Bird
Raised Fist0

Ad Click-Through Rate Prediction - Logistic Regression to DLRM

SYSTEM-DESIGNhardInterview freq: very_high
Choose your preparation mode6 modes available
LearnTraceDebugTrade-offChallenge
🎯
Design an Ad Click-Through Rate Prediction System
45 min · senior
senior

You are tasked with designing a click-through rate prediction system for a large-scale ad platform. Your team of 5 engineers has 8 months to complete the project. The platform serves 10 million daily active users with a P99 latency SLA of 100ms for ad serving. You have access to historical click data, user demographics, and ad metadata. The system must transition from a logistic regression model to a more sophisticated DLRM (Deep Learning Recommendation Model) to improve prediction accuracy.

Given Info
  • 10 million DAU with a P99 latency SLA of 100ms
  • Historical click data, user demographics, and ad metadata available
  • Current system uses logistic regression
  • Team of 5 engineers with 8 months to deliver
  • Need to transition to DLRM for better accuracy
📋
Guided Steps
1/5
Step 1
Data Pipeline Design
How will you design the data pipeline to handle the scale and ensure data freshness?
💡 Hint
Consider the trade-off between batch processing and real-time data ingestion.
Key Decision
Use a lambda architecture to balance real-time and batch processing because it ensures data freshness and scalability.
Interviewer Expectation
Mid: Proposes a batch processing pipeline. Senior: Adds a real-time component to handle data freshness and explains how it integrates with batch processing.
⚠️Common Wrong Answer
Relying solely on batch processing, leading to stale data and delayed model updates.
Step 2
Model Selection and Transition
What considerations are important when transitioning from logistic regression to DLRM?
💡 Hint
Think about the differences in feature representation and model complexity.
Key Decision
Choose DLRM for its ability to handle sparse features and interactions because it improves prediction accuracy over logistic regression.
Interviewer Expectation
Mid: Suggests DLRM for better accuracy. Senior: Explains the need for embedding layers and how they handle categorical features efficiently.
⚠️Common Wrong Answer
Underestimating the complexity of DLRM and not planning for embedding layer design.
Step 3
Serving Infrastructure
How will you design the serving infrastructure to meet the 100ms latency requirement?
💡 Hint
Consider the impact of model complexity on inference time.
Key Decision
Deploy a two-tower architecture with pre-computed embeddings to minimize online inference time.
Interviewer Expectation
Mid: Proposes a basic serving setup. Senior: Details a two-tower architecture and quantifies latency improvements.
⚠️Common Wrong Answer
Ignoring the latency impact of complex models, leading to SLA violations.
Step 4
Monitoring and Feedback Loop
What monitoring and feedback mechanisms will you implement to ensure model performance?
💡 Hint
Consider both offline and online metrics for comprehensive monitoring.
Key Decision
Implement real-time monitoring of CTR and revenue impact because they directly reflect business objectives.
Interviewer Expectation
Mid: Mentions basic monitoring. Senior: Proposes a feedback loop with A/B testing to continuously improve the model.
⚠️Common Wrong Answer
Focusing only on offline metrics, missing real-time performance issues.
Step 5
Scalability and Maintenance
How will you ensure the system scales with increasing data and user base?
💡 Hint
Think about distributed systems and model retraining strategies.
Key Decision
Use distributed training and inference with horizontal scaling to handle increased load.
Interviewer Expectation
Mid: Suggests basic scaling strategies. Senior: Details distributed training and inference, and plans for regular retraining.
⚠️Common Wrong Answer
Neglecting to plan for scalability, leading to bottlenecks as data volume grows.
📊
Scoring Rubric
▼ Show
Must Have - 60%
  • Designs a data pipeline that balances batch and real-time processing.
  • Selects DLRM for its ability to handle sparse features and interactions.
  • Proposes a serving infrastructure that meets the 100ms latency requirement.
  • Implements monitoring that includes both CTR and revenue impact.
  • Plans for scalability with distributed training and inference.
Good to Have - 30%
  • Explains the need for embedding layers in DLRM.
  • Details a two-tower architecture for serving.
  • Proposes a feedback loop with A/B testing.
  • Plans for regular model retraining to handle data drift.
Bonus - 10%
  • Quantifies latency improvements with the proposed architecture.
  • Integrates a lambda architecture for data processing.
  • Explains the trade-offs between model complexity and inference time.
✅
Model Answer
▼ Reveal

The ideal solution involves designing a scalable data pipeline using a lambda architecture to handle both batch and real-time data. Transitioning to DLRM requires careful consideration of feature representation and embedding layers to handle sparse data. The serving infrastructure should use a two-tower architecture to meet the 100ms latency requirement. Monitoring should include real-time CTR and revenue impact, with a feedback loop for continuous improvement. Scalability is ensured through distributed training and inference.

