I B M C E R T I F I C A T I O N
C9006000 IBM Certified Architect – Cloud Pak for Data V4.7 Practice Exam
Exam Number: 4353 | Last updated April 17, 2026 | 404+ questions across 5 vendor-aligned objectives
Data-platform architects who design Cloud Pak for Data V4.7 deployments for analytics, AI, and data-fabric workloads target the C9006000 credential. This architect-level exam validates your ability to plan storage, compute, governance, and service-composition choices across the CP4D portfolio. Candidates should be fluent with Watson Knowledge Catalog, Db2, DataStage, Watson Studio, and the operator-based deployment model that underpins all of them.
Hauling in 26% of the exam, Architecture and Sizing covers cluster sizing, storage tier selection, node labeling, and capability-level resource planning. At 22%, Data Governance covers Watson Knowledge Catalog, business glossaries, data quality, and lineage tracking. A further 20% targets Service Composition, covering Watson Studio, AutoAI, DataStage, Db2, and how they interact in a full data pipeline.
Rimming the remaining objectives, Integration and Data Fabric accounts for 18% and spans Watson Query, data virtualization, and cross-source federation. Security and Multitenancy represents 14% and spans projects, deployment spaces, and role-based isolation. Architect-level questions often require you to predict how a decision in one CP4D capability affects another — draw the service dependency graph before committing.
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Question #1 - Architecture and Sizing
A CP4D V4.7 architect at Fenshire Insurance must size a cluster that will run Watson Studio, Db2, and DataStage together for 200 users.
Which sizing approach matches the V4.7 architect reference?
A) Use a default single node size regardless of capabilities
B) Size each capability against its own resource profile using the V4.7 sizing guide, aggregate the per-capability node requirements, and add headroom for bursts and upgrades
C) Ask users to estimate their needs and sum the guesses
D) Skip sizing and expect autoscaling to solve everything
Show solution
Correct answers: B – Explanation:
Per-capability sizing aggregated with headroom is the V4.7 architect reference. Default sizing, user guesses, and autoscale-only all fail cluster planning. Source: Check Source
Question #2 - Architecture and Sizing
A CP4D V4.7 storage-tier decision at Oakhaven Utilities must place Db2 data on high-IO storage while Watson Studio notebooks use cheaper storage.
Which architect pattern fits?
A) Assign per-capability storage classes matched to their I/O characteristics, using dedicated high-IO classes for Db2 and cost-efficient classes for notebooks
B) Put every capability on the same default storage class regardless of I/O needs
C) Use RAM disk for every service
D) Skip storage design entirely
Show solution
Correct answers: A – Explanation:
Per-capability storage classes matched to workload characteristics is the V4.7 architect reference. One-class-for-all, RAM disk, and no-design all fail storage architecture. Source: Check Source
Question #3 - Architecture and Sizing
A CP4D V4.7 architect at Briarford Retail must isolate DataStage compute from Watson Studio compute on a shared cluster.
Which V4.7 architect technique fits?
A) Label nodes by capability and use node selectors or taints-and-tolerations so DataStage and Watson Studio pods land on dedicated node pools
B) Put every capability on every node and hope scheduling works out
C) Run DataStage on developer laptops
D) Disable the scheduler
Show solution
Correct answers: A – Explanation:
Node labeling plus selectors/taints is the V4.7 architect isolation reference. Uniform placement, laptops, and scheduler-off all fail isolation. Source: Check Source
Question #4 - Data Governance
A Watson Knowledge Catalog design at Lintmere Insurance must enforce that columns tagged as PII are masked by default when data is queried.
Which V4.7 governance capability fits?
A) Skip governance because analysts are trusted
B) Ask analysts to self-restrict when they see PII
C) Remove PII columns entirely from the catalog
D) Define a WKC data-protection rule that masks columns tagged as PII, and apply it through the governance framework so enforcement happens at query time
Show solution
Correct answers: D – Explanation:
WKC data-protection rules with runtime enforcement is the V4.7 governance reference. Self-restriction, column removal, and no-governance all fail the requirement. Source: Check Source
Question #5 - Data Governance
A business-glossary effort at Havershall Financial needs consistent business terms bound to technical columns across many data sources.
Which V4.7 governance feature fits?
A) Define the business glossary in Watson Knowledge Catalog and assign terms to columns via the catalog’s assignment workflow, enabling lineage and consistent semantics
B) Maintain a separate spreadsheet of terms per team
C) Skip glossaries and rely on individual memory
D) Create duplicate terms across every data source
Show solution
Correct answers: A – Explanation:
WKC business glossary with column assignments is the V4.7 governance reference. Per-team spreadsheets, individual memory, and duplicate terms all fail semantic consistency. Source: Check Source
Question #6 - Service Composition
A data pipeline at Trentham Analytics loads source data with DataStage, explores it in Watson Studio, and trains an AutoAI model.
Which V4.7 service-composition pattern fits?
