Adobe Interview Questions (12+ Questions)

We have a scenario when we troubleshoot some service. It produce untagged log outputs when it runs manually and we need to create some file and locate that file under local bin timestamp and make it executable so it can be used with any pipeline. We need to pipe out some output into this file and it has to produce us timestamp result. Try to use date command and format this in current format. We started with shebang to execute it via bash and while loop. Then we type IFS equals to not split lines. And we add read minus r to not interpret backslashes. We need this if we want to read lines safely. Then we start our loop, and then our command echo line. This is our variable that we get via the pipe. And then the date command. Use chmod plus x to make it executable. And now if we echo and pipe it out into this file, we'll see the timestamp.

We have a container image built from Dockerfile that fails during execution. Our task is to identify this issue and fix it so when it runs, it runs successfully with exit code zero. Error reads like exec, then the binary name, exec format error. Usually this error means that the binary was built with wrong architecture. To verify this, try to run file command. This is executable and the architecture is ARM 64. Next we need to identify the host architecture, the one that we're running this docker. Type uname hyphen M, which shows us our host architecture, which is X 86 64. There's a mismatch. X 86 is an instruction set and AMD 64 is a 64 bit version of that instruction set. When we are asked about X 86, for the 64 bit processors, we need to use AMD 64.

Traffic splitting with native Kubernetes means that we need to split traffic between two deployments, V one and V two, but using native Kubernetes primitives. We will not use reverse proxy like traefik or nginx and we'll use Kubernetes services instead. This is called a canary deployment where we have a stable version of our application V one, and we would like to introduce version V two, but we don't want to channel all the traffic to V two because we are not sure if it's stable or not, and we rather traffic only one third of it. We'll create a service with a selector that covers both my app, which will be V one and both V two, which will have the same selector. That service will randomly go both to V one and V two because they will have same selectors. We can monitor logs of our V two deployment and see if it's stable or not. When we check endpoints for my app service in the canary namespace, we have three endpoints and two of them will be for V one and one of them will be for V two.

Kubernetes Default StorageClass: fast-sc Automatic PVC Binding storage Namespace. Configure fast-sc as cluster default StorageClass with storageclass.kubernetes.io/is-default-class: "true" annotation so default-pvc in storage namespace binds automatically without storageClassName. Eliminate manual StorageClass specification for developer productivity and standardized provisioning. Perfect for platform engineering, self-service storage, GitOps consistency, multi-tenant defaults, operator patterns, and production storage standardization.

We have a repo with a test suite at run-test.sh, but there's no automated testing set up for the pull requests. We need to complete a workflow that runs the tests on every PR and uploads the results as an artifact, even if the tests fail. A trigger is what tells GitHub Actions when to run a workflow. In this case, we use on pull request, which means that the workflow fires every time someone opens up PR targeting main or pushes new commits to an existing PR. The tests run automatically on every PR, and if they pass, then the merge is allowed, and if they fail, the merge is blocked. By default, GitHub Actions stopped executing steps when a previous step fails. But we still want to upload the test results so that the team can see what went wrong. That's where if always comes in. It tells GitHub Actions to run that step no matter what. Artifacts are files that a workflow produces and stores after it finishes, things like test results, build outputs, or logs. The runner environment is temporary. By adding an upload artifact step, we save those files to storage. Actions/checkout pulls the code repo into the runner. The script executes the tests and writes the result to test-results.txt. If any test fails, the script exits with a non-zero code, which marks this step as failed.

We have multiple applications that all need the same build steps. Right now, each app has its own workflow with duplicated code. We need to create one shared workflow that any other workflow can call with different parameters. A reusable workflow is a workflow that other workflows can call. It's like a function. The shared workflow uses something called as workflow_call as its trigger, which means that it can't run on its own, and it will only run when another workflow calls it. The reusable workflow defines what parameters it expects to be using, using an inputs block. The calling workflow provides those values using a with block. Workflow dispatch is a manual trigger where a human fills in the inputs and clicks a button. Whereas in workflow call, it's a programmatic trigger where another workflow calls it automatically. App-name is a required string parameter, and if the caller doesn't provide it, the workflow fails.

We need to create a simple Lambda function for our greeting microservice and our task is to create Lambda function named Hello function, which will take the name from the event return. We can use pre-created IAM role, lambda execution role, and at the end we need to invoke the function with the name world and verify that we get Hello World as a return value. We can use Python or no Gs. We need Lambda execution role to allow Lambda function to access our AWS services. The name will be Lambda Handler. We can use different name, but Lambda Handler is a default name. We'll have to have this variable name equals to event name because event is a dictionary or a map with keys and values. And finally, we need to return the result in expected format. First you go to Lambda and create function. As a runtime we'll use Python, keep handler name as default and in permissions we can use existing role and we can choose role lambda execution role. We won't need JSON because we need a simple string return. This variable will grab even name and we will return F. Print. Hello name.

We are given two tables, departments and employees. These two tables are connected through department ID column. Some employees might not be assigned to any department, so their department ID is null. But we still want them in our results. Our main goal here is to return all employees that earn more than 50,000 with their department name sorted by hire date from most recent to oldest. In order to combine these two tables, we will use the concept of left join. A left join keeps all rows from the left table even if there is no matching row in the right table. The missing values just become null. We implement left join in between, and using on, we indicate which common columns were used to connect these tables. We will use where clause to filter the rows. We only want those employees whose salary is greater than 50,000. Finally, we sort everything out by hire date in descending order so that the most recent date pops up first and oldest date last.

Objective

Write an SQL query that identifies pairs of users who share the same email address. Each pair should be listed with the email address and the names of the two users, sorted by email. Ensure that each pair is listed only once, with the user having the smaller ID appearing first.

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How to Find the Hour with the Highest Sales Using SQL

When preparing for an SQL interview, it helps to master queries that analyze and summarize data efficiently. One common scenario is identifying the peak sales hour, a problem you might encounter during the interview, structured as follows:

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