10 Python AWS Interview Questions and Answers

Python AWS combines the versatility of Python with the robust cloud services provided by Amazon Web Services (AWS). This powerful combination is increasingly sought after in various industries for tasks such as cloud automation, data processing, and scalable application deployment. Python’s simplicity and extensive libraries, paired with AWS’s comprehensive cloud solutions, make it an essential skill set for modern developers.

This article offers a curated selection of interview questions designed to test your knowledge and proficiency in Python AWS. By working through these questions, you will gain a deeper understanding of how to leverage Python for AWS-related tasks, enhancing your readiness for technical interviews and boosting your career prospects.

Python AWS Interview Questions and Answers

1. Write a Python function using Boto3 to list all objects in a specific S3 bucket.

Boto3 is the AWS SDK for Python, enabling developers to interact with AWS services like S3 and EC2. To list all objects in a specific S3 bucket, use Boto3 to interface with the S3 service.

Example:

import boto3

def list_s3_objects(bucket_name):
    s3 = boto3.client('s3')
    response = s3.list_objects_v2(Bucket=bucket_name)
    
    if 'Contents' in response:
        for obj in response['Contents']:
            print(obj['Key'])
    else:
        print("No objects found in the bucket.")

# Example usage
list_s3_objects('your-bucket-name')

2. Write a Python script to start an EC2 instance using Boto3.

To start an EC2 instance using Boto3, follow these steps:

• Install Boto3 if needed.
• Configure AWS credentials.
• Use Boto3 to interact with EC2.

Here is a concise script:

import boto3

session = boto3.Session(
    aws_access_key_id='YOUR_ACCESS_KEY',
    aws_secret_access_key='YOUR_SECRET_KEY',
    region_name='YOUR_REGION'
)

ec2 = session.client('ec2')

response = ec2.start_instances(
    InstanceIds=['YOUR_INSTANCE_ID']
)

print(response)

3. How would you handle pagination when listing resources using Boto3?

To handle pagination when listing resources with Boto3, use the paginator object. It allows iteration through all pages of results without manually handling pagination tokens.

Example:

import boto3

client = boto3.client('s3')

paginator = client.get_paginator('list_objects_v2')

for page in paginator.paginate(Bucket='my-bucket'):
    for obj in page['Contents']:
        print(obj['Key'])

4. Write a Python function to publish a message to an SNS topic using Boto3.

Amazon SNS is a messaging service for sending messages to subscribers. Here is a function to publish a message to an SNS topic using Boto3:

import boto3

def publish_to_sns(topic_arn, message, subject):
    sns_client = boto3.client('sns')
    response = sns_client.publish(
        TopicArn=topic_arn,
        Message=message,
        Subject=subject
    )
    return response

# Example usage
topic_arn = 'arn:aws:sns:us-east-1:123456789012:MyTopic'
message = 'Hello, this is a test message.'
subject = 'Test Subject'
response = publish_to_sns(topic_arn, message, subject)
print(response)

5. Write a Python function to retrieve an item from a DynamoDB table using Boto3.

To retrieve an item from a DynamoDB table using Boto3:

1. Initialize a Boto3 client for DynamoDB.
2. Specify the table name and the key of the item.
3. Use the get_item method.

Example:

import boto3

def get_item_from_dynamodb(table_name, key):
    dynamodb = boto3.resource('dynamodb')
    table = dynamodb.Table(table_name)
    response = table.get_item(Key=key)
    return response.get('Item')

# Example usage
table_name = 'YourTableName'
key = {'PrimaryKey': 'YourPrimaryKeyValue'}
item = get_item_from_dynamodb(table_name, key)
print(item)

6. Write a Python script to create a Lambda function using Boto3.

To create a Lambda function using Boto3:

1. Set up Boto3 and configure AWS credentials.
2. Create the Lambda function by specifying necessary parameters.
3. Upload the function code.

