ASTRA Tech Sales Associate programme
Exam objectives — ASTRA Tech Sales Associate
Cloud, security and AI solutions: commercial decision-making
Certification coming — not yet available
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Exam objectives — ASTRA Tech Sales Associate
About the proposed examination
The proposed credential assesses evidence-based first-line decisions in customer-facing technology discussions. It does not attest engineering ability, security-auditor status, achievement in a specific job or external accreditation.
| Exam detail | Proposed specification |
|---|---|
| Credential | ASTRA Tech Sales Associate |
| Programme | ASTRA Tech Sales Associate programme |
| Scored questions | 48 across 8 original cases |
| Duration | 120 minutes |
| Question types | Single choice; exact set; row mapping; ordering; numeric; staged auto-marked exam simulations |
| Exam simulations | 1 staged simulation with 6 scored questions |
| Performance-based questions | At least 4 structured, automatically marked items (included in totals) |
| Pass mark | Not set — beta evidence, ASTRA Ready review and founder approval pending |
| Certification status | Coming; no live credential examination or issuance |
Domain weights
| Domain | What the exam covers | Percent |
|---|---|---|
| D1.0 | Solution architecture, integration and operating boundaries | 14.6% |
| D2.0 | Cloud and solution economics | 14.6% |
| D3.0 | Security, assurance and operational trust | 12.5% |
| D4.0 | AI value, quality and safe-use decisions | 12.5% |
| D5.0 | Technical-commercial translation and defensible recommendations | 12.5% |
| D6.0 | Discovery, stakeholders and decision sequencing | 10.4% |
| D7.0 | Partner, channel and contracting boundaries | 10.4% |
| D8.0 | Evidence governance, approvals and responsible claims | 12.5% |
| TOTAL | 100.0% |
Percentages describe the proposed allocation of scored questions by primary domain. Example concepts illustrate scope; examination questions may use different products, facts and fictional organisations. This document contains no answer keys, cases or confidential questions.
Assessable objectives
D1.0 Solution architecture, integration and operating boundaries (14.6%)
D1.1 Given a scenario, trace the components supporting a customer-facing feature and distinguish their jobs.
Example concepts: application, connector, source system, identity. Source objective: FM-01-S1.
D1.2 Given a scenario, trace a user request to its confirmed business result and identify an unverified hand-off.
Example concepts: request, response, persisted result. Source objective: FM-01-S2.
D1.3 Given a scenario, interpret the sending and receiving API contracts and identify necessary field or meaning translations.
Example concepts: field types, lookup rules, pagination. Source objective: FM-01-S3.
D1.4 Given a scenario, distinguish authentication from permission for a named operation.
Example concepts: identity, scope, denied action. Source objective: FM-01-S4.
D1.5 Given a scenario, classify a cloud offer by what its provider and customer actually operate.
Example concepts: SaaS, PaaS, IaaS, separate services. Source objective: FM-01-S5.
D1.6 Given a scenario, interpret freshness and response-time evidence without conflating display responsiveness with current data.
Example concepts: snapshot age, update cadence. Source objective: FM-01-S6.
D2.0 Cloud and solution economics (14.6%)
D2.1 Given a scenario, compare cloud service boundaries against the customer’s ability to operate them before comparing prices.
Example concepts: operating obligations, staffing dependency. Source objective: FM-02-S1.
D2.2 Given a scenario, separate software rights, one-time delivery and recurring service commitments.
Example concepts: entitlement, acceptance, service scope. Source objective: FM-02-S2.
D2.3 Given a scenario, derive a billable quantity under the specified usage rule.
Example concepts: meter, unit, rounding. Source objective: FM-02-S3.
D2.4 Given a scenario, compare alternative user quantities across an equivalent term.
Example concepts: seat-months, contracted period. Source objective: FM-02-S4.
D2.5 Given a scenario, calculate an in-scope first-year cost without omitting or double-counting included work.
Example concepts: fees, internal allocation, cash cost. Source objective: FM-02-S5.
D2.6 Given a scenario, interpret commitment thresholds and identify unverified financial assumptions.
Example concepts: minimums, overage, unknown work. Source objective: FM-02-S6.
D2.7 Given a scenario, distinguish a spending notification from an enforced limit and sequence the approved response.
Example concepts: billing lag, continuity, approval. Source objective: FM-02-S7.
D3.0 Security, assurance and operational trust (12.5%)
D3.1 Given a scenario, translate a described security event into the customer consequence and a testable requirement.
Example concepts: confidentiality, integrity, availability. Source objective: FM-03-S1.
D3.2 Given a scenario, evaluate who is included in an identity-control test and identify coverage exceptions.
Example concepts: population, enforcement, exclusions. Source objective: FM-03-S2.
D3.3 Given a scenario, trace the actual location and access boundary for each relevant data store or path.
Example concepts: primary, logs, backup, exports. Source objective: FM-03-S3.
D3.4 Given a scenario, distinguish service restoration time from the point to which data can be restored.
