At Simple Logic It Private, a Software Engineer plays a critical role in designing, developing, and maintaining robust software solutions that power client operations, particularly in highly specialized domains like financial services and mutual funds. The engineering team is responsible for bridging the gap between complex business logic and scalable technical execution. Whether you are building automation bots, managing enterprise-level databases, or supporting application infrastructure, your work directly impacts operational efficiency and client satisfaction. Engineers at Simple Logic It Private typically align with specific technical tracks, such as SQL Developer (L2 & L3), Python Developer + BOT Development, or Application Support Engineer – Mutual Fund Domain. This specialization means you will not just write generic code; you will solve targeted problems, optimize high-throughput database queries, build intelligent automation workflows, and ensure the seamless uptime of critical financial applications. The environment is fast-paced and demands strong foundational knowledge in software engineering principles, databases, and modern frameworks. As a Software Engineer, you will collaborate closely with system analysts, project managers, and operations teams to translate business requirements into high-performing, secure, and reliable software systems.
Initial Screening
reportedCandidates undergo an initial screening to assess basic aptitude and academic eligibility.
What to demonstrate
- Candidates undergo an initial screening to assess basic aptitude and academic eligibility
- Depth in SQL
How to prepare
- Be able to walk your CV end to end in two minutes, and say why this company specifically.
- Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.
Aptitude Test
reportedAn aptitude test is conducted to filter candidates based on logical reasoning and basic technical literacy.
What to demonstrate
- An aptitude test is conducted to filter candidates based on logical reasoning and basic technical literacy
- Depth in SQL
How to prepare
- Answer aloud and timed: How does a Unique Key differ from a Primary Key? Can a table have multiple Unique Keys?
- Answer aloud and timed: What are Joins in SQL? Explain the difference between an Inner Join, Left Join, and Full Outer Join with practical use cases.
Technical Rounds
reportedCandidates who pass the initial stages participate in technical rounds focusing on core engineering concepts, coding, and database management.
What to demonstrate
- Candidates who pass the initial stages participate in technical rounds focusing on core engineering concepts, coding, and database management
- Depth in SQL
How to prepare
- Answer aloud and timed: What is a Stored Procedure? How does it differ from a database function, and when would you use one over the other?
- Answer aloud and timed: Explain the core concepts of MVC (Model-View-Controller) architecture and how data flows through these components.
Managerial Rounds
reportedFinal stages involve managerial rounds to assess career goals, project experience, and alignment with company policies.
What to demonstrate
- Final stages involve managerial rounds to assess career goals, project experience, and alignment with company policies
- Depth in SQL
How to prepare
- Answer aloud and timed: What is Middleware in ASP.NET Core, and how does the HTTP request pipeline utilize it?
- Answer aloud and timed: Describe Dependency Injection and explain its benefits in building loosely coupled software.
PracHub editorial advice for the preparation topics above.
Prepare Your Resume Thoroughly
Interviewers will ask detailed questions about the technologies and projects you have mentioned in your resume. Do not list skills you cannot explain in depth.
Be Ready for Basic Coding
You may be asked to write basic programs (e.g., string manipulation, array operations, or pattern printing) on paper or a whiteboard. Focus on syntax and clean logic.
Understand the Domain
If you are applying for the Application Support Engineer role, take some time to understand the basics of the Mutual Fund Domain and financial transaction workflows.
Walk-in drives can be highly competitive and crowded
Arrive early, carry multiple copies of your resume, keep your academic certificates handy, and prepare for a long day of evaluations.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Diff a projection against the primary without per-row point reads
The listing projection has drifted and some rows show a stale version. The primary holds 40,000,000 resource rows across 12,000 tenants while serving 1,200 writes and 14,000 reads per second. The obvious repair, reading each resource row and comparing its version against the projection, is correct and would eventually finish. Explain precisely why it is unacceptable here, then give a diff that finds the differing rows, state its complexity, and make it safe to run against a live primary. Replication lag is usually under 100 ms and is not bounded.
