PII detection and redaction in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to detect PII entities in the text using a pre-trained model.

Prompt Engineering / GenAI

entities = model.[1](text)

Drag options to blanks, or click blank then click option'

Apredict

Bfit

Ctransform

Dcompile

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fit' instead of 'predict' which trains the model, not detects.

The predict method is used to get the PII entities detected by the model from the input text.

2fill in blank

medium

Complete the code to replace detected PII entities with a redaction token.

Prompt Engineering / GenAI

redacted_text = text.replace(entity.value, [1])

Drag options to blanks, or click blank then click option'

A'[REDACTED]'

Bentity

Ctext

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Replacing with the entity itself does not redact anything.

We replace the value of the detected PII entity with the string '[REDACTED]' to hide sensitive information.

3fill in blank

hard

Fix the error in the code to correctly iterate over detected PII entities.

Prompt Engineering / GenAI

for entity in [1]:
    print(entity)

Drag options to blanks, or click blank then click option'

Amodel

Bredacted_text

Centities

Dtext

Attempts:

3 left

💡 Hint

Common Mistakes

Looping over the raw text string instead of the entities list.

The variable entities holds the list of detected PII entities to iterate over.

4fill in blank

hard

Fill both blanks to create a dictionary mapping PII types to their counts.

Prompt Engineering / GenAI

pii_counts = {entity.[1]: pii_entities.[2](entity) for entity in pii_entities}

Drag options to blanks, or click blank then click option'

Atype

Bcount

Cindex

Dappend

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'index' or 'append' which do not count occurrences.

We use entity.type as the key and count how many times each entity appears with pii_entities.count(entity).

5fill in blank

hard

Fill all three blanks to filter PII entities by confidence score and redact them.

Prompt Engineering / GenAI

for entity in entities:
    if entity.[1] > [2]:
        text = text.replace(entity.[3], '[REDACTED]')

Drag options to blanks, or click blank then click option'

Aconfidence

B0.8

Ctext

Dvalue

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong attribute names like 'text' instead of 'value' for replacement.

We check if the entity's confidence is above 0.8 and replace the entity's value in the text with '[REDACTED]'.

Practice

1. What is the main purpose of PII detection in text data?

easy

A. To increase the size of the dataset

B. To improve the speed of text processing

C. To find personal information to protect privacy

D. To translate text into different languages

Solution

1. Step 1: Understand PII detection

   PII detection is about finding personal information like names, emails, or phone numbers in text.

2. Step 2: Identify the purpose

   The goal is to protect privacy by recognizing sensitive data that should not be shared openly.

3. Final Answer:

   To find personal information to protect privacy -> Option C

4. Quick Check:

   PII detection = find personal info [OK]

Hint: PII detection means finding personal info to keep it safe [OK]

Common Mistakes:

• Confusing PII detection with data translation
• Thinking it speeds up processing
• Believing it increases dataset size

2. Which of the following is the correct way to redact an email address in text?

easy

A. Replace the email with <EMAIL_REDACTED>

B. Delete the entire sentence containing the email

C. Change the email to a random number

D. Highlight the email in bold

Solution

1. Step 1: Understand redaction

   Redaction means hiding sensitive info by replacing it with a placeholder, not deleting or changing it randomly.

2. Step 2: Choose the correct method

   Replacing the email with a clear placeholder like <EMAIL_REDACTED> keeps the text readable and safe.

3. Final Answer:

   Replace the email with <EMAIL_REDACTED> -> Option A

4. Quick Check:

   Redaction = replace sensitive info with placeholder [OK]

Hint: Redact by replacing sensitive info with clear placeholders [OK]

Common Mistakes:

• Deleting whole sentences instead of redacting
• Replacing emails with unrelated data
• Highlighting instead of hiding

3. Given this Python code snippet for PII redaction:

import re
text = 'Contact me at [e-mail] or 123-456-7890.'
redacted = re.sub(r'\S+@\S+\.\S+', '<EMAIL_REDACTED>', text)
print(redacted)

What will be the output?

medium

A. Contact me at [e-mail] or 123-456-7890.

B. Contact me at [e-mail] or <EMAIL_REDACTED>.

C. Contact me at <EMAIL_REDACTED> or <EMAIL_REDACTED>.

D. Contact me at <EMAIL_REDACTED> or 123-456-7890.

Solution

1. Step 1: Understand the regex pattern

   The pattern '\S+@\S+\.\S+' matches email addresses (non-space chars @ non-space chars . non-space chars).

