JNCIA-DevOps Data Serialization Practice Test 2 — 15 Questions

Question 1

An automation engineer stores Junos device facts in a YAML file used by a PyEZ script. The file currently contains:

interfaces:
  - ge-0/0/0
  - ge-0/0/1

management_ip: 192.0.2.10

The script needs to add a second management address, 192.0.2.11, as a list while keeping the file valid YAML. Which YAML snippet correctly represents management_ip as a two-element sequence?

Accepted Answer

management_ip:
  - 192.0.2.10
  - 192.0.2.11. YAML block sequences use a dash followed by a space for each item, indented beneath the mapping key. That syntax yields a Python list of two strings, which the PyEZ script can iterate to apply both management addresses. Flow style is valid but does not match the block-style file the engineer is maintaining.

Answer

management_ip: [192.0.2.10, 192.0.2.11]

Answer

management_ip: 192.0.2.10, 192.0.2.11

Answer

management_ip:
  192.0.2.10
  192.0.2.11

Question 2

A network automation engineer exports the running configuration of a Junos device to a file named `backup.conf` and wants to parse it with a Python script using a data serialization library so that interface names, descriptions, and IP addresses can be accessed programmatically. Which data serialization format is the most appropriate for this task?

Accepted Answer

XML. Junos exposes both configuration and operational state in XML, which is a structured serialization designed for hierarchical data. Because XML maps directly to the Junos configuration tree, a Python script can parse `show configuration | display xml` output and navigate elements deterministically. JSON and YAML require conversion from a native Junos representation, and CSV cannot express the nesting, so XML is the appropriate format here.

Answer

CSV

Answer

JSON

Answer

YAML

Question 3

An automation engineer is comparing JSON and YAML for serializing Junos device inventory data that will be consumed by multiple tools. Which TWO statements correctly describe intrinsic characteristics of these formats? (Choose two.)

Accepted Answer

YAML supports anchors and aliases that allow reusing repeated inventory structures within the same document.. YAML's indentation-based hierarchy and its anchor/alias mechanism are two intrinsic features that distinguish it from JSON. JSON uses explicit delimiters and has no comment or anchor support, while YAML is a superset of JSON and adds readability and reuse features. These characteristics matter when choosing a format for inventory data that must be both human-editable and machine-consumable across multiple tools.

Answer

JSON natively supports binary data through a dedicated base64 scalar type.

Answer

JSON supports comments, which makes it easier to document inventory fields inline.

Answer

JSON is a superset of YAML, so any valid YAML document is also valid JSON.

Question 4

An engineer needs to represent an ordered list of IP addresses in a YAML configuration file for a Junos automation playbook. Which valid YAML syntax correctly defines a sequence of items?

Accepted Answer

ip_addresses:
  - 192.168.1.1
  - 192.168.1.2. In YAML, lists or sequences are represented using a hyphen followed by a space (-) at the beginning of each line.

Answer

ip_addresses = {192.168.1.1, 192.168.1.2}

Answer

ip_addresses: [192.168.1.1: 192.168.1.2]

Answer

ip_addresses:
  * 192.168.1.1
  * 192.168.1.2

Question 5

A Python script retrieves interface statistics from a Junos device in JSON format, modifies a threshold value, and needs to output it back into a formatted JSON string with an indentation level of 4 spaces for readability. Which Python snippet achieves this?

Accepted Answer

json.dumps(data, indent=4). The json.dumps() method accepts an 'indent' parameter which specifies the number of spaces to use for pretty-printing the resulting JSON string.

Answer

json.format(data, spacing=4)

Answer

json.dump(data, indent=4)

Answer

json.stringify(data, spaces=4)

Question 6

An automation engineer is writing a Python script that takes a complex nested Python dictionary containing Junos telemetry parameters and converts it into a YAML string. Which PyYAML function should be used?

Accepted Answer

yaml.safe_dump(). yaml.safe_dump() converts a Python object into a YAML-formatted string safely, avoiding the security risks associated with the base yaml.dump().

Answer

yaml.export()

Answer

yaml.serialize_string()

Answer

yaml.to_yaml()

Question 7

A network automation script is reading a YAML configuration file containing Junos device credentials. The script encounters an unhandled exception when attempting to load the file using PyYAML. Upon inspection, the error is caused by a tab character used for indentation instead of spaces. Which YAML specification rule does this violate?