Approach
- Quantify the naive cost rather than calling it slow: 40,000,000 point reads at even 0.5 ms each is over five hours serialised, and the only lever is concurrency, which is exactly what you cannot spend. The primary's pool is sized for the write path, and 40,000,000 random reads evict the buffer cache that sustains the 85 percent cache hit rate, so the audit degrades the system it is auditing.
- Replace random access with one ordered pass per side. Both sides can be read in (tenant_id, resource_id) order, which is a sequential scan on each and a merge join in O(n) time and O(1) memory. For a dense diff that is the whole answer, and it reads the primary once instead of 40,000,000 times.
- For the expected sparse case, compare range hashes instead of rows: partition the key space, compute per range an order-independent aggregate over hash(resource_id, version), compare aggregates, and descend only into ranges that differ. With d differing rows and branching factor B, at most d ranges mismatch per level, so the drill-down examines O(d log_B(n/d)) ranges and reads full rows only in mismatching leaves.
- Aggregate with a sum modulo 2^64 or a multiset hash, never XOR. XOR is order-independent but self-cancelling, so two rows wrong in the same way, or a row duplicated on one side, leave the range aggregate matching and the range is declared clean.
Follow-up
- The diff reports 900 stale rows. How do you decide between patching those rows and rebuilding the projection from resource_revision?
- Same job, but the projection lives in a search index that cannot be scanned in key order. What changes?
Identify the heaviest tenants in a five-minute window under memory pressure
The edge service handles about 3,000 requests per second across roughly 50,000 tenants, peaking near 9,000. Expose the 50 heaviest tenants by request count over the trailing five minutes so limits can be tightened before one tenant's backfill starves the fleet. You may not retain five minutes of raw records. Give the exact solution and its memory, then the bounded-memory approximation with its error stated as a formula, and say which you would ship and at what tenant cardinality that choice changes.
Approach
- Do the exact version first, because it is affordable at this cardinality: a ring of 300 one-second counters per tenant, advanced lazily, is 1,200 bytes of counters per tenant and roughly 60 to 90 MB for 50,000 tenants with overhead. Carry a running total and subtract the bucket you overwrite so a window read is O(1) rather than 300 adds.
- Extract the top 50 with a size-k min-heap over the tenant sums: O(d log k) for d tenants, against O(d log d) to sort them all. Maintaining the heap continuously instead of on query requires a tenant-to-heap-index map, because incrementing a count already inside the heap means sifting from a known position, and without that map you rebuild the heap on every request.
- State the approximation precisely rather than gesturing at sketches. Misra-Gries with m counters retains every item whose true count exceeds N/(m+1), and each retained count underestimates the truth by at most N/(m+1). With m = 1,000 and N = 900,000 requests in the window the error is roughly 900 requests, which is fine for spotting a tenant sending 50,000 and useless for ranking two tenants 200 apart.
- Say what breaks when the window slides: Misra-Gries and Space-Saving are insert-only and cannot be decremented as records age out. The workable construction is one summary per sub-window, say ten seconds, with 30 summaries merged at query time, and the merged error is the sum of the per-summary errors, so the bound degrades linearly in the number of sub-windows.
Follow-up
- The heaviest tenant is heavy because of one export job rather than user traffic. Should the limiter treat those as the same tenant?
- Two tenants sit tied at the boundary of the top 50. Does your answer flap, and does the flapping matter?
Find overlapping job attempts and peak concurrency from lease records
A day of job_run history yields about 50,000,000 attempt records: (job_run_id, job_type, attempt, started_at, finished_at which is NULL when the worker died, lease_expires_at). Leases expire on a clock, so a job that outran its lease ran twice. Produce (a) every job_run_id whose attempts overlapped in wall-clock time and (b) the peak number of simultaneously running attempts per job_type with the minute it occurred. Target O(n log n). State how you treat a NULL finished_at and what clock skew does to your answer.
Approach
- Define the interval before sorting anything: an attempt occupies [started_at, COALESCE(finished_at, lease_expires_at)). finished_at is observed and lease_expires_at is only a promise, so every attempt without a finish contributes an estimate and the whole result is a lower bound on overlap rather than an exact count.
- For peak concurrency, sweep: emit 2n endpoints, sort by (timestamp, kind) with ends ordered before starts at equal timestamps, then walk the sequence maintaining a counter per job_type and record each type's maximum with its timestamp. O(n log n) dominated by the sort, O(n) space, or O(1) extra if the sort is external and the walk streams.
