As a Software Engineer at UPMC (University of Pittsburgh Medical Center), you will build and support the critical digital infrastructure that powers one of the leading healthcare networks in the United States. This role goes far beyond standard enterprise software development. Your work directly impacts clinical workflows, hospital operations, patient care portals, and life-saving medical technologies. By translating complex healthcare requirements into reliable, secure, and scalable software solutions, you will help bridge the gap between cutting-edge technology and patient outcomes. The engineering teams at UPMC work on a diverse array of challenges, from processing high-volume clinical data to optimizing cloud-based infrastructure. Because healthcare systems require near-constant availability and absolute data integrity, software reliability is paramount. You will be responsible for designing robust backend APIs, managing complex database systems, and deploying cloud services that clinicians and patients rely on daily. This position offers a unique opportunity to solve technically demanding problems while contributing to a mission-driven organization. Whether you are modernizing legacy systems or building new web applications, your contributions will play a vital role in keeping at the forefront of clinical and technological innovation. UPMC
HR Phone Screen
reportedInitial call to discuss your background, interest in the role, and salary expectations.
What to demonstrate
- Initial call to discuss your background, interest in the role, and salary expectations
- Depth in OOP Fundamentals
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.
Technical Assessment
reportedIncludes live coding, a technical quiz, or deep-dive questions on OOP and SQL.
What to demonstrate
- Includes live coding, a technical quiz, or deep-dive questions on OOP and SQL
- Depth in OOP Fundamentals
How to prepare
- Answer aloud and timed: How do you manage memory and prevent leaks in a managed language like C# or Java?
- Answer aloud and timed: Explain the difference between method overloading and method overriding.
Behavioral Panel
reportedMeet with peer engineers and hiring managers to discuss behavioral aspects.
What to demonstrate
- Meet with peer engineers and hiring managers to discuss behavioral aspects
- Depth in OOP Fundamentals
How to prepare
- Prepare three examples from your own work, each with a decision you made and an outcome you can quantify.
- Re-read the description of the behavioral panel above and write down what you would ask to confirm before it.
PracHub editorial advice for the preparation topics above.
Master the Fundamentals
Do not overcomplicate your preparation. Spend your time mastering the four pillars of OOP, basic data structures, and standard SQL joins and aggregations.
Highlight Azure and PowerShell
If you have experience with Microsoft Azure or PowerShell scripting, make sure to highlight it on your resume and during your conversations, as these skills are highly sought after by UPMC teams.
Prepare Your Resume Stories
Be ready to walk through any project listed on your resume in detail. Interviewers will ask you to explain your specific contributions, the technologies used, and the challenges you overcame.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
14 technical prompts1 include a worked solution
How do you manage memory and prevent leaks in a managed language like C# or Java?
medium
How do you manage memory and prevent leaks in a managed language like C# or Java?
Approach
- Reframe first: the garbage collector frees objects that are unreachable, so a leak in C# or Java is an object you no longer need that is still reachable, or an unmanaged resource (file handle, socket, DB connection) that was never released.
- Explain the collector briefly: it traces from roots (stack locals, static fields, GC handles) and is generational, collecting young objects often and promoting survivors. In .NET, objects of 85,000 bytes or more go on the Large Object Heap, collected with Gen 2 and not compacted by default.
- List the usual leak causes, most common first: static collections or caches that only grow; event subscriptions (
+=without-=) where the publisher keeps the subscriber alive; lambdas or non-static inner classes capturing large outer objects;ThreadLocalvalues left on pooled threads. - Release resources deterministically:
IDisposablewithusingin C#,AutoCloseablewith try-with-resources in Java. Treat finalizers andCleaneras a safety net only, since they run at an unpredictable time; Java'sfinalize()is deprecated for removal. - Prevent growth by design: give caches a size limit or TTL, unsubscribe in
Dispose, useWeakReference,WeakHashMaporConditionalWeakTableonly where weak semantics fit, and cut allocation churn with pooling such asArrayPool<T>. CallingGC.Collect()orSystem.gc()fixes nothing. - Diagnose with evidence: if heap after each full GC keeps climbing, take two dumps some time apart (
jcmd <pid> GC.heap_dump /tmp/heap1.hprof,dotnet-gcdump collect -p <pid>), diff object counts by type, and follow the growing type's path to a GC root.
Follow-up
- Do circular references leak in a garbage-collected language? No; a tracing collector frees a cycle once nothing reachable from a root points into it, unlike pure reference counting.
