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Smart Factory Data Analytics Flashcards

7 cards from real SACA practice questions. Tap to flip, then mark Knew It or Still Learning โ€” missed cards come back until you master them.

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  1. Which smart factory data strategy involves collecting raw data without a predefined schema to enable flexible future analysis?

    Answer: Schema-on-read

    Schema-on-read allows raw data to be stored in a data lake without enforcing structure at ingestion, applying the schema only when the data is queried.

  2. A factory wants to predict remaining useful life (RUL) of a motor using sensor history. Which model family is best suited for learning temporal dependencies in the degradation signal?

    Answer: Recurrent Neural Network (RNN) / LSTM

    RNNs and LSTMs are designed to capture long-range temporal dependencies in sequential data, making them well-suited for RUL prediction from sensor time-series.

  3. What does the term 'cold path' refer to in a lambda architecture deployed in a smart factory?

    Answer: Batch processing of historical data for deep analytics and model retraining

    In lambda architecture, the cold (batch) path processes large volumes of historical data at high latency to produce accurate, comprehensive analytics.

  4. Which data governance practice ensures that factory sensor data can be traced from its origin machine through every transformation to its final analytics output?

    Answer: Data lineage tracking

    Data lineage tracking records the full journey of data from source to output, enabling auditability, debugging, and regulatory compliance.

  5. A quality control model achieves 99% accuracy on a dataset where 99% of parts are non-defective. Why is this accuracy misleading?

    Answer: The high accuracy reflects class imbalance, not genuine defect detection ability

    When the negative class dominates, a model that always predicts 'non-defective' achieves high accuracy while completely failing to detect any real defects.

  6. In smart factory analytics, what is 'feature engineering' in the context of preparing sensor data for machine learning?

    Answer: Transforming raw sensor readings into meaningful input variables that improve model performance

    Feature engineering transforms raw measurements into derived signals (e.g., rolling mean, FFT amplitude, rate of change) that encode domain knowledge and improve model accuracy.

  7. Which standard communication model allows different smart factory devices from multiple vendors to exchange data using a unified information model?

    Answer: OPC UA (OPC Unified Architecture)

    OPC UA provides a platform-independent, vendor-neutral information model and secure communication framework adopted widely across industrial automation and smart factory systems.

Smart Factory Data Analytics Flashcards โ€” SACA Study Cards with Answers