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Query Understanding - Intent Classification, Entity Extraction

SYSTEM-DESIGNmediumInterview freq: high
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🎯
Design an Intent Classification and Entity Extraction System for a Voice Assistant
45 min · mid
mid

You are tasked with designing an ML system for a voice assistant used by 10M DAU. The system must classify user intents and extract entities from voice queries. You have 6 months and a team of 3 engineers. The system must handle 1000 QPS with a P99 latency SLA of 200ms. You have access to a labeled dataset of 1M queries.

Given Info
  • 10M daily active users
  • P99 latency SLA of 200ms
  • 1000 queries per second
  • 1M labeled queries available
  • Team of 3 engineers, 6-month timeline
📋
Guided Steps
1/4
Step 1
Data Pipeline Design
How will you design the data pipeline to handle the labeled queries and ensure data freshness?
💡 Hint
Consider the trade-off between real-time processing and batch updates for model retraining.
Key Decision
Batch processing with daily updates because it balances data freshness with engineering effort given the team size.
Interviewer Expectation
Mid: Proposes a batch processing pipeline. Senior: Also considers incremental updates for critical data freshness and explains the trade-offs.
⚠️Common Wrong Answer
Choosing real-time processing without considering the engineering overhead and team constraints.
Step 2
Model Selection
What model architecture will you choose for intent classification and entity extraction?
💡 Hint
Think about the latency requirements and the complexity of the model.
Key Decision
Use a BERT-based model for its strong performance on NLP tasks, with optimizations for inference speed.
Interviewer Expectation
Mid: Chooses a BERT-based model. Senior: Justifies the choice with latency optimizations like distillation or quantization.
⚠️Common Wrong Answer
Selecting a complex model like GPT-3 without addressing latency and inference cost.
Step 3
Serving Architecture
How will you design the serving architecture to meet the latency SLA?
💡 Hint
Consider the impact of model size and inference time on the serving infrastructure.
Key Decision
Deploy on a GPU-based infrastructure to meet the P99 latency SLA of 200ms.
Interviewer Expectation
Mid: Proposes GPU-based serving. Senior: Also considers horizontal scaling and load balancing strategies.
⚠️Common Wrong Answer
Relying solely on CPU-based serving without considering the latency impact.
Step 4
Monitoring and Retraining Strategy
What is your strategy for monitoring model performance and retraining?
💡 Hint
Think about how you will detect performance degradation and handle data drift.
Key Decision
Implement monitoring for intent classification accuracy and entity extraction precision, with a retraining schedule based on performance thresholds.
Interviewer Expectation
Mid: Proposes basic monitoring and periodic retraining. Senior: Includes specific metrics and thresholds for triggering retraining.
⚠️Common Wrong Answer
Ignoring the need for monitoring and relying on fixed retraining intervals.
📊
Scoring Rubric
▼ Show
Must Have - 60%
  • Designs a batch processing pipeline with daily updates.
  • Selects a BERT-based model with inference optimizations.
  • Proposes GPU-based serving to meet latency requirements.
  • Includes monitoring for model performance and data drift.
Good to Have - 30%
  • Considers incremental updates for data freshness.
  • Justifies model choice with specific latency optimizations.
  • Includes horizontal scaling and load balancing in serving design.
  • Defines specific metrics and thresholds for retraining.
Bonus - 10%
  • Proposes model distillation or quantization for latency improvements.
  • Considers the use of edge computing for further latency reduction.
  • Includes a strategy for A/B testing model updates.
✅
Model Answer
▼ Reveal

The ideal solution involves designing a batch processing pipeline with daily updates, selecting a BERT-based model optimized for inference speed, deploying on a GPU-based infrastructure to meet latency requirements, and implementing a robust monitoring and retraining strategy.

