

FP16 (Baseline):

16 bits per weight
Full precision
Quality: 100% (reference)

INT8 (Quantized):

8 bits per weight
50% memory reduction
Quality: 97-99% of FP16
Degradation: 1-3% typically

Quantization Process:

Calibration: Run on representative dataset
Range Finding: Determine min/max values per layer
Quantization: Map FP16 range to INT8 range
Validation: Measure quality impact

Quality Metrics:

Perplexity: +1-2% increase (acceptable)
Task accuracy: -1-3% decrease
User satisfaction: Negligible impact

INT4 Comparison:

4 bits per weight
75% memory reduction
Quality: 90-95% of FP16
Degradation: 5-10% (more noticeable)

When to Use:

INT8: Production default (best balance)
INT4: Extreme resource constraints only
FP16: When quality is paramount

Why NOT Others:

A: Some degradation inevitable but minimal
C/D: Only with poor quantization or INT4/lower

Reference: Performance Optimization

Question 10: Batch Processing for Throughput

RAG system needs to process 10K user queries in batch overnight. What optimization provides BEST throughput?

A) Process queries sequentially with full model
B) Batch retrieval + batch inference with dynamic batching
C) Scale up to larger GPU for each query
D) Use multiple separate model instances

Click to reveal answer

Correct Answer: B

Explanation: Batch processing leverages parallelism for maximum throughput:

Optimized Batch Processing:

1. Batch Retrieval (Vectorization):

# Embed all queries at once
query_embeddings = embed_batch(queries, batch_size=128)
# Time: 10K queries / 128 per batch = 78 batches × 200ms = 16 seconds

# Batch vector search
results = vector_db.search_batch(query_embeddings, k=5)
# Time: 78 batches × 50ms = 4 seconds

2. Dynamic Batch Inference:

# Group queries by similar length (for efficiency)
batches = create_dynamic_batches(queries, max_batch_size=32)

# Process batches in parallel
for batch in batches:
    responses = model.generate(batch, batch_size=32)
# Time: 10K / 32 = 313 batches × 500ms = 157 seconds

Total Time: ~3 minutes (vs. 10K × 500ms = 83 minutes sequential)

Throughput Gain: 28x

Dynamic Batching Benefits:

✅ Amortizes model load overhead
✅ Better GPU utilization (80%+ vs. 20-30%)
✅ Reduced memory transfers
✅ Optimal for batch workloads

Why NOT Others:

A: Sequential leaves GPU idle; 28x slower
C: Larger GPU helps but batching more impactful
D: Multiple instances adds overhead without batching benefit

Reference: Performance Optimization

Question 11: Caching Strategy

Implementing caching for RAG to reduce latency. What caching layer provides BEST performance gain?

A) Cache final LLM responses (semantic cache)
B) Cache vector retrieval results
C) Cache embedding computation
D) All three layers with different TTLs

Click to reveal answer

Correct Answer: D

Explanation: Multi-layer caching provides maximum benefit with appropriate TTLs:

Caching Strategy:

Layer 1: Semantic Response Cache

✅ Cache final LLM responses
✅ Semantic similarity matching (similar queries)
✅ Hit: Instant response (< 5ms)
✅ TTL: 1-4 hours (context-dependent)
✅ Benefit: Avoids entire pipeline

Layer 2: Retrieval Cache

✅ Cache vector search results
✅ Key: Query embedding
✅ Hit: Skip retrieval (save 30-50ms)
✅ TTL: 15-60 minutes
✅ Benefit: Still personalizes response but skips retrieval

Layer 3: Embedding Cache

✅ Cache query embeddings
✅ Exact match only
✅ Hit: Skip embedding (save 20-50ms)
✅ TTL: 5-30 minutes
✅ Benefit: Useful for repeated exact queries

Cache Hit Rates (Typical):

Semantic cache: 15-30% (high value)
Retrieval cache: 20-40%
Embedding cache: 30-50%

Combined Latency:

No cache: 200ms average
With caching: 60-100ms average (50% reduction)

Cache Invalidation:

Knowledge base updates: Clear retrieval cache
Model updates: Clear all caches
Time-sensitive data: Aggressive TTLs

Why NOT Others:

A/B/C: Single-layer caching misses optimization opportunities
D: Multi-layer captures benefits at each stage

Reference: Performance Optimization

Question 12: SLA Definition for Production RAG

Defining SLA for customer-facing RAG chatbot. What metrics should be included?

