IBM Certification Big Data Architect 4 — Questions and Answers
Question 1: Which IBM solution is designed for in-database analytics, executing advanced analytics algorithms directly within the Db2 Warehouse engine?
- IBM SPSS Collaboration and Deployment Services
- IBM Db2 Warehouse in-database analytics (Correct answer)
- IBM Watson Machine Learning
- IBM OpenScale
Correct answer: IBM Db2 Warehouse in-database analytics
Db2 Warehouse supports in-database analytics that run algorithms inside the database engine, eliminating data movement overhead.
Question 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?
- Row-based storage because it supports faster row inserts
- Columnar storage because it reads only relevant columns and compresses efficiently (Correct answer)
- Row-based storage because transactions require full row access
- In-memory row store because aggregations need random access
Correct 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.
Question 3: In IBM Event Streams (Apache Kafka), what does the consumer group concept enable?
- Encryption of messages at rest within a partition
- Parallel consumption of topic partitions across multiple consumers (Correct answer)
- Schema enforcement on producer messages
- Exactly-once delivery within a single producer session
Correct 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.
Question 4: When applying the Kappa architecture as an alternative to Lambda on IBM platforms, what is the key simplification?
- Eliminating the need for any streaming component
- Processing all data through a single streaming pipeline, removing the batch layer (Correct answer)
- Replacing Kafka with a relational message broker
- Using only batch jobs for historical reprocessing
Correct 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.
Question 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?
- IBM Db2 with JSON extensions
- IBM Graph (JanusGraph on IBM Cloud) (Correct answer)
- IBM Netezza Performance Server
- IBM BigInsights HBase
Correct 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.
Question 6: Which approach does IBM recommend for handling schema-on-read versus schema-on-write in a data lake?
- Always enforce schema-on-write to prevent data corruption at ingestion
- Use schema-on-read for raw landing zones and schema-on-write for curated zones (Correct answer)
- Avoid schemas entirely to maximize ingestion flexibility
- Apply schema-on-write only for streaming data sources
Correct 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).
Question 7: In IBM OpenScale (now IBM Watson OpenScale / AI Fairness 360), what is the primary function relevant to a big data ML pipeline?
- Training deep learning models on GPU clusters
- Monitoring deployed ML models for bias, drift, and explainability (Correct answer)
- Orchestrating Spark jobs across YARN clusters
- Managing HDFS quota policies for ML datasets
Correct 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.
IBM Certification Big Data Architect 4
Which IBM solution is designed for in-database analytics, executing advanced analytics algorithms directly within the Db2 Warehouse engine?