- If there's a GC, why implement
IDisposable? For objects that own unmanaged or scarce resources, such as handles, sockets and pooled DB connections, whose release can't wait for a non-deterministic collection. - What is the difference between a memory leak and high memory usage? A leak keeps growing across full GCs; high but flat usage after GC is a sizing or allocation-pattern issue, not a leak.
Track a rolling failure rate per destination for circuit decisions
easy
The egress service delivers about 1,500 webhooks per second across roughly 40,000 destinations, each call bounded by a 10 second timeout. Maintain, per destination, the failure rate over the trailing 60 seconds so a caller can ask before dispatch whether the circuit should open. Attempts arrive as (destination_id, finished_at_ms, outcome). Requirement: amortised O(1) per attempt, with total memory bounded by the destination count rather than by traffic. Give the structure, its exact memory, and the rule that stops a destination with three attempts from opening a circuit.
Approach
- Name the exact-deque version and then reject it as the default. Holding timestamps and advancing a tail pointer past anything older than now minus 60 seconds is a correct two-pointer window at amortised O(1) per attempt, but its memory tracks in-window traffic, so one destination in a retry storm holds hundreds of thousands of entries while thousands of quiet destinations hold none.
- Use a ring of 60 one-second buckets per destination, each bucket a pair of counters for attempts and failures. On an attempt, advance the ring by the elapsed whole seconds, zeroing at most min(elapsed, 60) buckets, then increment the head. That is amortised O(1) with a fixed footprint per destination.
- State the footprint: 60 buckets times two 4-byte counters is 480 bytes of payload per destination, so 40,000 destinations is roughly 20 to 25 MB with per-entry overhead, bounded by the catalogue rather than by the rate. The cost is granularity, since the oldest bucket ages out in whole seconds, which is far tighter than the decision needs.
- Require a minimum sample before the circuit may open. A destination with three attempts and three failures reads as 100 percent and is not evidence; a floor of roughly 20 attempts in the window makes the ratio meaningful, and below that floor use a run of consecutive failures as the trigger instead.
Follow-up
- The fleet is 30 instances and each sees roughly a thirtieth of a destination's traffic. Where does the rate actually live, and what does a per-instance answer get wrong?
- A destination answers in 9.5 seconds and succeeds. It is not failing but it is consuming your per-destination concurrency. What signal should open the circuit here?
Collapse a redelivered event batch into per-aggregate high-water marks
easy
You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.
Approach
- One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
- Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
- Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
- If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.
Follow-up
- The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
- How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?
Write a SQL query to find duplicate records in a patient database table.
easyWorked solution
Write a SQL query to find duplicate records in a patient database table.
Approach
- Define duplicate before writing SQL: with a surrogate
patient_idno two rows are byte-identical, so a duplicate means two ids sharing the identifying fields. State the key you'll use, e.g. normalizedfirst_name,last_nameanddate_of_birth. GROUP BYthe key withHAVING COUNT(*) > 1lists each duplicated key once with its count, but drops thepatient_ids needed for cleanup. To return every offending row, attachCOUNT(*) OVER (PARTITION BY key)and filter it in an outer query, sorted by the key.- Normalize before comparing, e.g.
LOWER(TRIM(last_name)), so 'Smith ' and 'smith' match.TRIMstrips only spaces, not tabs or line breaks, and SQLite'sLOWERfolds only ASCII letters, so remove control characters withREPLACEand use ICU where accented names matter. - Exclude rows with a NULL or blank key column:
GROUP BYandPARTITION BYput all NULLs in one group, andTRIMturns every space-only name into '', so either would pair unrelated patients with missing data.NULLIF(TRIM(col), '') IS NOT NULLrules out both. - If asked to remove them, rank with
ROW_NUMBER() OVER (PARTITION BY key ORDER BY patient_id)and treatrn > 1as merge candidates. Child rows such as visits or orders reference those ids, so re-point them to the surviving record before deleting anything. - Mention limits and speed: an index on the key columns, or an expression index on the normalized values, lets the database avoid a full sort. Exact matching misses typos and nicknames yet flags distinct people who share a name and birth date, so its output is candidates for review.
Worked solution 15 min
List every duplicate patient row with a window count
- Drop rows with a missing or blank name, or a missing birth date, inside the subquery, before the window runs, so missing data can't form a fake group.
- Attach
dup_countto each row with a windowCOUNT(*)partitioned by the trimmed, lower-cased names and the birth date; unlikeGROUP BY, every original row survives. - Keep
dup_count > 1in the outer query, since a window result can't be referenced in the same query'sWHERE. - Order by the normalized key and then
patient_id, so each cluster prints together with the oldest record first.