Key Decisions
  • Batch processing with daily updates because it balances data freshness with engineering effort.
  • Use a BERT-based model for its strong NLP performance, with optimizations for inference speed.
  • Deploy on a GPU-based infrastructure to meet the P99 latency SLA of 200ms.
  • Implement monitoring for intent classification accuracy and entity extraction precision, with a retraining schedule based on performance thresholds.
Anti-patterns
Choosing real-time processing without considering the engineering overhead.
Selecting a complex model like GPT-3 without addressing latency and inference cost.
Relying solely on CPU-based serving without considering the latency impact.
Ignoring the need for monitoring and relying on fixed retraining intervals.

Practice

(1/5)
1. For a content-based recommendation system, which metric is most appropriate to evaluate the quality of item embeddings?
easy
A. Cosine similarity
B. Precision
C. Recall
D. Mean Absolute Error (MAE)

Solution

  1. Step 1: Understand the evaluation context

    Item embeddings are evaluated based on how well they capture similarities, which is best measured by cosine similarity.

  2. Step 2: Eliminate wrong options

    A & B: Precision and Recall are more suited for classification tasks. D: MAE is used for regression, not similarity.

  3. Final Answer:

    Option A -> Option A

  4. Quick Check:

    Cosine similarity measures embedding quality. [OK]
Hint: Cosine similarity measures embedding quality. [OK]
Common Mistakes:
2. For a vector search system using HNSW, which metric should be optimized to ensure fast retrieval times?
easy
A. Recall
B. Latency
C. Precision
D. F1 Score

Solution

  1. Step 1: Understand the goal of fast retrieval.

    Latency measures the time taken for retrieval, crucial for speed.

  2. Step 2: Eliminate wrong options.

    Precision, recall, and F1 score relate to accuracy, not speed.

  3. Final Answer:

    Option B -> Option B

  4. Quick Check:

    Optimize latency for fast retrieval. [OK]
Hint: Optimize latency for fast retrieval. [OK]
Common Mistakes:
3. In a hate speech detection system, should you prioritize using a deep learning model over a simpler model like logistic regression when computational resources are limited?
medium
A. Yes, deep learning models always perform better.
B. Yes, because deep learning models require less feature engineering.
C. No, simpler models are more interpretable and efficient.
D. No, because simpler models cannot handle large datasets.

Solution

  1. Step 1: Evaluate resource constraints

    When computational resources are limited, simpler models like logistic regression are more efficient and interpretable.

  2. Step 2: Eliminate wrong options

    Deep learning models may perform better but require more resources, and simpler models can handle large datasets with proper techniques.

  3. Final Answer:

    Option C -> Option C

  4. Quick Check:

    Limited resources = Simpler models [OK]
Hint: Limited resources = Simpler models [OK]
Common Mistakes:
4. In an ML platform, what is a likely failure mode if the feature store is not properly versioned?
medium
A. Increased model training time
B. Inconsistent model predictions
C. Higher computational costs
D. Reduced data ingestion speed

Solution

  1. Step 1: Identify the impact of unversioned features.

    Without versioning, features may change unexpectedly, leading to inconsistent predictions.

  2. Step 2: Eliminate wrong options.

    Options A, C, and D are not directly affected by feature versioning.

  3. Final Answer:

    Option B -> Option B

  4. Quick Check:

    Versioning prevents prediction inconsistency. [OK]
Hint: Versioning prevents prediction inconsistency. [OK]
Common Mistakes:
5. As the scale of users increases tenfold, which component of a bot detection system using behavioral signals is most likely to fail first?
hard
A. Real-time processing capability
B. Data storage capacity
C. Model training time
D. User interface responsiveness

Solution

  1. Step 1: Consider the impact of scale on system components.

    Real-time processing is critical for immediate detection and is resource-intensive.

  2. Step 2: Eliminate wrong options.

    Data storage can be scaled with cloud solutions; model training is periodic; UI is less critical to detection.

  3. Final Answer:

    Option A -> Option A

  4. Quick Check:

    Real-time processing is resource-intensive at scale [OK]
Hint: Real-time processing is resource-intensive at scale [OK]
Common Mistakes:
Recommended

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10 questions~5 mineasy hard