Accepted Answer

Tab characters are strictly forbidden for indentation in YAML.. YAML 1.2 specifications explicitly prohibit the use of the tab character for indentation. All indentation whitespace must consist exclusively of space characters to maintain hierarchy definition.

Answer

Comments must start with a double forward slash (//).

Answer

YAML files require all keys to be wrapped in double quotes.

Answer

Top-level keys must start with a hyphen (-) character.

Question 8

You are writing a Python automation script that processes JSON output from a Junos device's 'show interfaces' operational command via PyEZ. One of the interface descriptions contains raw control characters. When the script attempts to dump this data to a JSON string using json.dumps(), it raises a ValueError. Which parameter should you pass to json.dumps() to handle non-printable or out-of-range character encoding gracefully?

Accepted Answer

ensure_ascii=False. The json.dumps() function in Python includes an 'ensure_ascii' parameter. When set to False, it allows non-ASCII characters to be emitted directly without raising serialization errors or escaping them into Unicode escape sequences.

Answer

skipkeys=True

Answer

check_circular=False

Answer

allow_nan=True

Question 9

Which of the following data types is natively supported in JSON without requiring custom serialization handlers?

Accepted Answer

Boolean. JSON natively supports a limited set of data types: string, number, boolean, null, array, and object. Boolean is one of these primitive types, so it can be directly represented as true or false without any custom serialization. This makes it straightforward to include boolean values in JSON payloads for APIs and configuration files.

Answer

Python set

Answer

Complex number

Answer

Python tuple

Question 10

Which character is used to indicate a mapping key-value pair in YAML syntax?

Accepted Answer

Colon (:). In YAML, a key-value mapping is represented by a colon followed by a space (: ).

Answer

Semicolon (;)

Answer

Arrow (->)

Answer

Equals sign (=)

Question 11

Which file extension is conventionally used for YAML data serialization files?

Accepted Answer

.yaml. YAML files conventionally use the .yaml or .yml extension. YAML (YAML Ain't Markup Language) is a human-readable data serialization standard often used for configuration files and in DevOps tools like Ansible, Kubernetes, and CI/CD pipelines. The .yaml extension is the most standard and widely recognized.

Answer

.json

Answer

.ini

Answer

.xml

Question 12

Which of the following characters is used to start a comment line in a YAML document?

Accepted Answer

Hash sign (#). Comments in YAML begin with the hash or pound sign (#) and continue to the end of the line.

Answer

Exclamation mark (!)

Answer

Percent sign (%)

Answer

Double forward slash (//)

Question 13

An automation engineer needs to convert a JSON payload representing Junos interface configurations into equivalent YAML format to reuse in an Ansible playbook. Which fundamental structural relationship between JSON and YAML makes this conversion straightforward?

Accepted Answer

YAML is a strict superset of JSON, allowing valid JSON to be parsed as YAML.. YAML 1.2 is a strict superset of JSON, meaning every valid JSON document is also valid YAML. This structural compatibility allows tools like PyYAML to parse JSON directly, and makes conversion between the two formats straightforward — the data model (mappings, sequences, scalars) is identical.

Answer

JSON uses indentation for structure just like YAML.

Answer

JSON and YAML both strictly require curly braces for all data containers.

Answer

YAML forces all numeric values to be encapsulated in strings.

Question 14

An automation scripter needs to load a YAML configuration file in Python. To prevent arbitrary code execution vulnerabilities associated with untrusted files, which function should always be called?

Accepted Answer

yaml.safe_load(). yaml.safe_load() restricts loading to simple Python types like scalars, lists, and dicts, preventing instantiation of arbitrary Python objects.

Answer

yaml.load()

Answer

yaml.secure_load()

Answer

yaml.restricted_load()

Question 15

When converting a deeply nested JSON data structure representing Junos device health metrics into YAML using PyYAML, the output uses complex block styles that are difficult for team members to read. Which parameter in yaml.safe_dump() can force scalar values or containers to prefer flow style (JSON-like curly braces and brackets) instead?

Accepted Answer

default_flow_style=True. The default_flow_style parameter in yaml.safe_dump() controls whether containers (lists and dictionaries) are rendered in flow style (JSON-like with braces and brackets) or block style (indented). Setting default_flow_style=True forces all containers to use flow style, making the output more compact and JSON-like. This directly addresses the readability issue caused by complex block styles.

Answer

force_json=True

Answer

format='compact'

Answer

style='flow'