SQL
-- Assumed table: patients(patient_id INTEGER PRIMARY KEY, first_name TEXT,
-- last_name TEXT, date_of_birth TEXT) -- dates stored as 'YYYY-MM-DD'
-- Duplicate = same normalized first name, last name and date of birth.
SELECT patient_id, first_name, last_name, date_of_birth, dup_count
FROM (
SELECT p.*,
COUNT(*) OVER (
PARTITION BY LOWER(TRIM(first_name)),
LOWER(TRIM(last_name)),
date_of_birth
) AS dup_count
FROM patients AS p
WHERE NULLIF(TRIM(first_name), '') IS NOT NULL
AND NULLIF(TRIM(last_name), '') IS NOT NULL
AND date_of_birth IS NOT NULL
) AS t
WHERE dup_count > 1
ORDER BY LOWER(TRIM(last_name)), LOWER(TRIM(first_name)),
date_of_birth, patient_id;
Scroll sideways to view long lines.
EXPECTED RESULTWith rows (1, 'Ann', 'Lee', '1980-01-01') and (3, 'ann ', ' LEE', '1980-01-01') plus a unique patient 2, it returns ids 1 and 3, each with `dup_count` 2. It runs in O(n log n) time for the partition sort and O(n) working space.
Follow-up
- How would you delete the duplicates but keep one? Re-point child rows to the kept id first, then delete only rows with a complete, non-blank key whose
patient_idisn't their group'sMIN(patient_id); incomplete keys go to review. - How would you stop new duplicates? Not with a unique index, since distinct patients can share a name and birth date; index the normalized key, check it at registration with phone or insurance ID, and route likely matches to review.
- How would you catch near-duplicates such as 'Jon' and 'John'? Block on a cheap key such as birth date plus last-name Soundex, then score candidate pairs by edit distance and send high scorers to manual review.
What is database normalization, and what are the trade-offs between normalized and denormalized schemas?
medium
What is database normalization, and what are the trade-offs between normalized and denormalized schemas?
Approach
- Normalization is structuring tables so each fact is stored exactly once, by splitting them along functional dependencies. Its purpose is to remove update, insert and delete anomalies, not to save disk space.
- Know the forms: 1NF means atomic values and no repeating groups; 2NF removes partial dependencies on part of a composite key; 3NF removes transitive dependencies between non-key columns; BCNF requires every determinant to be a candidate key.
- Make the anomalies concrete with an
orderstable that repeatscustomer_addresson every row: changing the address in one row leaves others stale, you can't record a customer who hasn't ordered, and deleting their last order erases them. - Normalized schemas give integrity and cheap, single-place writes at the cost of more joins on read. Denormalized schemas give faster, simpler reads at the cost of redundancy, larger writes and copies that can drift out of sync.
- Match the choice to the workload: normalize OLTP systems to 3NF by default; use denormalized star schemas (fact and dimension tables) for reporting and analytics; denormalize one hot read path only after measuring it, with a defined sync mechanism.
- Avoid two misconceptions: denormalization is a deliberate, maintained copy (trigger, same-transaction update, materialized view), not skipping design; and storing
unit_priceon an order line is not redundancy, since price at time of sale is a different fact from today's price.
Follow-up
- How do 3NF and BCNF differ? BCNF demands every determinant be a candidate key; 3NF tolerates a non-key determinant when the dependent column is part of a candidate key, which is rare in practice.
- Why does 2NF only matter for composite keys? A partial dependency needs a key with more than one column, so a 1NF table whose candidate keys are all single columns is automatically in 2NF.
- Does a JSON column violate 1NF? It does if you query its elements as separate values; it's fine for opaque payloads, but you lose foreign keys and easy indexing on the parts.
How do you optimize a slow-running SQL query?
medium
How do you optimize a slow-running SQL query?
Approach
- Measure before touching it: confirm which statement is slow and by how much (slow query log, Query Store,
pg_stat_statements), and whether it is always slow, slow only for some parameter values, or slow only under load, since those point to different causes. - Read the actual execution plan (
EXPLAIN ANALYZEin PostgreSQL, the actual plan in SQL Server). Look for full scans of large tables, estimated rows far from actual rows, sorts or hashes spilling to disk, and nested loops over large inputs. - Check the predicate is sargable:
WHERE YEAR(created_at) = 2025,LIKE '%term', or an implicit type conversion on the column blocks an index seek. Rewrite as a range such ascreated_at >= '2025-01-01' AND created_at < '2026-01-01'. - Index for the query: a composite index with equality columns first, then the range or sort column, plus included columns so it covers the
SELECTand skips table lookups. Every index taxes inserts and updates, so add the one that matches this access path. - Rewrite if the plan is still poor: select only needed columns, filter before joining or aggregating, turn correlated subqueries into joins or
EXISTS, and replace deepOFFSETpagination with keyset pagination. - Rule out non-query causes: stale statistics (
ANALYZE,UPDATE STATISTICS), a cached plan compiled for an atypical parameter, lock waits from a long transaction, or an N+1 pattern where each query is fast but runs hundreds of times. Re-test with production-sized data.
