IBM Cloud Pak for Data v4.x Data Engineer Advanced Practice Exam: Hard Questions 2025

An enterprise is deploying Cloud Pak for Data v4.x across multiple OpenShift clusters in different regions for disaster recovery. The DataStage service must maintain consistent metadata and job definitions across clusters while ensuring minimal RPO. Which architectural approach best addresses these requirements while maintaining operational efficiency?

A data engineer encounters performance degradation in a DataStage parallel job processing 500GB of data daily. The job reads from multiple Oracle tables, performs complex transformations with 15 lookup stages, and writes to a Db2 warehouse. CPU utilization remains at 40% while I/O wait time exceeds 60%. The job configuration uses 8 nodes with 4 processing cores each. What is the MOST likely root cause and appropriate remediation?

An organization needs to virtualize data from SAP HANA, MongoDB, and Salesforce into a unified view for analytics while implementing row-level security based on user attributes stored in LDAP. The virtual view must support real-time queries with sub-second response times for a dashboard serving 200 concurrent users. Which Data Virtualization configuration strategy should be employed?

A Cloud Pak for Data environment experiences intermittent Watson Knowledge Catalog asset search failures with 'elasticsearch cluster health yellow' warnings. The catalog contains 2 million assets with complex lineage relationships. CPU and memory utilization are within normal ranges, but heap usage on elasticsearch pods shows frequent GC pauses. What is the most appropriate resolution strategy?

A data governance team needs to enforce a policy where personally identifiable information (PII) discovered in any dataset must be automatically masked for all users except the Data Privacy team, and any new columns added to existing tables must be automatically scanned and classified within 24 hours. The environment processes 500 new data assets weekly across structured and semi-structured sources. Which implementation approach satisfies these requirements?

During a Cloud Pak for Data upgrade from v4.0 to v4.6, the Data Virtualization service fails to start with 'CrashLoopBackOff' status. Logs indicate 'schema version mismatch' errors. The pre-upgrade backup was completed successfully, but the upgrade operator shows the service as updated. What is the correct recovery procedure?

A DataStage job must incrementally load data from a source table with 10 billion rows where the source system does not provide change data capture (CDC) or reliable timestamps. The source is a partitioned Oracle database with a composite primary key (customer_id, transaction_id). Full loads take 18 hours but business requirements mandate data freshness within 4 hours. What is the most efficient incremental loading strategy?

An organization has implemented Watson Knowledge Catalog with automated business term assignment based on column name matching and data profiling. Data stewards report that technical columns like 'CUST_ID' are incorrectly receiving multiple conflicting business term assignments ('Customer Identifier', 'Client Reference Number', 'Account Holder ID') due to overlapping term matching rules. This affects downstream data protection rule application. What is the most effective governance approach to resolve this?

A Data Virtualization environment serving 50 concurrent analytical queries experiences query timeout issues specifically for joins between a virtualized Hadoop Hive table (5TB, 20 billion rows) and a virtualized PostgreSQL table (500GB, 2 billion rows). Network latency between systems is minimal. Execution plans show the join is being processed at the Data Virtualization engine with full table scans. Which optimization approach will most effectively address this issue?

A Cloud Pak for Data environment spans three OpenShift clusters (dev, staging, production) with separate Watson Knowledge Catalog instances. The organization needs to promote data governance artifacts (business terms, data classes, governance policies, and data protection rules) from dev through staging to production while maintaining environment-specific role assignments and data source connections. Audit trail and rollback capabilities are required. What is the recommended approach?