Example:

import boto3

session = boto3.Session(
    aws_access_key_id='YOUR_ACCESS_KEY',
    aws_secret_access_key='YOUR_SECRET_KEY',
    region_name='YOUR_REGION'
)

lambda_client = session.client('lambda')

response = lambda_client.create_function(
    FunctionName='my_lambda_function',
    Runtime='python3.8',
    Role='arn:aws:iam::YOUR_ACCOUNT_ID:role/YOUR_LAMBDA_ROLE',
    Handler='lambda_function.lambda_handler',
    Code={
        'ZipFile': open('lambda_function.zip', 'rb').read(),
    },
    Description='My Lambda function',
    Timeout=120,
    MemorySize=128,
    Publish=True
)

print(response)

7. Write a Python function to create an RDS instance using Boto3.

To create an RDS instance using Boto3, use the create_db_instance method with the required parameters.

Example:

import boto3

def create_rds_instance(db_instance_identifier, db_instance_class, engine, master_username, master_user_password):
    rds_client = boto3.client('rds')

    response = rds_client.create_db_instance(
        DBInstanceIdentifier=db_instance_identifier,
        DBInstanceClass=db_instance_class,
        Engine=engine,
        MasterUsername=master_username,
        MasterUserPassword=master_user_password,
        AllocatedStorage=20
    )

    return response

# Example usage
response = create_rds_instance(
    db_instance_identifier='mydbinstance',
    db_instance_class='db.t2.micro',
    engine='mysql',
    master_username='admin',
    master_user_password='password123'
)

print(response)

8. Write a Python script to upload a file to an S3 bucket and make it publicly accessible.

To upload a file to an S3 bucket and make it publicly accessible, use the boto3 library:

import boto3
from botocore.exceptions import NoCredentialsError

def upload_to_s3(file_name, bucket, object_name=None):
    s3_client = boto3.client('s3')
    try:
        s3_client.upload_file(file_name, bucket, object_name or file_name, ExtraArgs={'ACL': 'public-read'})
        print(f"File {file_name} uploaded to {bucket} and made publicly accessible.")
    except FileNotFoundError:
        print("The file was not found.")
    except NoCredentialsError:
        print("Credentials not available.")

# Example usage
upload_to_s3('example.txt', 'my-bucket', 'example.txt')

9. Describe how you would configure S3 event notifications using Boto3.

Amazon S3 event notifications allow you to receive notifications for specific events in your S3 bucket. To configure these notifications using Boto3:

• Create an S3 bucket if needed.
• Define the event notification configuration.
• Apply the configuration to the S3 bucket.

Example:

import boto3

s3_client = boto3.client('s3')
bucket_name = 'your-bucket-name'
lambda_function_arn = 'arn:aws:lambda:region:account-id:function:your-function-name'

notification_configuration = {
    'LambdaFunctionConfigurations': [
        {
            'LambdaFunctionArn': lambda_function_arn,
            'Events': ['s3:ObjectCreated:*']
        }
    ]
}

s3_client.put_bucket_notification_configuration(
    Bucket=bucket_name,
    NotificationConfiguration=notification_configuration
)

10. Explain how you would use Boto3 to manage AWS Kinesis streams.

Boto3 allows interaction with AWS Kinesis, a platform for real-time data streaming. To manage Kinesis streams using Boto3:

• Import Boto3.
• Create a Kinesis client.
• Use the client to manage streams and records.

Example:

import boto3

kinesis_client = boto3.client('kinesis', region_name='us-east-1')

stream_name = 'example_stream'
kinesis_client.create_stream(StreamName=stream_name, ShardCount=1)

kinesis_client.put_record(
    StreamName=stream_name,
    Data=b'example_data',
    PartitionKey='partition_key'
)

response = kinesis_client.get_shard_iterator(
    StreamName=stream_name,
    ShardId='shardId-000000000000',
    ShardIteratorType='TRIM_HORIZON'
)
shard_iterator = response['ShardIterator']

records_response = kinesis_client.get_records(ShardIterator=shard_iterator)
print(records_response['Records'])