Follow-up
- How do you order columns in a composite index? Equality columns first, then the range or sort column; columns after the first range predicate can only filter rows, not narrow the seek.
- What is parameter sniffing? SQL Server builds a cached plan from the first call's parameter values, and later calls with very different selectivity reuse that plan;
OPTION (RECOMPILE)orOPTIMIZE FORcan help. - When would you stop tuning the query and change the design? When it aggregates huge ranges on every call; precompute with a summary table or materialized view, or move the reporting to a replica.
Explain the four pillars of Object-Oriented Programming (OOP) and provide a real-world example of how you have
easy
Explain the four pillars of Object-Oriented Programming (OOP) and provide a real-world example of how you have used them.
Approach
- Name all four, encapsulation, abstraction, inheritance and polymorphism, and give each a one-line definition plus the problem it solves; a bare list of names sounds memorized.
- Encapsulation keeps an object's state private and lets it change only through methods that enforce invariants, e.g.
withdraw()rejects an overdraft instead of exposing a settablebalance. Abstraction is the outward contract: callers see what an object does, not how. - Separate those two out loud, since they are easy to blur: abstraction is a design choice about what the interface exposes; encapsulation is the enforcement (access modifiers, no public fields) that hides the implementation behind it.
- Inheritance models an is-a relationship so a subclass reuses and extends a base class. Mention its cost, tight coupling and the fragile base class problem, and that you prefer composition when the relationship is really has-a or you only want code reuse.
- Polymorphism lets one call site work with many types: at runtime through overriding and dynamic dispatch, at compile time through overloading and generics. Show it as a loop over
List<Notifier>callingsend()without knowing the concrete class. - For the real-world half, walk through one component from your own work where all four appear, e.g. an
IReportExporterinterface,CsvExporterandPdfExporter, a shared base for file naming, private buffers. End on the payoff: adding a format changed no callers.
Follow-up
- Which pillars does dependency injection lean on? Abstraction and polymorphism: the class depends on an interface, and the composition root or DI container decides which implementation to supply at runtime.
- What is the Liskov Substitution Principle? A subclass must work anywhere its base type is expected without breaking callers; a
Squarethat breaksRectangle's independent width and height setters is the classic violation. - Is a class with private fields and public getters and setters encapsulated? Only weakly: it still exposes its representation. Real encapsulation exposes behavior like
deposit()that protects invariants.
What is the difference between an interface and an abstract class, and when would you use each?
easy
What is the difference between an interface and an abstract class, and when would you use each?
Approach
- An interface is a contract: members a type promises to provide, no instance fields, no constructors, and a class can implement many. An abstract class is a partially built base class that can't be instantiated, and in Java or C# a class can extend only one class, abstract or not.
- What an abstract class adds: instance state, constructors,
protectedmembers, and a mix of concrete andabstractmethods, so it is the tool for sharing implementation and state across closely related types that truly are-a kind of the base. - Correct the outdated claim that interfaces can't contain code: Java 8 added
defaultandstaticinterface methods and C# 8 added default interface members. The real remaining difference is state and constructors, plus single versus multiple inheritance. - Use an interface for a capability unrelated classes share (
Comparable,IDisposable) and for boundaries you want to swap or mock in tests, like a repository. Use an abstract class for a template method: the base fixes the algorithm and subclasses fillprotected abstracthooks. - Mention versioning: adding a concrete method to an abstract class doesn't break subclasses, while adding an abstract member to either breaks every implementer unless the interface gives it a default body.
- Show that they combine: the interface is the public contract and an abstract base implements it to remove boilerplate, as Java's
AbstractListdoes forList. Callers depend on the interface, and implementers may extend the base or not.
Follow-up
- What if a Java class implements two interfaces with the same default method? It won't compile until the class overrides the method; inside, it can call one explicitly with
InterfaceA.super.method(). - Why does an abstract class have a constructor if you can't instantiate it? It initializes the base's state and runs when a concrete subclass is constructed via
super(...)or: base(...). - How does the choice affect unit testing? Code that depends on an interface can be handed a fake or mock without inheriting any real behavior, which keeps tests fast and isolated.
Explain the difference between method overloading and method overriding.
easy
Explain the difference between method overloading and method overriding.
