Accenture Attention to Detail Questions: 15 Solved Examples

The Attention to Detail section of Accenture’s Cognitive Ability test is the one section where no deduction is required: 15 questions, 15 minutes, and the answer is always visible in the data.

This article covers the section pattern, four question formats, and five step-by-step worked examples. For the full Cognitive Ability test structure and the ASE versus Advanced ASE CTC breakdown, start with the Accenture placement papers guide.

Where Attention to Detail fits in Accenture’s Cognitive Ability test

Accenture’s fresher selection runs four gates in sequence: Communication Assessment, Cognitive Ability test, Technical Assessment, and Coding round, followed by interviews. The Attention to Detail section sits inside the Cognitive Ability test alongside Critical Reasoning, Abstract Reasoning, and Verbal Ability.

The section is 15 questions in 15 minutes. Questions do not require logical inference or pattern deduction. They test whether a candidate can compare two or more data items systematically and identify where they diverge. That scope makes it the most mechanical section of the Cognitive Ability test, and also the one where consistent technique closes most of the score gap.

Eligibility for both Accenture tracks:

• 60% aggregate throughout Class 10, Class 12, and graduation
• No active backlogs at the time of application
• All engineering branches qualify: CSE, ECE, EEE, IT, AIDS, Mech, Civil

Four question formats in the section

Format	What it tests	Core technique
String comparison	Find the pair where two strings differ	Left-to-right character scan
Number and code matching	Identify the pair where two numbers or codes don’t match	Digit-by-digit column scan
Similarity identification	Pick the option that exactly matches a reference item	Character-by-character comparison against reference
Error detection in data	Spot the entry in a table that contains an inconsistency	Column-by-column audit across rows

String comparison and number matching are the most common formats and resolve fastest. Error detection questions are slower because the data set is larger.

For the Critical Reasoning and Abstract Reasoning questions in the same test, see the Accenture logical reasoning guide.

String and number comparison: worked examples

These are the most common format. One side shows a string or number; the other side shows what appears to be the same. Most options match exactly; one option has a difference, typically a transposition (two adjacent characters swapped), a substitution (one character replaced by another), or a deletion (one character removed).

Worked example 1: String comparison

Question: Which pair has different content?

• (A) CANDIDATE | CANDIDATE
• (B) ASSESSMENT | ASSESMENT
• (C) PLACEMENT | PLACEMENT
• (D) TECHNICAL | TECHNICAL

Solution:

• (A): C-A-N-D-I-D-A-T-E vs C-A-N-D-I-D-A-T-E. All 9 characters match.
• (B): ASSESSMENT has 10 characters (A-S-S-E-S-S-M-E-N-T). ASSESMENT has 9 characters (A-S-S-E-S-M-E-N-T). At position 6, the reference has S while the second string has M. One S is missing.
• (C): P-L-A-C-E-M-E-N-T vs P-L-A-C-E-M-E-N-T. All 9 characters match.
• (D): T-E-C-H-N-I-C-A-L vs T-E-C-H-N-I-C-A-L. All 9 characters match.
• Answer: (B)

The key is counting, not just pattern-matching visually. ASSESSMENT has two S characters before the M; ASSESMENT has one. Visual scanning at speed often misses a single-character deletion.

Worked example 2: Number comparison

Question: Which pair contains mismatched numbers?

• (A) 4,782,563 | 4,782,563
• (B) 4,782,563 | 4,782,536
• (C) 9,134,708 | 9,134,708
• (D) 9,134,708 | 9,134,708

Solution:

• (A): 4-7-8-2-5-6-3 vs 4-7-8-2-5-6-3. Match.
• (B): 4-7-8-2-5-6-3 vs 4-7-8-2-5-3-6. Position 6 is 6 vs 3. Position 7 is 3 vs 6. The last two digits are transposed.
• (C) and (D): both match.
• Answer: (B)

The transposition in (B) swaps 63 to 36. Both digits are present, so the total looks plausible at a glance. Column-by-column scanning catches it because the mismatch appears at a fixed position regardless of how plausible the digits look.

Worked example 3: Code comparison

Question: Find the pair where the codes are different.