A) Uptime only
B) Uptime + average latency
C) Uptime + P99 latency + quality score + error rate
D) Uptime + cost per query

Click to reveal answer

Correct Answer: C

Explanation: Comprehensive SLA covers availability, performance, quality, and reliability:

Production RAG SLA:

1. Availability:

✅ Uptime: 99.9% (8.76 hours downtime/year)
✅ Measured: Service available and responding
✅ Excludes: Planned maintenance windows

2. Latency:

✅ P50 Latency: < 300ms
✅ P95 Latency: < 800ms
✅ P99 Latency: < 1500ms
✅ Measured: End-to-end query → response time

3. Quality:

✅ Quality Score: > 90% (human evaluation)
✅ Relevance Rate: > 95% (retrieval accuracy)
✅ Measured: Weekly sample evaluation (100 queries)

4. Reliability:

✅ Error Rate: < 0.5%
✅ Errors: Timeouts, failures, crashes
✅ Excludes: User input errors

Example SLA:

Service Level Agreement: Enterprise RAG Chatbot

Availability: 99.9% uptime
Performance:
  - P50 latency < 300ms
  - P99 latency < 1500ms
Quality:
  - Response quality > 90% satisfaction
  - Retrieval relevance > 95%
Reliability:
  - Error rate < 0.5%
  - No data loss
Reporting: Monthly SLA report
Credits: Downtime > 0.1% = 10% service credit

Why NOT Others:

A: Uptime insufficient; users need performance guarantees
B: Average latency misleading (hides tail latencies)
D: Cost not customer-facing SLA metric

Reference: Enterprise Operations

Question 13: Disaster Recovery for Edge RAG

Production edge RAG system loses connectivity to management plane. What ensures continued operation?

A) Automatic failover to cloud RAG
B) Local autonomy: Model + knowledge base + monitoring all edge-local
C) Queue queries until connectivity restored
D) Shut down gracefully

Click to reveal answer

Correct Answer: B

Explanation: Local autonomy is core design principle for edge systems:

Edge Autonomy Architecture:

1. Local Model Deployment:

✅ Model files stored on edge
✅ No cloud dependencies for inference
✅ Continues operating during outage

2. Local Knowledge Base:

✅ Vector database edge-hosted
✅ Full index locally available
✅ Retrieval works offline

3. Local Monitoring:

✅ Metrics collected locally
✅ Local time-series database
✅ Alerts generated locally
✅ Syncs to cloud when available

4. Local Configuration:

✅ Config cached locally
✅ Policies enforced edge-side
✅ No remote dependencies

Degraded Mode Operations:

Cloud Connected:
✅ Full functionality
✅ Telemetry streaming
✅ Remote updates

Cloud Disconnected:
✅ Inference continues
✅ Retrieval continues
✅ Local monitoring active
⚠️ No model updates (acceptable)
⚠️ Telemetry queued (acceptable)

Why NOT Others:

A: Failover defeats edge purpose; adds cloud dependency
C: Queueing degrades user experience unacceptably
D: Shutting down violates availability SLA

Edge First Principle: Design for disconnected operation as default.

Reference: Production Architecture

Question 14: Cost Optimization

Edge RAG deployment costs $10K/month. What provides BEST cost reduction without sacrificing quality?

