The Complete Practical Docker Guide - Creating Dockerfile for the Python API Service

The Complete Practical Docker Guide - Creating Dockerfile for the Python API Service

Assessment

Interactive Video

Information Technology (IT), Architecture

University

Hard

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This tutorial guides you through creating a custom Docker image for an API application. It covers setting up a Dockerfile, choosing a Python base image, configuring the working directory, exposing necessary ports, and installing Python dependencies. The tutorial also explains how to copy application files into the Docker image and set up the CMD instruction to run the application. By the end, you'll understand how to build and run a Docker container for a Python Flask web server.

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10 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the purpose of creating a Dockerfile in the root of the API folder?

To store all application files

To run the application directly

To expose application ports

To provide instructions for building a Docker image

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which base image is used for creating the custom Docker image in this tutorial?

Ruby

Python

Java

Node.js

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Why is it recommended to create a separate folder for application files inside the Docker image?

To make the image smaller

To follow best practices and avoid cluttering the root

To increase application speed

To reduce memory usage

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What port is exposed for the Flask web server in the Dockerfile?

3000

5050

5000

8080

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which tool is used to install Python dependencies inside the Docker image?

yarn

gradle

pip

npm

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the purpose of the requirements.txt file in the Docker image?

To configure the network settings

To list all development tools

To list all project dependencies

To specify the Docker version

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Why is there no need to create a Python virtual environment inside the Docker container?

It increases the image size

The container runs only a single application

It is too complex to set up

It is not supported by Docker

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