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Big Data Architect Flashcards

7 cards from real IBM Certification practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

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  1. Which IBM solution is designed for in-database analytics, executing advanced analytics algorithms directly within the Db2 Warehouse engine?

    Answer: IBM Db2 Warehouse in-database analytics

    Db2 Warehouse supports in-database analytics that run algorithms inside the database engine, eliminating data movement overhead.

  2. A Big Data Architect needs to choose between row-based and columnar storage for an analytical workload scanning millions of rows for aggregations. What is the recommended choice?

    Answer: Columnar storage because it reads only relevant columns and compresses efficiently

    Columnar storage reads only the columns involved in a query and achieves high compression ratios, making analytical aggregations significantly faster.

  3. In IBM Event Streams (Apache Kafka), what does the consumer group concept enable?

    Answer: Parallel consumption of topic partitions across multiple consumers

    Consumer groups allow multiple consumer instances to divide topic partitions among themselves, enabling parallel and scalable message processing.

  4. When applying the Kappa architecture as an alternative to Lambda on IBM platforms, what is the key simplification?

    Answer: Processing all data through a single streaming pipeline, removing the batch layer

    Kappa architecture removes the batch layer entirely, reprocessing historical data by replaying the stream through the same real-time pipeline.

  5. An IBM Big Data Architect is evaluating graph databases for a fraud detection use case. Which IBM-ecosystem graph solution is most directly applicable?

    Answer: IBM Graph (JanusGraph on IBM Cloud)

    IBM Graph, built on JanusGraph, is designed for traversing highly connected datasets and is well-suited to fraud detection relationship analysis.

  6. Which approach does IBM recommend for handling schema-on-read versus schema-on-write in a data lake?

    Answer: Use schema-on-read for raw landing zones and schema-on-write for curated zones

    IBM recommends landing raw data without enforcing a schema (schema-on-read) then applying strict schemas in curated or consumption zones (schema-on-write).

  7. In IBM OpenScale (now IBM Watson OpenScale / AI Fairness 360), what is the primary function relevant to a big data ML pipeline?

    Answer: Monitoring deployed ML models for bias, drift, and explainability

    IBM Watson OpenScale monitors production ML models to detect fairness issues, data drift, and provides explainability of model predictions.