Cisco Updated 200-901 Exam Questions and Answers by axl

YAML and JSON are both data serialization formats often used in configuration files. One of the key differences is that YAML supports comments, whereas JSON does not.

Comments: YAML allows comments, which makes it easier to add explanatory notes within the configuration files.

Data Representation: Both formats are used to represent data structures, but YAML is often considered more human-readable.

Option B is correct as it highlights the difference that YAML supports comments while JSON does not.

When you run the git add . command, the changes are moved to the staging area. The git diff command, by default, shows differences between the working directory and the last commit. Since the changes have been staged, git diff will not show any differences because it is comparing the working directory to the last commit, not the staging area. To see the changes that have been staged, you need to use the git diff --staged command, which compares the staged changes with the last commit.

References:

Pro Git Book, Chapter 2: Git Basics - Recording Changes to the Repository

Git Documentation

The main security issue in this scenario is the transmission of credentials in cleartext over HTTPS. Even though HTTPS provides a secure transport layer, the credentials should still be protected, such as by using secrets or encryption. Additionally, setting logging to debug and potentially exposing these credentials in logs is also a security concern.

Cleartext Credentials: Sending credentials in cleartext within the webhook payload can expose them if HTTPS is not configured correctly or if intercepted by an attacker.

Debug Logging: Enabling debug logging can expose sensitive information, including credentials, in logs.

Option B highlights the risk of transmitting credentials unencrypted over HTTPS, which can lead to credential leakage.

The Bash script shown in the exhibit performs the following actions:

Updates the list of available packages using brew update.

Installs Docker using brew install docker.

Upgrades pip and installs tensorflow and pytorch using pip install.

Pulls the latest TensorFlow Docker image using docker pull tensorflow/tensorflow:latest.

This script is designed to install the necessary software packages (Docker, TensorFlow, PyTorch) required for a machine learning-based application. It does not create or remove any software packages but installs them.

References:

Cisco DevNet Associate Certification Guide

Docker Documentation

Homebrew Documentation

Pip Documentation