- For overlap detection, do not compare attempts pairwise. A single global sort by (job_run_id, started_at) gives both the grouping and the order; within a group, keep the maximum end seen so far and report an overlap exactly when the next start is less than that running maximum, which is one linear pass after the sort.
- Half-open intervals matter and are easy to get wrong: with closed intervals an attempt ending at the same millisecond another begins reads as concurrency two, and across 50,000,000 records that artefact swamps the real signal.
Follow-up
- A handler is not idempotent and you have found 400 overlapping jobs. Which of them actually caused damage, and what would you query to find out?
- Peak concurrency for one job_type is 4 against a configured cap of 4. Is the cap working, or is the data hiding attempts that never started?
What are Joins in SQL? Explain the difference between an Inner Join, Left Join, and Full Outer Join with pract
What are Joins in SQL? Explain the difference between an Inner Join, Left Join, and Full Outer Join with practical use cases.
Approach
- Name the grain you start from and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which index the query would use, and what makes it unusable.
- Handle the rows that do not match: that is usually the actual question.
Follow-up
- How does the query change if that join becomes one-to-many?
- What happens to this when the table is ten times larger?
Stop tag and share joins from fanning out a page
resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.
Approach
- Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
- Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
- Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
- Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.
Follow-up
- The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
- A resource can be shared with the same user twice under different permissions. Does your count change, and should it?
What is the difference between a Primary Key and a Foreign Key, and how do they enforce referential integrity?
What is the difference between a Primary Key and a Foreign Key, and how do they enforce referential integrity?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Explain the difference between DELETE, TRUNCATE, and DROP commands. Which ones can be rolled back?
Explain the difference between DELETE, TRUNCATE, and DROP commands. Which ones can be rolled back?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
How does a Unique Key differ from a Primary Key? Can a table have multiple Unique Keys?
How does a Unique Key differ from a Primary Key? Can a table have multiple Unique Keys?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
What is a Stored Procedure? How does it differ from a database function, and when would you use one over the o
What is a Stored Procedure? How does it differ from a database function, and when would you use one over the other?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Explain the core concepts of MVC (Model-View-Controller) architecture and how data flows through these compone
Explain the core concepts of MVC (Model-View-Controller) architecture and how data flows through these components.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
What is Middleware in ASP.NET Core, and how does the HTTP request pipeline utilize it?
What is Middleware in ASP.NET Core, and how does the HTTP request pipeline utilize it?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Describe Dependency Injection and explain its benefits in building loosely coupled software.
Describe Dependency Injection and explain its benefits in building loosely coupled software.
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
What is Swagger, and how does it assist in the development and documentation of a Web API?
What is Swagger, and how does it assist in the development and documentation of a Web API?
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
Explain the life cycle of an ASP.NET MVC request.
Explain the life cycle of an ASP.NET MVC request.
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Explain the four main pillars of Object-Oriented Programming (OOPs) with real-world examples.
Explain the four main pillars of Object-Oriented Programming (OOPs) with real-world examples.
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Write a program to reverse a string without using built-in language functions.
Write a program to reverse a string without using built-in language functions.
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
What is the difference between an abstract class and an interface? When would you choose one over the other?
What is the difference between an abstract class and an interface? When would you choose one over the other?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Every query on one table stalls for forty seconds mid-deploy
During a release on PostgreSQL, every query touching resource times out for about 40 seconds and then recovers with no intervention. The release ran one migration, ALTER TABLE resource ADD COLUMN archived_reason TEXT, and the migration log shows it completing in 6 ms. Unrelated tables showed no change in error rate. Explain how a 6 ms statement caused a 40-second stall, give the ordered checks you would run on a live system to confirm it, and give the migration procedure that prevents a repeat.
Approach
- Separate the statement's duration from the lock's duration. ADD COLUMN with no default is a catalogue-only change and genuinely runs in milliseconds, but it requires ACCESS EXCLUSIVE, and it cannot acquire that until every transaction already touching the table has finished.
