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Python / FastAPI Basics Interview Questions

How do you use Pydantic models for data validation and what validation features does FastAPI support?

Pydantic is FastAPI's validation engine. Models are Python classes inheriting from BaseModel where each field is a type-annotated attribute. Pydantic validates on instantiation, raising ValidationError for invalid data. FastAPI catches this and returns a 422 automatically.

from pydantic import BaseModel, Field, field_validator, model_validator
from typing import Annotated
from datetime import datetime

class UserCreate(BaseModel):
    username: Annotated[str, Field(min_length=3, max_length=50)]
    email:    Annotated[str, Field(pattern=r"^[\w.-]+@[\w.-]+\.\w+$")]
    age:      Annotated[int, Field(ge=18, le=120)]  # >= 18 and <= 120
    password: str
    created_at: datetime = Field(default_factory=datetime.utcnow)

    # Field-level validator
    @field_validator("username")
    @classmethod
    def username_alphanumeric(cls, v: str) -> str:
        if not v.isalnum():
            raise ValueError("Username must be alphanumeric")
        return v.lower()

    # Cross-field validator (model level)
    @model_validator(mode="after")
    def passwords_match(self) -> "UserCreate":
        # example: confirm_password field check would go here
        return self

class UserRead(BaseModel):  # separate model for responses (no password)
    username: str
    email: str
    age: int
    created_at: datetime

    model_config = {"from_attributes": True}  # allows ORM object input

Common Field constraints: min_length, max_length, pattern, ge (≥), gt (>), le (≤), lt (<), multiple_of, min_items, max_items.

Best practice: use separate Pydantic models for input (UserCreate) and output (UserRead) to avoid accidentally exposing sensitive fields like passwords in responses.

What does Field(ge=18) enforce on an integer field in Pydantic?
Why is it a best practice to use separate Pydantic models for input and output?

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