Approach
- Overloading is several methods with the same name but different parameter lists (count, types or order) in the same class or scope. The compiler picks one from the static types of the arguments, so it is compile-time polymorphism.
- Overriding is a subclass supplying its own implementation of an inherited virtual method with the same signature. The call is resolved at runtime from the object's actual type through dynamic dispatch, so a base-type reference still runs the subclass version.
- Name the one-line contrast: overloading is same name, different signatures, chosen by the compiler; overriding is same signature, different class in the hierarchy, chosen at runtime. Return type alone can't distinguish overloads in Java or C#.
- Give the language rules: in C# the base method must be
virtual,abstractor itself anoverride, and the subclass must sayoverride; in Java any inherited non-static, non-final method can be overridden, and@Overridemakes the compiler verify it. An override can't reduce visibility. - Show the trap that proves understanding: with
print(Object o)andprint(String s),Object x = "hi"; print(x);calls theObjectoverload, because overload resolution uses the declared type, while an overriddenx.toString()still runsString's version. - Mention hiding, which weak answers confuse with overriding: C#'s
newmodifier and Java static methods with the same signature hide the base method, so which one runs depends on the reference's declared type, not the object.
Follow-up
- Can a private method be overridden? No; private methods aren't inherited, so a same-named method in the subclass is unrelated and calls inside the base class still run the base version.
- What does
@Overrideor C#'soverridekeyword buy you? A compile error if the method doesn't actually override anything, which catches signature typos that would silently create an overload. - Can an override change the return type? Only to a subtype (covariant return), supported in Java and in C# 9 and later; the parameter list must stay identical.
Explain the lifecycle of an HTTP request in an ASP.NET framework.
medium
Explain the lifecycle of an HTTP request in an ASP.NET framework.
Approach
- Answer for ASP.NET Core, then map classic ASP.NET. The server (Kestrel, alone or behind a reverse proxy, or IIS via the ASP.NET Core Module) parses the request into an
HttpContextholdingRequest,Responseand a per-request DI scope inRequestServices. - It then runs the middleware pipeline in the order registered in
Program.cs. Each component receives the context and anextdelegate; a typical order is exception handler, HTTPS redirection, static files, routing, CORS, authentication, authorization, then the endpoint. - A middleware that doesn't call
nextshort-circuits the rest, which is how static files are served without MVC running.UseRoutingonly selects the endpoint and stores it on the context, so authorization can read its[Authorize]metadata before the endpoint executes. - MVC runs authorization and resource filters, creates the controller, binds and validates into
ModelState, then runs action filters, the action and result filters. Exception filters catch errors from controller creation through the action, not from resource or result filters or result execution. - The response unwinds back through the middleware in reverse order, so timing or logging code after
await next()sees the result. Once headers are sent (Response.HasStarted), the status code and headers can't change; scoped services are disposed when the request ends. - In classic ASP.NET (System.Web),
HttpApplicationraisesBeginRequest,AuthenticateRequest,AuthorizeRequest;UrlRoutingModulemaps the route toMvcHandleratPostResolveRequestCache; thenAcquireRequestStateloads session for that handler, the handler runs, andEndRequestfires.
Follow-up
- Why must
UseAuthenticationcome beforeUseAuthorization? Authorization checksHttpContext.User, which the authentication middleware populates; reversed, every request looks anonymous. - Middleware or filter for cross-cutting logic? Middleware runs for every request and knows nothing about MVC; filters run only for MVC endpoints and can see action arguments,
ModelStateand the result. - How do DI lifetimes relate to a request? Scoped services live for one request; injecting one into a singleton captures it, causing stale or thread-unsafe state across requests.
What are the differences between SaaS, IaaS, and PaaS, and can you give examples of when to use each?
easy
What are the differences between SaaS, IaaS, and PaaS, and can you give examples of when to use each?
Approach
- Define the three by who manages what. IaaS: the provider runs hardware, networking and virtualization, and you manage the OS, patching, runtime, app and data, e.g. Azure Virtual Machines or AWS EC2.
- PaaS: the provider also runs the OS, runtime and scaling machinery; you deploy code and configuration and own the data, e.g. Azure App Service, Azure SQL Database or AWS Elastic Beanstalk.
- SaaS: a finished application used over the web; you configure it and manage users, permissions and your data, e.g. Microsoft 365, a hosted ticketing system or a CRM.
- When to use each: IaaS for lift-and-shift of legacy apps or anything needing OS-level control or custom installs; PaaS for new web apps and APIs where the team should ship code rather than patch servers; SaaS for commodity capabilities that aren't worth building.