Example concepts: RTO, RPO, evidence timestamp. Source objective: FM-03-S4.
D3.5 Given a scenario, match assurance evidence to the exact control, population, period and claim.
Example concepts: control scope, expiry, test. Source objective: FM-03-S5.
D4.0 AI value, quality and safe-use decisions (12.5%)
D4.1 Given a scenario, select a bounded AI use case consistent with approved inputs, reviewer availability and action authority.
Example concepts: drafting, deterministic alternatives, exclusions. Source objective: FM-04-S1.
D4.2 Given a scenario, separate retrieved source evidence from model-generated content and unsupported assertions.
Example concepts: grounding, provenance, retention. Source objective: FM-04-S2.
D4.3 Given a scenario, choose task-sufficient inputs under the stated data-use contract.
Example concepts: minimisation, identity routing, prohibited fields. Source objective: FM-04-S3.
D4.4 Given a scenario, test an AI answer against the cited evidence rather than confidence language.
Example concepts: source support, correction, review. Source objective: FM-04-S4.
D4.5 Given a scenario, calculate a measured workflow outcome after human review and rework.
Example concepts: eligible volume, quality denominator, time. Source objective: FM-04-S5.
D4.6 Given a scenario, distinguish an instruction embedded in content from valid execution authority.
Example concepts: injection, attempted action, gateway result. Source objective: FM-04-S6.
D5.0 Technical-commercial translation and defensible recommendations (12.5%)
D5.1 Given a scenario, construct a bounded integration or data-flow claim from the supplied component evidence.
Example concepts: sender, receiver, data, unknown paths. Source objective: FM-01-S7.
D5.2 Given a scenario, identify a test and delivery-scope decision before promising the customer an integrated outcome.
Example concepts: acceptance sequence, included services. Source objective: FM-01-S8.
D5.3 Given a scenario, recommend a conditional offer using operating fit, cost and documented gates.
Example concepts: price boundary, open dependencies. Source objective: FM-02-S8.
D5.4 Given a scenario, turn a vague customer request into a measurable acceptance criterion.
Example concepts: actor, scope, timing, evidence. Source objective: FM-05-S2.
D5.5 Given a scenario, calculate operational effort while separating modeled capacity from realised cash savings.
Example concepts: baseline, mix, exclusion. Source objective: FM-05-S4.
D6.0 Discovery, stakeholders and decision sequencing (10.4%)
D6.1 Given a scenario, identify the highest-value unresolved discovery question behind a customer request.
Example concepts: outcome, owner, integration dependency. Source objective: FM-05-S1.
D6.2 Given a scenario, identify who can approve each business, technical and commercial decision.
Example concepts: seniority versus authority, gate. Source objective: FM-05-S3.
D6.3 Given a scenario, sequence the missing facts and decisions required before committing a proposal.
Example concepts: dependencies, owner, next action. Source objective: FM-05-S5.
D7.0 Partner, channel and contracting boundaries (10.4%)
D7.1 Given a scenario, distinguish reseller, referral and separately contracted service responsibilities.
Example concepts: contract, bill, implement, support. Source objective: FM-05-S6.
D7.2 Given a scenario, apply permission and approval limits to a partner claim or customer message.
Example concepts: discount, customer choice, exception. Source objective: FM-05-S7.
D7.3 Given a scenario, prepare a conditional buyer/partner handover naming evidence, unresolved gates and next owners.
Example concepts: scope, review, hand-off. Source objective: FM-05-S8.
D8.0 Evidence governance, approvals and responsible claims (12.5%)
D8.1 Given a scenario, identify which assurance document can be shared with a recipient under its stated conditions.
Example concepts: approval, named contacts, channel. Source objective: FM-03-S6.
D8.2 Given a scenario, sequence the authorised response to a reported incident without inventing impact findings.
Example concepts: observed facts, owner, wording. Source objective: FM-03-S7.
D8.3 Given a scenario, classify exact questionnaire claims by support, contradiction or missing evidence.
Example concepts: status, exception, next owner. Source objective: FM-03-S8.
D8.4 Given a scenario, apply version-bound human approval to a changed draft or high-impact action.
Example concepts: approval record, changed content, gateway. Source objective: FM-04-S7.
D8.5 Given a scenario, convert AI marketing claims into a bounded proposal with evidenced results and open release gates.
Example concepts: quality sample, retention, permitted stage. Source objective: FM-04-S8.
Important candidate notes
A programme learning-completion record is not the assessed credential. Completion of ASTRA Ready lessons is not required for assessment-only entry where eligibility conditions are met.
Practice assessments, module exercises, formative simulations and planned mocks are disclosed learning activities; their results do not award a credential or predict a pass.
The final blueprint, examination time, availability and pass standard require pilot evidence, ASTRA Ready review, founder approval, examination-engine tests and confirmation of the issuing legal entity.
Assessment examples are fictional and judge decisions within supplied contracts and artefacts; they do not substitute for laws, vendor attestations or live system validation.