IBM Cloud Pak for Data V4.x Data Engineer Advanced Practice Exam: Hard Questions 2025

A multi-tenant Cloud Pak for Data environment is experiencing performance degradation in DataStage jobs when multiple teams run parallel ETL workflows. The infrastructure team reports CPU throttling on specific worker nodes, but overall cluster utilization is only at 40%. Which architectural approach would BEST resolve this issue while maintaining workload isolation?

During a DataStage job that processes 500GB of data using a connector stage to read from a partitioned database table, you observe that only one partition is being utilized despite the database having 16 partitions and the job configured for 16-way parallel execution. The connector supports parallel reads. What is the MOST LIKELY root cause and solution?

An enterprise is implementing Watson Knowledge Catalog with automated data quality rules across 200+ data sources. They need to ensure that PII data classification runs automatically on new assets while minimizing computational overhead. Business glossary terms must be automatically assigned based on semantic analysis. However, data stewards report that classification jobs are timing out on datasets larger than 50GB, and semantic term assignment is inconsistent. What is the OPTIMAL solution architecture?

A Data Virtualization layer is joining six tables from different sources (DB2, Oracle, and MongoDB) to create a unified customer view. Query performance is poor (45+ seconds) despite indexed columns being used in join conditions. The execution plan shows that MongoDB collections are being fully materialized in cache before joins. Which optimization strategy would provide the GREATEST performance improvement?

A DataStage job that processes streaming data from Kafka is failing intermittently with 'OutOfMemory' errors despite adequate heap size configuration. The job uses a Transformer stage with complex lookups against a 2GB reference dataset that changes hourly. Processing rates vary from 10K to 100K messages per second. Memory profiling shows the lookup cache is being repeatedly reloaded. What architectural change would BEST resolve this issue?

An organization has implemented data lineage tracking in Cloud Pak for Data across DataStage jobs, Data Refinery flows, and Jupyter notebooks. Business users report that lineage graphs are incomplete—transformations within custom Python UDFs in DataStage and specific Data Refinery operations don't appear. The metadata import jobs complete successfully. What is the MOST COMPREHENSIVE solution to achieve complete lineage visibility?

A Cloud Pak for Data deployment uses multiple persistent volumes for Watson services, DataStage projects, and Knowledge Catalog metadata. After a storage array failure and recovery, several DataStage jobs fail with 'Project not found' errors despite PVCs showing as 'Bound'. Pods are running normally. Volume snapshots exist from 6 hours before the failure. What is the MOST EFFECTIVE recovery approach that minimizes data loss?

A data virtualization view combines real-time sales data (updated every 5 minutes) with historical data (updated daily). Analysts report that queries sometimes return inconsistent results when comparing current day sales to historical trends. The view uses a UNION ALL combining today's data from the operational database with prior data from the data warehouse. What design approach would ensure consistency while maintaining acceptable performance?

A DataStage parallel job processes 10TB of data daily using a 32-node configuration file. After migrating to Cloud Pak for Data, the same job runs 3x slower despite similar CPU/memory resources. Analysis shows that data skew is causing one partition to process 40% of the data while others remain underutilized. The job uses hash partitioning on customer_id. What combination of techniques would MOST effectively resolve this performance regression?

A company is implementing a governed self-service analytics platform where business users can create Data Refinery flows and publish datasets to Watson Knowledge Catalog. Security requirements mandate that PII data must be masked for most users, but data engineers need access to raw data. Masking policies must be enforced consistently across Data Refinery, data virtualization, and DataStage. The current implementation shows that masked data in catalog previews appears unmasked when accessed through virtualization. What is the ROOT CAUSE and solution?