- Name the trade-off axis: moving from IaaS to PaaS to SaaS, operational burden drops, while control, customization and portability drop too and dependence on provider-specific services grows. Serverless functions (Azure Functions, AWS Lambda) push PaaS further.
- Correct the misconception that SaaS makes security the vendor's problem: under the shared responsibility model you always own your data, identities, access settings and compliance obligations; only the infrastructure line moves.
Follow-up
- Where does managed Kubernetes fit? AKS or EKS is often called CaaS: the provider runs the control plane while you own images, workloads and node settings, sitting between IaaS and PaaS.
- Is SQL Server on an Azure VM the same as Azure SQL Database? No; on a VM it's IaaS and you patch, back up and configure HA yourself, while Azure SQL Database is PaaS with those handled.
- How would you move a legacy on-prem app to the cloud? Often lift-and-shift onto IaaS first for speed, then refactor parts onto PaaS once it runs and you can measure it.
How would you use PowerShell to automate a repetitive deployment or system configuration task?
medium
How would you use PowerShell to automate a repetitive deployment or system configuration task?
Approach
- Anchor on one concrete task, e.g. deploying a web build to several IIS servers, painful by hand because of many steps and drift between environments. Script it with a typed, validated
param()block and read server lists and paths from a per-environment.psd1or JSON file. - Support dry runs with
[CmdletBinding(SupportsShouldProcess)]and anif ($PSCmdlet.ShouldProcess($server, 'Deploy'))guard around each change.-WhatIfdoes not carry intoInvoke-Commandscript blocks on remote machines, so make that check locally before calling them. - Make every step idempotent so a rerun after a partial failure is safe: check before changing (
Test-Path,Get-WindowsFeature, comparingGet-FileHash). For long-lived machine configuration, Desired State Configuration declares the end state instead of scripting each step. - Handle errors explicitly: many cmdlet errors are non-terminating and skip
try/catchunless you set$ErrorActionPreference = 'Stop'or pass-ErrorAction Stop. AddSet-StrictMode -Version Latest, log withStart-Transcript, and exit non-zero so the pipeline fails. - Reach many machines with
Invoke-Command -ComputerName $servers -ScriptBlock {...}, which runs over PowerShell remoting in parallel. For a rolling deploy, per server: drain from the load balancer, stop the app pool, copy, start, hit a health check, re-enable, and halt on the first failure. - No plaintext secrets: use
Get-Credential, SecretManagement or a vault lookup from CI. Sign scripts and setAllSignedto catch tampered or accidental runs, but execution policy isn't a security boundary, so also limit who can run them. Add Pester tests and run from a pipeline, not a laptop.
Follow-up
- How do you pass a local variable into a remote script block? Reference it as
$using:nameinside the block or pass it with-ArgumentList; the remote session can't see local variables otherwise. - How would you roll back? Keep the previous release in a versioned folder and point the IIS site's physical path back to it, driven by the same script with a
-Versionparameter. - Imperative script or DSC? A script describes how to change things; DSC declares the desired state, and its test-then-set cycle converges the machine and reports drift, better for ongoing configuration.
How does horizontal scaling differ from vertical scaling in a cloud environment?
easy
How does horizontal scaling differ from vertical scaling in a cloud environment?
Approach
- Vertical scaling (scale up) gives one instance more CPU, memory or faster disk. It needs no code change, but it hits a ceiling at the largest size, usually needs a restart to resize, and leaves a single point of failure.
- Horizontal scaling (scale out) adds more instances behind a load balancer. It has no practical ceiling for stateless tiers, adds redundancy, and can autoscale on metrics such as CPU, request rate or queue depth, then scale in to cut cost.
- Scaling out requires a stateless app tier: move session state to a distributed cache such as Redis or to the database, put uploads in object storage, and coordinate scheduled jobs so two instances don't both run them. Sticky sessions are only a stopgap.
- Databases are the hard case: a relational primary usually scales up. Scaling out means read replicas, with replication lag that breaks read-your-own-writes, or sharding, where cross-shard joins and transactions get hard.
- Warn about the classic mistake: adding app instances doesn't help when a shared dependency is the bottleneck and can make it worse, e.g. each instance's connection pool multiplies database connections until the server's limit is hit. Find the bottleneck first.
- In practice combine them: pick an instance size that runs efficiently, then scale the count out for load and resilience. In Azure App Service terms, scale up means changing the pricing tier and scale out means changing the instance count.
Follow-up
- How would you configure autoscaling? Scale out on a leading metric like queue depth or request rate with a cooldown, scale in more conservatively to avoid flapping, and set minimum and maximum instance counts.