A) Move data between capabilities by emailing CSVs
B) Chain DataStage → Watson Studio notebooks → AutoAI experiments within a CP4D project so each step consumes the prior step’s outputs via shared project assets
C) Use unrelated products in separate environments and glue them together manually
D) Skip integration and keep each tool in its own silo
Show solution
Correct answers: B – Explanation:
Chained capabilities via CP4D project assets is the V4.7 service-composition reference. Email CSVs, unrelated products, and silos all fail the pattern. Source: Check Source
Question #7 - Service Composition
A CP4D V4.7 architect at Summerhill Bank must model a dependency between AutoAI experiment output and a downstream scoring deployment.
Which V4.7 composition artifact fits?
A) Skip deployment spaces and score ad hoc
B) Copy model files around between services manually
C) Promote the AutoAI model to a deployment space, version it, and reference it from the scoring deployment — keeping the dependency explicit and versioned
D) Keep the model only on a developer’s laptop
Show solution
Correct answers: C – Explanation:
Deployment-space promotion with versioning is the V4.7 composition reference. Manual copies, ad-hoc scoring, and laptop-only models all fail lifecycle. Source: Check Source
Question #8 - Integration and Data Fabric
An analyst at Kingswood Retail must query across Db2, Snowflake, and Cloudant without copying data.
Which V4.7 data-fabric capability fits?
A) Skip the request because cross-source queries are impossible
B) Copy all data into a single warehouse overnight
C) Use Watson Query (data virtualization) to expose federated views across the sources so analysts query them as if they were one database
D) Export CSVs from each source and merge in Excel
Show solution
Correct answers: C – Explanation:
Watson Query / data virtualization is the V4.7 data-fabric reference for federated queries. Overnight copies, refusal, and Excel merges all miss the feature. Source: Check Source
Question #9 - Integration and Data Fabric
A cross-source federation at Stonemere Insurance must push predicates to source engines for performance.
Which V4.7 integration practice fits?
A) Disable pushdown and pull full tables across the network
B) Rely on Watson Query’s cost-based optimization plus source-specific optimizations (predicate pushdown, join ordering) and verify execution plans for hot queries
C) Query each source independently and merge in memory without optimization
D) Skip federation because pushdown is complex
Show solution
Correct answers: B – Explanation:
Pushdown and cost-based optimization is the V4.7 federation performance reference. Disabling pushdown, full-table pulls, and skipping all fail federation at scale. Source: Check Source
Question #10 - Security and Multitenancy
A multi-team CP4D V4.7 deployment at Plumford Insurance must keep team A’s notebooks and datasets isolated from team B.
Which V4.7 multitenancy primitive fits?
A) Rely on filename conventions to separate teams
B) Put every team in a single shared project
C) Disable project-level role controls
D) Create separate CP4D projects for team A and team B with membership scoped to each team, using deployment spaces for promoted assets to further control downstream access
Show solution
Correct answers: D – Explanation:
Per-team projects plus deployment-space scoping is the V4.7 multitenancy reference. Shared projects, disabled controls, and name-based separation all fail isolation. Source: Check Source
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Exam mode & learn mode · Score by objective · Updated April 17, 2026
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What the C9006000 cloudpak data v4 architect exam measures
- Plan and size CP4D clusters, storage tiers, node labels, and capability resource requirements to deliver a data platform that meets performance SLAs without over-provisioning
- Catalog and govern Watson Knowledge Catalog, business glossaries, data quality, and lineage to make enterprise data discoverable, trusted, and auditable across business units
- Compose and orchestrate Watson Studio, AutoAI, DataStage, and Db2 across pipelines to build end-to-end data and AI workflows that stay maintainable as they scale
- Federate and virtualize Watson Query, data virtualization, and cross-source federation to give analysts unified access to data without forcing a single physical location
- Isolate and control projects, deployment spaces, and role-based isolation to keep multi-tenant data workloads safely separated while sharing underlying infrastructure
How to prepare for this exam
- Review the official exam guide to understand every objective and domain weight before you begin studying
- Work through the relevant IBM Training learning path — ibm certified architect cloud pak for data v4 7 C9006000 — to cover vendor-authored material end-to-end
- Get hands-on inside IBM TechZone or a comparable sandbox so you can practice the console tasks, CLI commands, and APIs the exam expects
- Tackle a real-world project at your workplace, a volunteer role, or an open-source repository where the technology under test is actually in use
- Drill one exam objective at a time, starting with the highest-weighted domain and only moving on once you can teach it to someone else
- Study by objective in PowerKram learn mode, where every explanation links back to authoritative IBM documentation
- Switch to PowerKram exam mode to rehearse under timed conditions and confirm you consistently score above the pass mark
Career paths and salary outlook
Data platform architects are rare enough that recruiters pursue them across consulting and industry:
- Data Platform Architect — $145,000–$195,000 per year, designing enterprise data platforms on CP4D (Glassdoor salary data)
- Senior Analytics Architect — $135,000–$180,000 per year, leading analytics and AI platform work (Indeed salary data)
- Enterprise Data Architect — $150,000–$200,000 per year, owning data-platform strategy across the enterprise (Glassdoor salary data)
Official resources
Work through the official IBM Training learning path for this certification, which bundles videos, labs, and skill tasks aligned to every objective. The official exam page lists the full objective breakdown, prerequisite knowledge, and scheduling details.