2. Step 2: Apply substitution

   The code replaces the email with '<EMAIL_REDACTED>' but leaves the phone number unchanged.

3. Final Answer:

   Contact me at <EMAIL_REDACTED> or 123-456-7890. -> Option D

4. Quick Check:

   Email replaced, phone unchanged = Contact me at <EMAIL_REDACTED> or 123-456-7890. [OK]

Hint: Regex replaces emails only, phone stays same [OK]

Common Mistakes:

• Thinking phone number is replaced
• Misreading regex pattern
• Assuming no replacement happens

4. You wrote this code to redact phone numbers:

import re
text = 'Call 555-1234 or 555-5678.'
redacted = re.sub(r'\d{3}-\d{4}', '<PHONE_REDACTED>', text)
print(redacted)

But the output is:
'Call 555-1234 or 555-5678.'
What is the likely error?

medium

A. The regex pattern is incorrect and does not match the phone numbers

B. The re.sub function is missing the text argument

C. The print statement is missing parentheses

D. The text variable is empty

Solution

1. Step 1: Check regex pattern against phone format

   The pattern '\d{3}-\d{4}' matches numbers like '555-1234', but the phone numbers might have different formats or extra spaces.

2. Step 2: Confirm if pattern matches text

   If the phone numbers have area codes or spaces, the pattern won't match, so no replacement occurs.

3. Final Answer:

   The regex pattern is incorrect and does not match the phone numbers -> Option A

4. Quick Check:

   Regex mismatch causes no replacement [OK]

Hint: Check regex matches exact phone format in text [OK]

Common Mistakes:

• Assuming re.sub syntax error
• Forgetting parentheses in print (Python 3+)
• Thinking text is empty without checking

5. You want to redact both emails and phone numbers in a text using Python. Which combined regex pattern correctly matches emails and US phone numbers like '123-456-7890'?

hard

A. r'\d{3}-\d{4}|\S+@\S+\.\S+'

B. r'\S+@\S+\.\S+|\d{3}-\d{3}-\d{4}'

C. r'\S+@\S+\.\S+\d{3}-\d{3}-\d{4}'

D. r'\S+@\S+\.\S+&\d{3}-\d{3}-\d{4}'

Solution

1. Step 1: Understand regex for emails and phones

   The email pattern '\S+@\S+\.\S+' matches emails; '\d{3}-\d{3}-\d{4}' matches US phone numbers like '123-456-7890'.

2. Step 2: Combine patterns with OR operator

   Using '|' between patterns matches either emails or phone numbers separately.

3. Step 3: Evaluate options

   r'\S+@\S+\.\S+|\d{3}-\d{3}-\d{4}' correctly uses '|' to combine patterns; r'\d{3}-\d{4}|\S+@\S+\.\S+' reverses order but still works; r'\S+@\S+\.\S+\d{3}-\d{3}-\d{4}' concatenates patterns (wrong); r'\S+@\S+\.\S+&\d{3}-\d{3}-\d{4}' uses '&' which is invalid in regex.

4. Final Answer:

   r'\S+@\S+\.\S+|\d{3}-\d{3}-\d{4}' -> Option B

5. Quick Check:

   Use '|' to combine regex patterns [OK]

Hint: Use '|' to combine email and phone regex patterns [OK]

Common Mistakes:

• Concatenating patterns without '|'
• Using invalid regex operators like '&'
• Mixing order but forgetting OR operator