Docker Online Test

Consider the following Dockerfile, which utilizes multistage builds. The aim is to build a lightweight, optimized image that just runs the application.

The Dockerfile first defines a base image that includes Node.js and npm, then it creates an intermediate image to install the npm dependencies. Afterwards, it runs the tests in another stage and finally, creates the release image.

Which of the following statements are true?

A: The final image will include the test scripts.
B: If a test fails, the final image will not be created.
C: The node_modules directory in the final image comes from the base image.
D: The final image will only contain the necessary application files and dependencies.
E: If the application's source code changes, only the release stage needs to be rebuilt.

You have two docker containers, X and Y. Container X is running a web service listening on port 8080, and container Y is supposed to consume this service. Both containers are created from images that don't have any special network configurations.

Container X has a Dockerfile as follows:

And, you build and run it with the following commands:

Container Y is also running alpine with python installed, and it's supposed to read data from the `/app/data` directory and send a GET request to `http://localhost:8080` every 5 minutes. The Dockerfile for container B is:

And you run it with:

Assuming all the python scripts work perfectly and firewall isn't blocking any connections, you find that container Y can't access the web service of container X via `http://localhost:8080` and also it can't read the data in `/app/data` directory. What could be the potential reason(s)?

A: Y can't access X's web service because they're in different Docker networks.
B: Y can't read the data because the volume is not shared correctly.
C: Both A and B are correct.
D: Both A and B are incorrect.

You have been asked to optimize a Dockerfile for a Python application that involves a heavy dependency installation. Here is the Dockerfile you are starting with:

Given that the application's source code changes frequently but the dependencies listed in requirements.txt rarely change, how can you optimize this Dockerfile to take advantage of Docker's layer caching, reducing the build time?

A: Move the `RUN pip install` command to before the `COPY` command.
B: Change `COPY . /app` to `COPY ./app.py /app` and move the `RUN pip install` command to before the `COPY` command.
C: Add `RUN pip cache purge` before `RUN pip install`.
D: Replace the base image with `python:3.8-slim`.
E: Implement multi-stage builds.

Check the following Dockerfile used for a project (STAGE 1):

We created an image from this Dockerfile on Dec 14 2021. A couple of weeks after Dec 14 2021, Ubuntu released new security updates to their repository. After 2 months, we modified the file (STAGE 2):

Couple of weeks later, we further modified the file to add a local file ada.txt to /ada.txt (STAGE 3): (Note that ada.txt exists in /home/adaface and the dockerfile exists in /home/code folders)

Pick correct statements:

A: If we run “docker build .” at STAGE 2, new Ubuntu updates will be fetched because apt-get update will be run again since cache is invalidated for all lines/layers of Dockerfile when a new line is added.
B: If we run “docker build .” at STAGE 2, new Ubuntu updates will not be fetched since cache is invalidated only for last two lines of the updated Dockerfile. Since the first two commands remain the same, cached layers are re-used skipping apt get update.
C: To skip Cache, “docker build -no-cache .” can be used at STAGE 2. This will ensure new Ubuntu updates are picked up.
D: Docker command “docker build .” at STAGE 3 works as expected and adds local file ada.txt to the image.
E: Docker command “docker build .” at STAGE 3 gives an error “no such file or directory” since /home/adaface/ada.txt is not part of the Dockerfile context.

Review the following Dockerfiles that work on two projects (project and project2):

All Docker files have the same end result:

- ‘project’ is cloned from git. After running few commands, ‘project’ code is removed.
- ‘project2’ is copied from file system and permissions to the folder is changed.

Pick the correct statements:

A: File 1 is the most efficient of all.
B: File 2 is the most efficient of all.
C: File 3 is the most efficient of all.
D: File 4 is the most efficient of all.
E: Merging multiple RUN commands into a single RUN command is efficient for ‘project’ since each RUN command creates a new layer with changed files and folders. Deleting files with RUN only marks these files as deleted but does not reclaim disk space. 
F: Copying ‘project2’ files and changing ownership in two separate commands will result in two layers since Docker duplicates all the files twice.