JNCIA-DevOps Data Serialization Practice Test 5 — 15 Questions

Question 1

When serializing Python data structures to JSON for Junos automation payloads, which THREE parameters can be passed to json.dumps() to control formatting and readability? (Choose three)

Accepted Answer

separators. Option B, separators, is correct because json.dumps() accepts a (item_separator, key_separator) tuple that controls the characters placed between JSON elements, letting you produce compact output such as (',', ':') for smaller Junos payloads. Option C, sort_keys, is correct because setting sort_keys=True makes json.dumps() emit object keys in sorted order, which yields deterministic, diff-friendly JSON for configuration and automation scripts. Option E, indent, is correct because passing indent (for example indent=2 or indent=4) pretty-prints the JSON with newlines and that many spaces per level, improving readability of Junos payloads. Option A, max_depth, is not a valid json.dumps() parameter; Python's json module has no depth-limiting argument. Option D, strict_mode, is also not a json.dumps() parameter; the closest real argument is strict, which controls whether control characters inside strings are allowed, but 'strict_mode' itself does not exist.

Answer

max_depth

Answer

strict_mode

Question 2

Which TWO of the following are valid scalar values in a YAML document? (Choose two)

Accepted Answer

Integer. In YAML, a scalar is a single, indivisible value rather than a collection, so option B (Integer) is correct because values such as 42 or -7 are scalar types. Option D (String) is also correct because text values like "hello" or 'world' are scalars, whether quoted or plain. By contrast, option A (Mapping) is a collection of key-value pairs, option C (Sequence) is an ordered list of items, and option E (Block) refers to a block-style presentation of collections or scalars, not a scalar value itself, so none of those are valid scalar values.

Answer

Mapping

Answer

Sequence

Answer

Block

Question 3

Which TWO Python standard library modules are commonly combined when a script needs to fetch web content (such as Junos REST API data) and parse the resulting payload? (Choose two)

Accepted Answer

json. Option D, urllib.request, is correct because it is the standard library module used to open URLs and perform HTTP requests, so it can fetch web content such as Junos REST API data. Option A, json, is correct because REST API payloads are typically returned in JSON format, and the json module parses that response text into Python dictionaries and lists. Together, urllib.request retrieves the raw response and json.loads() converts it into usable Python objects. Option B, yaml, is not needed here since the scenario specifies web content/REST payloads, which are JSON rather than YAML. Option C, socket, is a lower-level networking module and does not provide convenient HTTP fetching or payload parsing. Option E, pickle, is for serializing/deserializing Python objects and is not used to parse JSON API responses.

Answer

yaml

Answer

socket

Answer

pickle

Question 4

Which TWO characters or sequences are valid in YAML for representing lists or sequences? (Choose two)

Accepted Answer

Hyphen followed by a space (- ). Option A is correct because YAML uses a hyphen followed by a space ("- ") as block sequence indicator, where each list item is introduced by that dash at the same indentation level. Option E is correct because YAML supports flow sequences written with square brackets, e.g. [one, two, three], representing a list inline. Option B is incorrect because a semicolon followed by a space is not a YAML list indicator; semicolons are not used for sequences in YAML. Option C is incorrect because angle brackets are not YAML sequence syntax; they are used in other markup languages or as plain scalar characters. Option D is incorrect because curly braces denote flow mappings (key-value pairs), not sequences.

Answer

Semicolon followed by a space (; )

Answer

Angle brackets (< >)

Answer

Curly braces ({ })

Question 5

When serializing complex nested Python dictionaries to JSON, which THREE data types will cause a TypeError unless handled by a custom default encoder function? (Choose three)

Accepted Answer

Python set objects. Option B (Python set objects) is correct because the json module has no native representation for sets, so json.dumps() raises TypeError: Object of type set is not JSON serializable unless a custom default function converts it (e.g., to a list). Option C (Custom class instances) is correct because arbitrary user-defined objects are not JSON-serializable by default; json.dumps() only knows how to encode dict, list, tuple, str, int, float, bool, and None, so a custom default handler must return a serializable representation such as obj.__dict__. Option E (Complex numbers) is correct because Python's complex type is not among the supported JSON types and json.dumps(1+2j) raises TypeError, requiring a custom encoder to convert it (e.g., to a string or a [real, imag] list). Option A (Standard string objects) is not correct because str is natively supported and serializes directly to a JSON string. Option D (Boolean values) is not correct because bool is natively supported and serializes to JSON true/false.

Answer

Standard string objects

Answer

Boolean values

Question 6

When writing an automation script that converts data formats between YAML and JSON for Junos configuration pipelines, which THREE potential pitfalls should an engineer account for? (Choose three)

Accepted Answer

JSON integer keys are automatically converted to strings in Python dictionaries.. Option C is correct because JSON object keys are always strings, so when Python's json module loads a JSON document, any key that appears numeric (e.g., 1) becomes the string '1', which can break lookups or round-trips if the script expects an integer key. Option D is correct because JSON has no comment syntax, so any YAML comments (# ...) are discarded by the YAML parser and cannot be represented in the resulting JSON output. Option E is correct because YAML 1.1 parsers interpret unquoted scalars such as 'yes'/'no' as booleans and leading-zero values like 012 as octal integers, silently changing the data type unless the scalars are explicitly quoted. Option A is wrong because YAML does not guarantee lossless round-trip conversion for arbitrary Python objects (custom types, tuples, sets, etc. require tags or custom representers). Option B is wrong because JSON has no native support for YAML anchors and aliases; those are YAML-specific features that must be resolved before JSON serialization.

Answer

YAML automatically guarantees lossless round-trip conversion for all Python objects.

Answer

JSON files natively support YAML anchors and aliases.