- How does the load balancer spot an unhealthy instance? A health probe; failing instances leave rotation. Keep the probe about the instance itself, since checking a shared database can pull every instance out at once during a brief blip.
- How do costs differ? Scaling up means paying for peak capacity around the clock; autoscaled horizontal capacity pays for baseline and adds instances only at peak, though each instance has fixed overhead.
Walk me through a challenging technical problem you solved in a past project. How did you identify the root ca
medium
Walk me through a challenging technical problem you solved in a past project. How did you identify the root cause?
Approach
- Pick a problem where the cause was non-obvious and you personally drove the diagnosis, e.g. an intermittent failure or a performance regression. The answer has to show a method for getting from symptom to root cause, not just the fix.
- State the symptom and stakes in about 30 seconds: what users or systems saw, how often, what it cost, and why the obvious fixes didn't work. Save the time for the investigation.
- Walk through the diagnosis as hypotheses and evidence: how you reproduced or captured it, how logs, metrics or traces narrowed the scope, what you ruled out and how, and the tool that cracked it (profiler, heap dump,
git bisect, packet capture, query plan). - Separate root cause from trigger: explain why it happened, not only which line changed, e.g. "a retry loop without backoff amplified a slow dependency", and how you confirmed it, such as the repro disappearing with the fix and returning without it.
- Finish with the fix, the guardrail against recurrence (test, alert, lint rule, runbook), and numbers: errors from X a day to zero, p95 from 2 s to 300 ms, hours of on-call saved.
- Avoid a story where your role hides behind "we", one that's really about a deadline, or one solved by luck. Choose one whose technical details you still remember precisely, down to the tool output that confirmed the cause.
Follow-up
- What would you do differently? Name a faster step you'd now take first, e.g. adding correlation IDs or a targeted metric earlier, rather than saying nothing.
- How did you know it was the root cause and not a coincidence? Describe a controlled test: reintroducing the cause brought the bug back and the fix removed it under the same conditions.
- How did you keep stakeholders informed while it was unresolved? Regular updates with what was known, the mitigation in place, and when the next update would come.
Built from the rounds and topics Upmc candidates report.
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- Write out the reported sequence: HR Phone Screen, Technical Assessment, Behavioral Panel.
- 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 3 reported rounds, with the weakest marked.
02Work OOP Fundamentals
- Spend the session on OOP Fundamentals, which Upmc candidates report being tested on.
- Write one worked example in OOP Fundamentals and time yourself on it.
Deliverable: One timed worked example in OOP Fundamentals.
03Work Data Structures
- Spend the session on Data Structures, which Upmc candidates report being tested on.
- Write one worked example in Data Structures and time yourself on it.
Deliverable: One timed worked example in Data Structures.
04Work Algorithms
- Spend the session on Algorithms, which Upmc candidates report being tested on.
- Write one worked example in Algorithms and time yourself on it.
Deliverable: One timed worked example in Algorithms.
05Answer out loud: Object-Oriented Programming (OOP) & Fundamentals
- Answer aloud, timed: Explain the four pillars of Object-Oriented Programming (OOP) and provide a real-world example of how you have used them.
- Answer aloud, timed: What is the difference between an interface and an abstract class, and when would you use each?
Deliverable: Spoken answers to 2 reported Object-Oriented Programming (OOP) & Fundamentals question(s), under time.
06Answer out loud: Database & Backend Development
- Answer aloud, timed: Write a SQL query to find duplicate records in a patient database table.
- Answer aloud, timed: What is database normalization, and what are the trade-offs between normalized and denormalized schemas?
Deliverable: Spoken answers to 2 reported Database & Backend Development question(s), under time.
07Answer out loud: Cloud Infrastructure & Scripting
- Answer aloud, timed: What are the differences between SaaS, IaaS, and PaaS, and can you give examples of when to use each?
- Answer aloud, timed: How would you use PowerShell to automate a repetitive deployment or system configuration task?
Deliverable: Spoken answers to 2 reported Cloud Infrastructure & Scripting question(s), under time.
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Behavioural rounds judge the decision you made and what it cost.
Describe your experience with Microsoft Azure services and how you secure cloud-hosted applications.
medium
Describe your experience with Microsoft Azure services and how you secure cloud-hosted applications.
Approach
- This tests hands-on depth and security judgment, not a list of service names. Anchor on one or two systems you actually ran on Azure: which services (App Service, Functions, Azure SQL, Storage, Key Vault) and why each. If your Azure time is thin, say so and map from the cloud you know.
- Frame the security half as layers, each with a control you actually applied: identity, secrets, network, data, and monitoring. A layered model shows judgment; a flat list of features sounds rehearsed.
