Why Hadoop security protects sensitive data - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why is Hadoop security important for sensitive data?

Which of the following best explains why Hadoop security is essential for protecting sensitive data?

A It speeds up data processing by optimizing resource allocation.

B It prevents unauthorized users from accessing or modifying data stored in Hadoop clusters.

C It automatically deletes old data to save storage space.

D It converts all data into encrypted formats without user control.

💡 Hint

Think about what happens if anyone can read or change your data without permission.

Hadoop security ensures only authorized users can access or change data, protecting sensitive information from leaks or tampering.

❓ Predict Output

intermediate

What is the output of this Hadoop permission check code?

Given the following pseudo-code checking user permissions in Hadoop, what will be the output?

Hadoop

user = 'alice'
permissions = {'alice': ['read', 'write'], 'bob': ['read']}
if 'write' in permissions.get(user, []):
    print('Access granted')
else:
    print('Access denied')

A Access granted

B Access denied

C SyntaxError

D KeyError

💡 Hint

Check if 'alice' has 'write' permission in the dictionary.

The user 'alice' has both 'read' and 'write' permissions, so the condition is true and 'Access granted' is printed.

❓ data_output

advanced

Result of Hadoop ACLs filtering sensitive files

Consider a Hadoop Access Control List (ACL) that allows only users in group 'finance' to read files tagged as 'sensitive'. Given the following data, which files will user 'john' (in group 'finance') see?

Hadoop

files = [
  {'name': 'report1.csv', 'tags': ['sensitive']},
  {'name': 'report2.csv', 'tags': ['public']},
  {'name': 'budget.xlsx', 'tags': ['sensitive']},
  {'name': 'notes.txt', 'tags': ['public']}
]
user_groups = {'john': ['finance'], 'mary': ['marketing']}

visible_files = [f['name'] for f in files if ('sensitive' in f['tags'] and 'finance' in user_groups.get('john', [])) or 'public' in f['tags']]
print(visible_files)

A ['report1.csv', 'budget.xlsx', 'report2.csv', 'notes.txt']

B ['report1.csv', 'budget.xlsx']

C ['report2.csv', 'notes.txt']

D []

💡 Hint

Remember that 'john' can see sensitive files because he is in 'finance' and all public files.

The code includes files tagged 'sensitive' if the user is in 'finance' and all files tagged 'public'. So 'john' sees all four files.

🔧 Debug

advanced

Identify the error in this Hadoop encryption code snippet

What error will this Python-like Hadoop encryption code raise?

Hadoop

def encrypt_data(data, key):
    encrypted = ''
    for char in data:
        encrypted += chr(ord(char) + key)
    return encrypted

result = encrypt_data('data', '3')
print(result)

A No error, prints encrypted string

B ValueError: chr() arg not in range(0x110000)

C NameError: name 'key' is not defined

D TypeError: unsupported operand type(s) for +: 'int' and 'str'

💡 Hint

Check the types of variables used in arithmetic operations.

The code tries to add an integer (ord(char)) and a string ('3'), causing a TypeError.

🚀 Application

expert

Which Hadoop security feature best protects data at rest?

Among the following Hadoop security features, which one specifically protects sensitive data stored on disk from unauthorized access?

A MapReduce job tracking

B Kerberos authentication

C Transparent Data Encryption (TDE)

D YARN resource scheduling

💡 Hint

Think about encryption that happens automatically when data is saved.

Transparent Data Encryption encrypts data stored on disk, protecting it from unauthorized access even if the storage is compromised.