A) Use smaller model (70% cost reduction, 20% quality loss)
B) Implement model quantization + caching (40% cost reduction, 2% quality loss)
C) Reduce knowledge base size by 50%
D) Increase query latency limits

Click to reveal answer

Correct Answer: B

Explanation: Quantization + caching provides best cost/quality trade-off:

Cost Optimization Strategy:

1. Model Quantization (INT8):

✅ Cost Impact: -25% (smaller hardware, less memory)
✅ Quality Impact: -1-3%
✅ Memory: 50% reduction
✅ Inference: 20-30% faster
✅ Can use smaller/cheaper edge hardware

2. Response Caching:

✅ Cost Impact: -15% (reduced inference load)
✅ Quality Impact: None (same responses)
✅ Cache hit rate: 20-30%
✅ Reduced compute cycles

3. Batch Processing for Updates:

✅ Cost Impact: -5% (efficient resource use)
✅ Process knowledge base updates in off-peak
✅ Amortize embedding costs

Total Cost Reduction: 40% Total Quality Impact: -2%

Cost Breakdown:

Before Optimization:
- Hardware: $4K/month
- Inference compute: $3K/month
- Storage: $2K/month
- Networking: $1K/month
Total: $10K/month

After Optimization:
- Hardware: $3K (-25% from quantization)
- Inference compute: $2K (-33% from caching + quantization)
- Storage: $2K (unchanged)
- Networking: $1K (unchanged)
Total: $6K/month (40% reduction)

Why NOT Others:

A: 20% quality loss unacceptable for production
C: Reducing knowledge base impacts usefulness
D: Latency increases hurt user experience

Reference: Cost Optimization

Question 15: MLOps - Retraining Triggers

RAG model should be retrained. Which combination of signals indicates retraining is needed?

A) Query volume increased 20%
B) Data drift > 0.3 + quality score < 85% + user complaints
C) New hardware available
D) Monthly schedule reached

Click to reveal answer

Correct Answer: B

Explanation: Multiple converging signals indicate genuine need for retraining:

Retraining Decision Matrix:

Signal 1: Data Drift (Quantitative)

✅ KL divergence > 0.3 threshold
✅ Query distribution shifted from training data
✅ New topics/vocabulary emerging

Signal 2: Quality Degradation (Quantitative)

✅ Quality score < 85% (from baseline 92%)
✅ P95 latency increasing
✅ Retrieval relevance declining

Signal 3: User Feedback (Qualitative)

✅ Complaint volume increased 2x
✅ Negative sentiment in feedback
✅ Specific quality issues reported

Decision Logic:

if (data_drift > 0.3 AND quality_score < 85%) OR user_complaints > threshold:
    evaluate_retraining_benefit()
    if benefit > cost:
        initiate_retraining()

Retraining Cost-Benefit:

Cost: Compute, data collection, validation, deployment (1-2 weeks)
Benefit: Quality improvement, user satisfaction, reduced complaints

Why NOT Others:

A: Volume increase doesn’t indicate model issues
C: Hardware availability not a retraining driver
D: Schedule-based retraining may be wasteful or insufficient

Avoid Over-Retraining:

Retraining has costs (time, compute, validation)
Only retrain when clear benefit
Typical frequency: Quarterly or trigger-based

Reference: MLOps Workflows

Question 16: Knowledge Base Version Control

Managing multiple knowledge base versions across edge sites. What versioning strategy is BEST?

A) Single latest version deployed everywhere
B) Semantic versioning with controlled rollout and rollback capability
C) Each site maintains its own version independently
D) No versioning; continuous updates

Click to reveal answer

Correct Answer: B

Explanation: Semantic versioning with rollout control provides safety and consistency:

Versioning Strategy:

Semantic Versioning (SemVer):

Version Format: MAJOR.MINOR.PATCH
Example: 2.3.1

MAJOR (2): Breaking changes (schema change, major content restructure)
MINOR (3): New content added (new documents, sections)
PATCH (1): Corrections/updates (fixes, small updates)

Version Metadata:

{
  "version": "2.3.1",
  "created": "2025-10-15",
  "documents": 10483,
  "vectors": 10483000,
  "size_gb": 87.5,
  "changelog": "Added Q4 2025 regulatory updates",
  "compatible_models": ["v1.2.x", "v1.3.x"]
}