Question 7

An automation engineer is writing a Python script to parse a Juniper Junos NETCONF reply received in XML format and needs to convert it into a JSON structure for easier manipulation in a web application. Which built-in Python module is primarily used to handle JSON data serialization and deserialization?

Accepted Answer

json. The 'json' module is the standard built-in Python library used for parsing, serializing, and deserializing JSON data.

Answer

pickle

Answer

yaml

Answer

xml.etree

Question 8

An engineer receives a YAML configuration snippet for a PyEZ automation script, but the script fails with a parser error. Upon inspection, the error is caused by improper indentation levels. Which rule must be strictly followed regarding indentation in YAML syntax?

Accepted Answer

Spaces must be used for indentation, and tabs are strictly forbidden.. YAML requires the use of space characters for indentation. Tab characters are strictly forbidden for indentation purposes.

Answer

Indentation is completely optional if curly braces are used.

Answer

Tabs must be used consistently for every level of indentation.

Answer

Indentation must always be exactly four spaces per level.

Question 9

You are writing a Python script using the PyEZ library to retrieve interface facts from a Juniper EX Series switch. The resulting data structure is stored in a Python dictionary. You need to dump this dictionary into a YAML file so it can be read by an Ansible playbook later. Which PyYAML function should you use to convert the Python dictionary into a YAML-formatted string or file stream?

Accepted Answer

yaml.dump(). yaml.dump() is the standard PyYAML function used to serialize Python objects (such as dictionaries) into a YAML-formatted stream or string.

Answer

yaml.load()

Answer

yaml.serialize()

Answer

yaml.safe_load()

Question 10

An automation developer is examining a JSON response payload returned by a Juniper device via a REST API call. Which valid JSON data type is used to represent an ordered list of values enclosed within square brackets?

Accepted Answer

Array. In JSON, an array is an ordered list of values enclosed in square brackets. It can contain multiple values of any data type, including strings, numbers, objects, and other arrays.

Answer

String

Answer

Object

Answer

Boolean

Question 11

During script development, an engineer encounters a YAML file containing comments and multiple configuration documents separated by document markers. Which characters are used to denote the start of a new document within a single YAML file stream?

Accepted Answer

---. In YAML, three dashes (---) indicate the start of a document, while three dots (...) indicate the end of a document.

Answer

===

Answer

...

Answer

###

Question 12

An automation engineer is reviewing data serialization formats used within Junos automation workflows. Which TWO statements are correct regarding JSON syntax and data types? (Choose two)

Accepted Answer

JSON object keys must be enclosed in double quotes.. Option C is correct because JSON (RFC 8259) requires that every object member name, i.e., every key, be a string enclosed in double quotes, so keys like {"host": "router1"} are valid while {host: "router1"} is not. Option D is correct because JSON defines exactly six value types — string, number, object, array, boolean (true/false), and null — and the literal keyword null is the valid representation of the null data type, commonly used in Junos automation payloads to indicate an absent or empty value. Option A is incorrect because JSON strings must use double quotes; single quotes are not valid JSON (they are only tolerated by some lenient parsers such as Python's ast.literal_eval or YAML). Option B is incorrect because standard JSON has no comment syntax at all, so a '#' character would be invalid — comments are only available in JSON5, JSONC, or YAML. Option E is incorrect because JSON forbids trailing commas after the last element of an array or the last member of an object, and such input will fail strict parsing.

Answer

Single quotes can be used interchangeably with double quotes for strings.

Answer

JSON supports inline comments starting with the '#' symbol.

Answer

Trailing commas are permitted in both arrays and objects.

Question 13

An engineer writes a Python script that loads a JSON configuration file using the json.load() method. The file contains configuration parameters for a Junos device. If the JSON file contains a trailing comma at the end of the last element in an array, what will happen when the script executes?

Accepted Answer

The script will raise a JSONDecodeError.. Standard JSON specification is strict and does not allow trailing commas. Attempting to parse it will raise a json.decoder.JSONDecodeError.

Answer

The script will successfully parse the file and ignore the trailing comma.

Answer

The script will throw a TypeError during execution.

Answer

The script will convert the trailing comma into a null value automatically.

Question 14

An automation developer is designing a script that reads routing policy parameters from a configuration file. Which TWO data formats are natively supported and commonly used across Junos automation toolchains like PyEZ, Ansible, and REST APIs for data serialization? (Choose two)

Accepted Answer

YAML. YAML (A) is correct because Junos automation toolchains such as Ansible and PyEZ commonly use YAML for structured configuration data, and Junos devices can consume YAML-formatted data in automation workflows. JSON (C) is correct because Junos REST APIs and PyEZ return and accept JSON as a native data serialization format for structured routing policy parameters. BMP (B) is incorrect because it is a binary image/monitoring protocol, not a data serialization format. RTF (D) is incorrect because it is a rich text document format, not used for automation data serialization. EXE (E) is incorrect because it is an executable file format, not a structured data format.

Answer

BMP

Answer

RTF

Answer

EXE

Question 15

A network automation engineer is using a Python script with the PyYAML library to load a YAML file that defines a Junos device's configuration. The script needs to ensure that the loaded data is a Python dictionary and that no arbitrary Python objects are instantiated during parsing. Which function should the engineer use to safely load the YAML content?

Accepted Answer

yaml.safe_load(stream). Using yaml.safe_load ensures that only standard Python data types are constructed from the YAML input. It prevents the instantiation of arbitrary objects and mitigates security risks such as code execution. This is the recommended approach for parsing configuration files in automation scripts.

Answer

yaml.load(stream, Loader=yaml.FullLoader)

Answer

yaml.load(stream, Loader=yaml.BaseLoader)

Answer

yaml.load(stream, Loader=yaml.UnsafeLoader)