- Account for the queueing, which is the part that surprises people. A lock request that is waiting blocks later requests for conflicting modes behind it rather than letting them overtake, so one long-open transaction holds the DDL and the DDL holds all the traffic. The stall length is set by the longest open transaction, not by the size of the change.
- Confirm on a live system in this order: pg_stat_activity for that table ordered by xact_start, looking for the oldest transaction and specifically for state = idle in transaction; then pg_locks where granted = false to find the waiter; then join them on pid to name blocker and blocked. pg_blocking_pids() does that join for you and is the fastest single call.
- Prevent rather than merely time it better. Set lock_timeout to a second or two on the migration session so the DDL abandons the queue after a bounded wait and is retried, instead of holding it for as long as the oldest transaction lives. Be exact about what that buys: queries arriving during the wait still queue behind the pending ACCESS EXCLUSIVE request, so each attempt costs them up to one lock_timeout of added latency. The outage goes from 40 seconds to about one second per attempt, not to zero. Also run migrations away from deploy-time peaks, and put a statement timeout and an idle-in-transaction timeout on the analytics role that opens the long transactions.
Follow-up
- The same release also wants NOT NULL on that column. What is the sequence that gets there without a long lock?
- Your lock_timeout retry fails ten times in a row because the analytics transaction is always open. What do you change?
Built from the rounds and topics Simple Logic It Private candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Simple Logic It Private loop
- Write out the reported sequence: Initial Screening, Aptitude Test, Technical Rounds, Managerial Rounds.
- For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.
Deliverable: A one-page map of the 4 reported rounds, with the weakest marked.
02Work SQL
- Spend the session on SQL, which Simple Logic It Private candidates report being tested on.
- Write one worked example in SQL and time yourself on it.
Deliverable: One timed worked example in SQL.
03Work .NET
- Spend the session on .NET, which Simple Logic It Private candidates report being tested on.
- Write one worked example in .NET and time yourself on it.
Deliverable: One timed worked example in .NET.
04Work Object-Oriented Programming (OOP)
- Spend the session on Object-Oriented Programming (OOP), which Simple Logic It Private candidates report being tested on.
- Write one worked example in Object-Oriented Programming (OOP) and time yourself on it.
Deliverable: One timed worked example in Object-Oriented Programming (OOP).
05Answer out loud: SQL & Database Management
- Answer aloud, timed: What is the difference between a Primary Key and a Foreign Key, and how do they enforce referential integrity?
- Answer aloud, timed: Explain the difference between DELETE, TRUNCATE, and DROP commands. Which ones can be rolled back?
Deliverable: Spoken answers to 2 reported SQL & Database Management question(s), under time.
06Answer out loud: .NET & Web Development
- Answer aloud, timed: Explain the core concepts of MVC (Model-View-Controller) architecture and how data flows through these components.
- Answer aloud, timed: What is Middleware in ASP.NET Core, and how does the HTTP request pipeline utilize it?
Deliverable: Spoken answers to 2 reported .NET & Web Development question(s), under time.
07Answer out loud: OOPs & Programming Logic
- Answer aloud, timed: Explain the four main pillars of Object-Oriented Programming (OOPs) with real-world examples.
- Answer aloud, timed: Write a program to reverse a string without using built-in language functions.
Deliverable: Spoken answers to 2 reported OOPs & Programming Logic question(s), under time.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
How do you handle exceptions in your code? Explain the role of the `finally` block.
How do you handle exceptions in your code? Explain the role of the finally block.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Argue against a design, lose, and commit anyway
Describe a design you argued against and lost. State the failure you predicted as a named mechanism, not a feeling about complexity: two services that would need one transaction, a projection with no rebuild path, a write path with no idempotency key. Say what evidence you brought, what the decision maker weighed instead, and what you did after the decision was made: what you instrumented, what you wrote down, and whether the prediction came true. Five minutes.
Approach
- State the prediction in falsifiable form up front: the mechanism, the condition that triggers it, and the observable outcome. A prediction that cannot be checked also cannot be credited to you later.
- Show the evidence you had at the time and label each piece honestly as measured, analogous, or intuition. Keeping the intuition is fine; disguising it as data is the thing that erodes your standing in the next argument.