Key Decisions
  • Use a lambda architecture for data processing to ensure data freshness and scalability.
  • Select DLRM for its ability to handle sparse features and interactions, improving prediction accuracy.
  • Deploy a two-tower architecture with pre-computed embeddings to minimize online inference time.
  • Implement real-time monitoring of CTR and revenue impact to directly reflect business objectives.
  • Use distributed training and inference with horizontal scaling to handle increased load.
Anti-patterns
Relying solely on batch processing, leading to stale data.
Ignoring the complexity of DLRM and not planning for embedding layers.
Neglecting real-time monitoring, missing performance issues.
Failing to plan for scalability, leading to system bottlenecks.

Practice

(1/5)
1. For a bot detection system using behavioral signals, which metric is most appropriate to optimize for minimizing false positives?
easy
A. F1 Score
B. Recall
C. Precision
D. Accuracy

Solution

  1. Step 1: Understand the goal of minimizing false positives.

    Precision is the metric that focuses on reducing false positives.

  2. Step 2: Eliminate wrong options.

    Recall focuses on false negatives; F1 balances precision and recall; accuracy can be misleading with imbalanced data.

  3. Final Answer:

    Option C -> Option C

  4. Quick Check:

    Minimize false positives = optimize precision [OK]
Hint: Minimize false positives = optimize precision [OK]
Common Mistakes:
2. In the context of LLM serving infrastructure, which component is primarily responsible for reducing latency by reusing previously computed attention outputs?
easy
A. Batching
B. Speculative Decoding
C. KV Cache
D. Load Balancer

Solution

  1. Step 1: Identify the component's role.

    KV Cache stores key-value pairs from previous computations to avoid redundant calculations.

  2. Step 2: Eliminate wrong options.

    Batching reduces overhead by processing multiple requests together, not by reusing computations. Speculative Decoding predicts future tokens, not reuse. Load Balancer distributes requests, unrelated to computation reuse.

  3. Final Answer:

    Option C -> Option C

  4. Quick Check:

    KV Cache reuses computations to cut latency. [OK]
Hint: KV Cache reuses computations to cut latency. [OK]
Common Mistakes:
3. In the context of semantic search, which component is responsible for encoding both queries and documents into a shared vector space?
easy
A. Bi-Encoder
B. Cross-Encoder
C. Dense Retriever
D. Sparse Retriever

Solution

  1. Step 1: Identify the role of each component

    Bi-Encoders encode both queries and documents into a shared vector space for efficient retrieval.

  2. Step 2: Eliminate wrong options

    Cross-Encoders evaluate query-document pairs directly, Dense Retrievers use encoded vectors, Sparse Retrievers use traditional methods.

  3. Final Answer:

    Option A -> Option A

  4. Quick Check:

    Bi-Encoder = shared vector space [check]
Hint: Bi-Encoder = shared vector space [check]
Common Mistakes:
4. In a spam detection system, when should you prefer a decision tree over a neural network?
medium
A. When interpretability is crucial
B. When the dataset is very large
C. When the data is highly unstructured
D. When computational resources are abundant

Solution

  1. Step 1: Consider the need for model interpretability

    Decision trees are preferred when interpretability is crucial due to their clear decision paths.

  2. Step 2: Eliminate options based on constraints

    Neural networks (A, C, D) are better for large, unstructured data and when resources are abundant.

  3. Final Answer:

    Option A -> Option A

  4. Quick Check:

    Use trees for interpretability. [OK]
Hint: Use trees for interpretability. [OK]
Common Mistakes:
5. As the number of concurrent experiments increases in an A/B testing platform, which component is most likely to become a bottleneck first?
hard
A. Data Storage Layer
B. Experiment Dashboard
C. Randomization Engine
D. User Segmentation Module

Solution

  1. Step 1: Consider the impact of scaling experiments.

    More experiments generate more data, stressing storage.

  2. Step 2: Eliminate wrong options.

    Dashboard and segmentation handle display and grouping, while randomization is not data-intensive.

  3. Final Answer:

    Option A -> Option A

  4. Quick Check:

    More experiments = more data. [OK]
Hint: More experiments = more data. [OK]
Common Mistakes:
Recommended

Ready to prove it?

Pass the test and this pattern moves unknown strong while earning +1 Grip toward your certificate.

10 questions~5 mineasy hard