Controlled Rollout:

Phase 1: Staging (1 site, 48 hours)

Deploy to staging environment
Validate quality
Check compatibility

Phase 2: Canary (5% sites, 1 week)

Deploy to 5% of edge sites
Monitor query quality
Check for issues

Phase 3: Production (100%, 2 weeks)

Gradual rollout
Per-site validation
Automated rollback on failure

Rollback Capability:

Keep previous 2 versions on edge
One-command rollback
< 10 minute rollback time

Why NOT Others:

A: No rollback; risky for quality issues
C: Version fragmentation; inconsistent experience
D: Continuous updates lack validation gates

Reference: Knowledge Base Management

Question 17: Performance Troubleshooting

Production RAG P99 latency increased from 800ms to 2500ms. What is the FIRST diagnostic step?

A) Restart all edge services
B) Review monitoring dashboards for component-level latency breakdown
C) Scale up edge hardware immediately
D) Switch to smaller model

Click to reveal answer

Correct Answer: B

Explanation: Component-level analysis identifies the bottleneck before taking action:

Latency Breakdown Analysis:

Healthy Baseline:

Query → Embedding: 50ms
Embedding → Retrieval: 80ms
Retrieval → LLM: 30ms
LLM Inference: 500ms
Response Processing: 40ms
Total: 700ms ✅

Current (Degraded):

Query → Embedding: 50ms (unchanged)
Embedding → Retrieval: 80ms (unchanged)
Retrieval → LLM: 30ms (unchanged)
LLM Inference: 2200ms ❌ (4.4x increase!)
Response Processing: 40ms (unchanged)
Total: 2400ms

Root Cause Identified: LLM Inference

Next Steps:

Check GPU/CPU utilization (thermal throttling?)
Check concurrent requests (resource contention?)
Review recent model changes
Check system logs for errors

Common Causes:

✅ Resource contention (too many concurrent queries)
✅ Hardware degradation (thermal throttling, memory issues)
✅ Model change (accidental switch to larger model)
✅ Batch size misconfiguration

Why NOT Others:

A: Restart doesn’t address root cause; may hide symptoms temporarily
C: Scale up expensive; may not solve problem if software issue
D: Model change too drastic without diagnosis

Monitoring Best Practice: Always instrument each pipeline stage separately.

Reference: Performance Monitoring

Question 18: Enterprise Governance

Multi-tenant RAG system must ensure data isolation. What architecture enforces STRONGEST isolation?

A) Logical isolation: Single model + vector DB, filter by tenant ID
B) Physical isolation: Separate model instance + vector DB per tenant
C) Shared model + separate vector DB per tenant
D) Separate models + shared vector DB with encryption

Click to reveal answer

Correct Answer: B

Explanation: Physical isolation provides strongest security and compliance:

Multi-Tenant Isolation Options:

Option A: Logical Isolation

Architecture:
- Single model instance
- Single vector database
- Row-level security (tenant_id filter)

Pros: Cost-efficient, easy management
Cons:
❌ Risk of query/response leakage (bug/misconfiguration)
❌ Noisy neighbor problem
❌ Difficult to prove isolation for compliance

Option B: Physical Isolation (RECOMMENDED)

Architecture:
- Separate model instance per tenant
- Separate vector database per tenant
- Separate compute/memory/storage

Pros:
✅ Complete isolation (bug cannot leak data)
✅ Independent scaling per tenant
✅ Clear compliance story
✅ Tenant-specific customization

Cons: Higher cost, more complex management

Option C: Hybrid

Architecture:
- Shared model (reasonable)
- Separate vector DB per tenant (good)

Pros: Balanced
Cons:
⚠️ Model could theoretically leak between tenants
⚠️ Moderate compliance risk

Option D: Inverted Hybrid

Architecture:
- Separate models (good)
- Shared vector DB (bad)

Cons:
❌ Defeats purpose; vector DB leakage risk highest

Recommendation for Sovereign/Regulated:

Use Option B for high-security environments
Accept higher cost for compliance assurance
Use containerization/K8s for management at scale

Cost Management:

Use quantized models (lower memory)
Schedule model unloading for inactive tenants
Optimize vector DB storage

Reference: Enterprise Governance

Scoring Guide

Score Interpretation

🏆 17-18 correct (94-100%): Expert! Production RAG mastery

You can architect enterprise RAG systems
You understand MLOps best practices for edge AI
You’re ready to lead production deployments
Ready for principal architect/engineering roles

✅ 14-16 correct (78-89%): Strong! Advanced proficiency

You have solid production RAG knowledge
Review missed topics to achieve expert level
Ready for senior engineering roles

⚠️ 12-13 correct (67-72%): Good - Additional practice recommended

Solid foundation but gaps in advanced operations
Review MLOps and optimization sections
Complete hands-on labs

❌ Below 12 correct (<67%): Needs Improvement - Comprehensive review required

Revisit production RAG and MLOps modules
Complete all hands-on labs
Consider additional real-world practice

Study Recommendations

If you missed questions on Architecture & Design (Q1-3)

Focus Areas:

Review Production Architecture
Study model selection criteria
Understand vector database trade-offs
Practice architecture design exercises

If you missed questions on Retrieval & Optimization (Q4-5, Q11)

Focus Areas:

Review Performance Optimization
Study reranking techniques
Learn chunking strategies
Understand caching patterns

If you missed questions on MLOps (Q6-7, Q15-16)

Focus Areas:

Review MLOps Workflows
Study deployment patterns
Understand monitoring strategies
Learn versioning best practices

If you missed questions on Operations (Q8-10, Q12-14)

Focus Areas:

Review Enterprise Operations
Study SLA definitions
Learn cost optimization
Practice performance tuning

If you missed questions on Troubleshooting & Governance (Q13, Q17-18)

Focus Areas:

Review disaster recovery patterns
Study multi-tenancy architectures
Learn troubleshooting methodologies
Understand compliance requirements

Next Steps

After completing this assessment:

1. 🎯 Course Completion

Congratulations! You’ve completed all Level 300 assessments
Previous Quiz: Zero Trust & Troubleshooting Quiz
Back to Start: Level 300 Overview

2. 📚 Deep Dive Content

4. 🌐 External Resources

5. ✋ Need Help?

Review CONTRIBUTING.md
Check README for overall program
Share feedback on course content

Quiz Version: 1.0
Last Updated: November 2025
Total Questions: 18
Passing Score: 14/18 (78%)
Level: 300 - Advanced/Expert

Edge RAG Production - Knowledge Check

Quiz Instructions

Question 1: Production RAG Architecture

Question 2: Model Selection for Edge

Question 3: Vector Database Selection

Question 4: RAG Retrieval Strategy

Question 5: Knowledge Base Chunking Strategy

Question 6: MLOps - Model Deployment Strategy

Question 7: Model Monitoring - Data Drift Detection

Question 8: Knowledge Base Update Strategy

Question 9: Inference Optimization - Quantization

Question 10: Batch Processing for Throughput

Question 11: Caching Strategy

Question 12: SLA Definition for Production RAG

Question 13: Disaster Recovery for Edge RAG

Question 14: Cost Optimization

Question 15: MLOps - Retraining Triggers

Question 16: Knowledge Base Version Control

Question 17: Performance Troubleshooting

Question 18: Enterprise Governance

Scoring Guide

Score Interpretation

Study Recommendations

If you missed questions on Architecture & Design (Q1-3)

If you missed questions on Retrieval & Optimization (Q4-5, Q11)

If you missed questions on MLOps (Q6-7, Q15-16)

If you missed questions on Operations (Q8-10, Q12-14)

If you missed questions on Troubleshooting & Governance (Q13, Q17-18)

Next Steps

1. 🎯 Course Completion

2. 📚 Deep Dive Content

3. 🔗 Related Content

4. 🌐 External Resources

5. ✋ Need Help?