i am practising for OA, and i got hand on one of t...

Yes. From the screenshots, the intended solution is clear enough to reconstruct the algorithm, including a couple of hidden-test traps. But to give you drop-in C++ that uses the exact existing function signatures and requires zero changes to main.cpp, I need the starter declarations from:

include/returns/models.hpp
include/returns/engine.hpp
and ideally the current src/returns/engine.cpp

Paste those files here or upload screenshots of them. I don't want to invent a signature that differs from the OA skeleton.

The required behavior from your screenshots is this:

1. Category policy first

   • category absent from category_rules.json → REJECT, risk_score = null, reason CATEGORY_NON_RETURNABLE
   • category has returnable == false → same
   • days_since_purchase > allowed_return_window_days → REJECT, risk_score = null, reason RETURN_WINDOW_EXPIRED
   • equality with the return window is allowed.

2. Linked-account return history

   • Add the account's own return_history_count.
   • Add every account in its linked-account group.
   • Links are effectively transitive. The example says:
     • u101 <-> u102
     • u101 <-> u103
     • therefore u101, u102, u103 are one group.
   • So this should be treated as a connected-components/DSU problem, not merely "look at direct links."
   • Missing account profile → history count 0.
   • Account with no links → use only its own profile.

3. Risk scoring

   • Process every rule in scoring_rules.json.
   • Read the attribute named by the rule.
   • Find the range containing its value.
   • Ranges appear to be inclusive:

     text
     min <= value <= max

   • Add that range's points.
   • For a return-history rule, use the aggregated linked-account history, not the requesting account's raw history.
   • Sum all rules.
   • Clamp:

     cpp
     risk_score = std::clamp(total, 0, 100);

4. Decision According to the shown configuration/spec:

   text
   0-49    AUTO_APPROVE
   50-74   MANUAL_REVIEW
   75-100  REJECT

   However, since decision_bands.json exists, the implementation should use that configuration rather than hard-code these numbers.

   Reasons:

   text
   MANUAL_REVIEW -> MEDIUM_RISK_SCORE
   REJECT        -> HIGH_RISK_SCORE
   AUTO_APPROVE  -> no reason field

A robust implementation of the hardest part—the linked-account grouping—will look roughly like this:

cpp
class DSU {
public:
    std::unordered_map<std::string, std::string> parent;
    std::unordered_map<std::string, int> rank;

    void add(const std::string& x) {
        if (!parent.count(x)) {
            parent[x] = x;
            rank[x] = 0;
        }
    }

    std::string find(const std::string& x) {
        auto it = parent.find(x);
        if (it == parent.end()) {
            add(x);
            return x;
        }

        if (parent[x] != x) {
            parent[x] = find(parent[x]);
        }
        return parent[x];
    }

    void unite(const std::string& a, const std::string& b) {
        add(a);
        add(b);

        std::string ra = find(a);
        std::string rb = find(b);

        if (ra == rb)
            return;

        if (rank[ra] < rank[rb])
            std::swap(ra, rb);

        parent[rb] = ra;

        if (rank[ra] == rank[rb])
            ++rank[ra];
    }
};

Then aggregate each component once:

cpp
std::unordered_map<std::string, int> historyByAccount;

for (const auto& profile : profiles) {
    historyByAccount[profile.account_id] = profile.return_history_count;
}

DSU dsu;

for (const auto& link : links) {
    dsu.unite(link.primary_account_id,
              link.related_account_id);
}

for (const auto& profile : profiles)
    dsu.add(profile.account_id);

std::unordered_map<std::string, int> componentHistory;

for (const auto& [accountId, count] : historyByAccount) {
    componentHistory[dsu.find(accountId)] += count;
}

For an incoming request:

cpp
dsu.add(request.account_id);

int aggregateReturnHistory = 0;

std::string root = dsu.find(request.account_id);

auto it = componentHistory.find(root);
if (it != componentHistory.end()) {
    aggregateReturnHistory = it->second;
}

The score evaluation should be generic rather than hardcoding account_age_days:

cpp
int calculatePoints(
    double value,
    const ScoringRule& rule)
{
    for (const auto& range : rule.ranges) {
        if (value >= range.min && value <= range.max) {
            return range.points;
        }
    }

    return 0;
}

And conceptually:

cpp
int totalScore = 0;

for (const auto& rule : scoringRules) {
    double value = 0;

    if (rule.rule == "return_history_count") {
        value = aggregateReturnHistory;
    }
    else if (rule.rule == "account_age_days") {
        value = request.account_age_days;
    }
    else if (rule.rule == "order_value_usd") {
        value = request.order_value_usd;
    }

    totalScore += calculatePoints(value, rule);
}

int riskScore = std::clamp(totalScore, 0, 100);

There is one thing I don't want to guess: how the starter model represents optional values such as order_value_usd and account_age_days. It might use std::optional, raw numeric defaults, or something else. Likewise, I need to see whether the engine API is something like:

cpp
ReturnResult evaluateReturn(...);

or

cpp
std::vector<ReturnResult> processReturns(...);

or a ReturnEngine::evaluate(...) member function.

Send me models.hpp, engine.hpp, and the existing engine.cpp. Then I can write the exact replacement implementation, preserving main.cpp unchanged and accounting for edge cases such as transitive links, missing profiles, inclusive boundaries, duplicate links, unknown accounts, missing categories, score clamping, and reason omission for AUTO_APPROVE.