• (A) EMP-4921-AB | EMP-4921-AB
• (B) EMP-4921-AB | EMP-4921-AB
• (C) EMP-4921-AB | EMP-4912-AB
• (D) EMP-4921-AB | EMP-4921-AB

Solution:

• Only option (C) shows a second code that differs. Comparing the number segment:
  • Reference: 4-9-2-1
  • Option C second code: 4-9-1-2
  • Position 3: 2 vs 1
  • Position 4: 1 vs 2
• Answer: (C)

The digit pair 21 is transposed to 12. The letter segments (EMP and AB) match in all options, so the difference is entirely in the numeric block.

Similarity matching and error detection: worked examples

Worked example 4: Similarity identification

Question: The reference code is KQ-3847-MX. Which option matches it exactly?

• (A) KQ-3847-MX
• (B) KQ-3874-MX
• (C) KQ-3847-NX
• (D) KO-3847-MX

Solution:

• (A): K-Q vs K-Q, 3-8-4-7 vs 3-8-4-7, M-X vs M-X. All characters match. This is the answer.
• (B): digit segment 3-8-7-4 vs reference 3-8-4-7. Positions 3 and 4 are swapped (4 and 7 reversed).
• (C): suffix M-X vs N-X. Position 7 is M vs N.
• (D): prefix K-Q vs K-O. Position 2 is Q vs O.
• Answer: (A)

Similarity questions are the inverse of comparison questions: instead of finding the odd pair out, you confirm the exact match. Work from the reference character by character rather than scanning the options for what looks right.

Worked example 5: Error detection in a data table

Question: One employee record below has a mismatch between the Reference ID and the Actual ID. Identify the row.

Row	Reference ID	Actual ID
1	EMP-1047-A	EMP-1047-A
2	EMP-2836-C	EMP-2836-C
3	EMP-3914-B	EMP-3914-B
4	EMP-4723-D	EMP-4732-D

Solution:

• Row 1: E-M-P-1-0-4-7-A vs E-M-P-1-0-4-7-A. Match.
• Row 2: E-M-P-2-8-3-6-C vs E-M-P-2-8-3-6-C. Match.
• Row 3: E-M-P-3-9-1-4-B vs E-M-P-3-9-1-4-B. Match.
• Row 4: digit segment 4-7-2-3 vs 4-7-3-2. Position 3 is 2 vs 3. Position 4 is 3 vs 2. The digits 23 are transposed to 32.
• Answer: Row 4

For data-table questions, scan one column at a time across all rows rather than reading each row in full. The vertical scan spots the inconsistency faster than row-by-row reading when there are four to six rows to check.

Preparation approach

Three principles that carry over to the actual test:

Scan left to right, one character at a time

The most common error is visual pattern-matching rather than systematic scanning. Eyes tend to skip over transpositions because all the expected characters are present. A fixed left-to-right approach resolves this.

Verify the full string even after spotting a mismatch

Once a difference is found, the answer is usually clear. Checking through the remaining characters still matters because some questions introduce two differences, and the follow-up part of the question may ask for the count or location of all mismatches, not just confirmation that a pair differs.

Build speed through varied practice material

The test uses strings and codes you haven’t seen before. Practising with the same worked examples repeatedly trains pattern recognition for those specific items, not the scanning technique. Use varied sources: employee ID formats, account codes, alphanumeric sequences with different lengths and structures. After a consistent block of practice, the scanning method becomes automatic enough that 15 questions in 15 minutes leaves room to check flagged answers.

Beyond the Attention to Detail section

IT sector fresher hiring is on track to add more than 150,000 roles in FY26, with Accenture among the firms driving that expansion with an explicit GenAI focus (Economic Times). The split between ASE (Rs 4.5 to 6.5 LPA) and Advanced ASE (Rs 6.5 to 9.0 LPA) now tracks AI technical depth more than it did three years ago. Accenture backs this with the GenAI Scholars Program, which offers 40-plus hours of self-paced GenAI content built on Stanford Online material.

Clearing the Attention to Detail gate is the first step in the Cognitive test. For the AI-skills layer that separates the two offer bands, TinkerLLM at ₹499 gets you into practical LLM work without a prior ML background. The 2026 AI roadmap for Indian engineering students maps the full path toward production AI engineering.