- Represent the opposing case at full strength, including the constraint you did not control: a fixed date, a team boundary, or the fact that the decision was cheap to reverse and yours was not.
- Make disagree-and-commit concrete. Name the artefact you left behind so the prediction could be settled without you: the alert and its threshold, the counter on the dashboard, the decision note that recorded the trade-off and the condition that would revisit it.
Follow-up
- What threshold on that alert would have proved you right, and did anyone ever look at it?
- If the same proposal arrived tomorrow with the same deadline, would you argue it the same way?
Tell callers you do not own that their integration breaks
A field in a write endpoint's response must change shape. You own the endpoint; you do not own the four internal callers or the outbound webhook consumers who read it. Describe a deprecation you were responsible for: what you shipped first, how you established who was actually reading the field, the window you gave and what set its length, what you did about the consumer who never moved, and how you decided removal was safe. Name the signal you used, not the announcement you sent.
Approach
- Establish the reader set empirically rather than from a wiki of owners: per-field usage counters keyed by principal, or access logs attributed to a consumer. State the blind spot of whichever you pick, since a consumer that reads the field only on a monthly job will not appear in a week of logs.
- Ship additive first. Populate the new field alongside the old one so no reader is forced to move, which is also what keeps a rolling deploy safe, because old and new instances answer the same requests at the same time and a rollback must still find the old shape present.
- Set the window from the slowest legitimate consumer's release cadence, not from your calendar, and decide separately what to do for a consumer with no release process at all, such as an external webhook endpoint you can only email.
- Convert silence into evidence before you rely on it: a short, low-traffic removal window that makes a still-dependent consumer fail visibly and loudly while you are watching, rather than at three in the morning after you have moved on.
Follow-up
- How would you detect a consumer that reads the field only during a monthly export?
- One caller refuses to move and has a commercial relationship behind it. What changes in your plan and what does not?
- 01
How do you handle exceptions in your code? Explain the role of the `finally` block.
- 02
Describe a design you argued against and lost. State the failure you predicted as a named mechanism, not a feeling about complexity: two services that would need one transaction, a projection with no rebuild path, a write path with no idempotency key. Say what evidence you brought, what the decision maker weighed instead, and what you did after the decision was made: what you instrumented, what you wrote down, and whether the prediction came true. Five minutes.
- 03
A field in a write endpoint's response must change shape. You own the endpoint; you do not own the four internal callers or the outbound webhook consumers who read it. Describe a deprecation you were responsible for: what you shipped first, how you established who was actually reading the field, the window you gave and what set its length, what you did about the consumer who never moved, and how you decided removal was safe. Name the signal you used, not the announcement you sent.
What is the difficulty level of the technical rounds?
The technical rounds are generally rated as average in difficulty. They focus heavily on fundamental concepts rather than highly complex algorithmic puzzles. If you have a strong grasp of SQL, OOPs, and your resume projects, you will find the questions straightforward.
Simple Logic It Private Software Engineer candidate reports ↗How important is the aptitude test?
The aptitude test is the primary eliminator round. It tests basic mathematical ability, logical reasoning, and English. You must clear this test to proceed to the technical and managerial interviews.
Simple Logic It Private Software Engineer candidate reports ↗How should I prepare for the SQL-specific questions?
Focus on database basics: understand the differences between DDL and DML commands, practice writing complex joins, learn how to write and optimize stored procedures, and understand indexing mechanisms.
Simple Logic It Private Software Engineer candidate reports ↗What is the typical timeline from the first round to an offer?
For walk-in drives, the entire process—including aptitude, technical, managerial, and personal rounds—is often completed in a single day. For scheduled virtual drives, the process can take between 1 to 2 weeks.
Simple Logic It Private Software Engineer candidate reports ↗What topics does Simple Logic It Private test in interviews?
Simple Logic It Private interviews most often cover SQL, Oracle Database, Oracle DBA (Database Administration),.NET, and Linux Fundamentals. The exact emphasis depends on the specific role you apply for.
Simple Logic It Private Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01Simple Logic It Private Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
Candidate reports · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
PracHub page · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
PracHub page · Accessed 2026-09-22
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