How we assess
Every applicant goes through 5 gates: application, an AI-assisted screen, a live technical assessment scored by a practice lead, a client-fit interview, and reference and credential checks. The questions below come from the live assessment. We score answers on reasoning and trade-offs, not on recalling definitions.
20 technical questions with model-answer hints
Questions 1–10 (role-specific)
Question 1: How do AI assistants choose which sources to cite?
Model-answer hint
Retrieval from indexed pages, authority, clarity, freshness, consistent facts; admits what is uncertain.
Question 2: A JavaScript site is not getting indexed. What do you check?
Model-answer hint
Rendering, server-side rendering, robots rules, canonicals, internal links, URL inspection.
Question 3: How do you structure content so AI systems cite it?
Model-answer hint
Answer-first paragraphs, definitions, clear entities, FAQs, schema, original data.
Question 4: Which schema types matter most for a B2B services site?
Model-answer hint
Organization, Service, FAQPage, BreadcrumbList, Article; accuracy over volume.
Question 5: How do you do Arabic keyword research?
Model-answer hint
Native phrasing, dialect variants, transliterated terms, separate intent analysis; never translation.
Question 6: Organic traffic dropped after a core update. What next?
Model-answer hint
Segment by page type, compare competitors, review quality, avoid panic changes.
Question 7: How do you measure GEO success?
Model-answer hint
Citation share for priority prompts, brand mentions, referral traffic from AI tools, assisted conversions.
Question 8: How do you implement hreflang for Arabic and English?
Model-answer hint
Reciprocal tags, x-default, consistent URLs, sitemap-based implementation.
Question 9: What makes digital PR effective today?
Model-answer hint
Original data, expert commentary, relevance over link volume.
Question 10: How do you prioritise an SEO backlog?
Model-answer hint
Impact, effort and confidence, tied to revenue-driving pages.
Questions 11–20 (Marketing core)
Question 11: How do you connect marketing activity to revenue?
Model-answer hint
tracking plan, CRM integration, known attribution limits, incrementality tests, definitions agreed with sales.
Question 12: How do you design a marketing experiment?
Model-answer hint
hypothesis, primary metric, sample size, duration, stopping rule, documented result.
Question 13: How do you adapt a global campaign for Gulf audiences?
Model-answer hint
cultural review, Arabic-first copy, local platforms such as Snapchat, local moments, regulatory checks.
Question 14: How do you agree with sales on what counts as a qualified lead?
Model-answer hint
shared definitions, follow-up SLAs, feedback loops, joint review of outcomes.
Question 15: When do you write in Khaleeji dialect versus Modern Standard Arabic?
Model-answer hint
channel, audience and formality, guided by a brand voice guide; never translated English.
Question 16: How do you use generative AI in marketing without harming the brand?
Model-answer hint
governance rules, human review, fact-checking, disclosure, no confidential data in tools.
Question 17: Which metrics do you report to a CMO every week?
Model-answer hint
pipeline or revenue contribution, CAC, conversion rates, spend pacing; few vanity metrics.
Question 18: How do you brief a creative or content team?
Model-answer hint
objective, audience insight, one message, mandatory elements, success measure.
Question 19: How do you decide to stop a channel or programme?
Model-answer hint
thresholds agreed in advance, marginal returns, opportunity cost, evidence.
Question 20: Describe a campaign that failed and what you learned.
Model-answer hint
honest diagnosis, the data used, the specific change made afterwards.
Interviewer tip: Ask for a real example after every answer. Strong candidates describe what they built, what broke and what they changed.
Practical task
Timebox 2 hours. Audit a provided bilingual B2B site using a crawl export, a Search Console sample and 20 priority buyer questions. Deliver the top 10 technical fixes, an AI-visibility baseline for 5 of the questions with cited sources, and a 90-day plan covering content, schema and digital PR with expected impact.
How to run it Share the brief 24 hours ahead or run it live. Ask the candidate to talk through trade-offs as they work; reasoning matters more than a finished artefact.
Red flags our practice leads watch for
- Promises guaranteed rankings or AI placements.
- Relies on link buying or scaled low-quality content.
- Treats Arabic SEO as translated English keywords.
- Cannot explain technical indexing problems.
- Universal flag: Vague answers that never reach a concrete system, number or decision the candidate personally owned.
The scorecard we use
Score each dimension 1–4 (1 = no evidence, 2 = partial, 3 = solid, 4 = exceptional):
| Dimension | Weight | What a 4 looks like |
|---|---|---|
| Technical depth, production judgement and security | 40% | Explains how and why, designs for failure and sensitive data, and knows each tool’s limits |
| Practical task and problem solving | 30% | Delivers a working solution with tests and documented decisions |
| Communication and documentation | 20% | Explains clearly to technical and non-technical audiences, in English or Arabic |
| References and regional context | 10% | References confirm the claimed scope; knows the frameworks and data-residency rules that apply |
Pass bar: weighted average of 3.0 out of 4 or higher, with no dimension below 2. The practice lead approves the final result.