The correct lambda accesses the ‘age’ value from the dictionary using the key `’age’`. Option 0 uses `.get()`, which works but is unnecessary for a known key. Option 2 tries attribute access (`.age`), which is for objects, not dictionaries. Option 3 references an index `[1]`, which is irrelevant for dictionary access.

This approach uses specific exception handling (`FileNotFoundError` for missing file, `JSONDecodeError` for corruption) which is a best practice. It then uses an `else` block to execute code only if the `try` block succeeded, providing a clear flow for the default values logic. The other options are either too broad (generic except), incomplete (file existence doesn’t prevent parse errors), or unnecessarily complex (nested try blocks).

In a Python function definition, parameters must be defined in this order: standard positional parameters, then `*args` (for variable positional arguments), then `**kwargs` (for variable keyword arguments). Therefore, any standard parameter must come before `*args`. The syntax `def func(*args, standard_param, **kwargs)` is invalid because a standard positional parameter cannot follow `*args`.

The key distinction is that a static method is just a regular function that happens to live in a class’s namespace for organizational purposes. It receives no automatic first argument. A class method, by contrast, receives the class (`cls`) as its first argument, allowing it to access or modify class state. Option 1 is too strong—a static method *can* access class attributes via the class name, but it isn’t passed them automatically.

The `or` operator in Python returns the first truthy operand, or the last falsy one if all are falsy. An empty string (`””`) is considered falsy in a Boolean context. Therefore, if `data` is an empty string, the expression evaluates to `default_value`. This is a common Python idiom for providing a fallback value.

The issue is that `item` is a local variable referencing the current value. Reassigning it (`item = item * 2`) changes what `item` points to, not the original list element. Using `enumerate()` provides both the index (`i`) and the value, allowing direct assignment back into the list at the correct position (`my_list[i] = item * 2`). This is a clear and readable solution for this specific in-place modification intent.

PEP 8 recommends that function names should be lowercase, with words separated by underscores. More specifically, for functions that return a Boolean (predicate functions), it is a strong convention to prefix the name with `is_` or `has_` to make the return type clear. Therefore, `is_user_active` is the most Pythonic and clear choice.

Using the `@property` decorator is the Pythonic way to implement managed attributes. It allows you to keep the simple attribute access syntax (`rect.width = 5`) while hiding the validation logic inside the setter method. This maintains encapsulation, is clean for the user, and centralizes the validation rule. Option 4 is a Java-style pattern that is non-idiomatic in Python, and option 1 offers no protection.

The output is `10 15`. The key concept here is a **closure**. The `lambda x: x * n` is an inner function that captures and remembers the value of the argument `n` from the enclosing `multiplier` function’s scope at the time `multiplier` was called. This is why `double` remembers `n=2` and `triple` remembers `n=3`. While it uses a lambda, the core test is about the closure behavior.

The correct lambda accesses the ‘age’ value from the dictionary using the key `’age’`. Option 0 uses `.get()`, which works but is unnecessary for a known key. Option 2 tries attribute access (`.age`), which is for objects, not dictionaries. Option 3 references an index `[1]`, which is irrelevant for dictionary access.0