- Identity: Microsoft Entra ID (formerly Azure AD) for sign-in, managed identities so the app gets tokens without stored credentials, and Azure RBAC with least privilege scoped to a resource or resource group, not Owner or Contributor on the subscription.
- Secrets and data: Key Vault for whatever a managed identity can't replace, referenced from app settings and rotated. Encryption at rest is on by default for Storage and Azure SQL; enforce TLS 1.2 or higher and disable anonymous blob access and shared-key auth where you can.
- Network and detection: private endpoints so databases aren't reachable from the internet, a WAF (Application Gateway or Front Door) in front of public apps, Defender for Cloud recommendations, and diagnostic logs sent to Log Analytics with alerts on them.
- Quantify and don't overclaim: e.g. "replaced 12 connection-string secrets with managed identity" or audit findings closed. Name only services you can explain one level deeper, such as how a managed identity obtains its token.
Follow-up
- How does a managed identity get a token without a secret? The platform exposes a local token endpoint only that resource can reach;
DefaultAzureCredentialcalls it and Entra ID issues a short-lived token. - What's the difference between Azure RBAC and Entra ID roles? Azure RBAC governs access to Azure resources such as storage and VMs; Entra ID roles govern directory administration such as users and app registrations.
- How would you guarantee no storage account is ever public? Disable anonymous access and public network access, use private endpoints, and enforce it across subscriptions with Azure Policy.
Describe a time when you had a conflict with a coworker. How did you resolve it, and what was the outcome?
medium
Describe a time when you had a conflict with a coworker. How did you resolve it, and what was the outcome?
Approach
- This tests whether you can disagree without damaging the working relationship, own your part, and still reach a decision. Pick a real disagreement about the work (design approach, code review, priorities, ownership), not a personality clash.
- Avoid stories where the coworker is the villain, where you went to the manager first, where the point is that you were right, or the "I've never had a conflict" non-answer; each signals low self-awareness.
- Set it up in two sentences: what you were building, what each of you wanted, and why it mattered (deadline, risk, users). Describe their position fairly enough that they would agree with your summary.
- Spend most of the answer on your actions: you raised it one-on-one early, asked what constraint drove their view, named the shared goal, and moved to evidence such as a quick prototype, benchmark or written trade-off doc, or agreed on who would decide.
- Give the outcome with numbers and the relationship: what was decided, what shipped, the measurable result (e.g. "shipped on the planned date, p95 latency down 30%"), and how you work with that person now. If their approach won, say so.
- Close with one specific habit the conflict changed, e.g. "I now write a one-page options doc before a design debate"; a concrete behavior change is more convincing than "I learned communication matters".
Follow-up
- What if you still couldn't agree? Agree up front on a decider or on criteria, escalate together with both options laid out, and commit fully once the call is made.
- What if the conflict were with your manager? Same approach with more context-seeking: ask what they're optimizing for, disagree privately with data, then commit.
- What would the coworker say about how you handled it? Answer honestly, including anything you'd do differently, such as raising the concern sooner.
How do you handle a situation where project requirements change late in the development cycle?
medium
How do you handle a situation where project requirements change late in the development cycle?
Approach
- Show judgment under change: not a reflexive yes (silent overtime, quality slips) and not a rigid no, but impact analysis, a trade-off conversation, and protected quality. Give your method, then prove it with a real instance.
- Start by understanding the change: what problem it solves, who is asking, and whether it's needed in this release or can follow. Often the underlying need is smaller than the request.
- Size the impact concretely (effort, components and tests touched, regression risk, what work it displaces) and turn it into options: move the date, swap out other scope, ship a minimal version behind a feature flag and follow up, or defer.
- Let the owner choose with the trade-offs visible, record the decision in the ticket or an email, and tell everyone affected, such as QA and dependent teams. Don't absorb the change quietly or cut testing to hold the date.
- In the story, quantify: how late it came (e.g. "two weeks before release"), what it cost, which option was chosen, and the outcome, such as shipping on time with reduced scope and no severe defects.
- End with prevention: what you changed upstream, such as earlier demos, acceptance-criteria reviews, a change-freeze window or smaller increments, so the next late change costs less.
Follow-up
- What if the change is mandatory, e.g. a legal or compliance requirement? Then the date or other scope must move; say what gets cut, add risk-based testing around the change, and announce the new plan early.
- What if the stakeholder insists on the original date and full scope? Put the risk in writing, propose the smallest safe slice, and escalate the trade-off instead of quietly lowering quality.
- How do you keep code ready for late changes? Small, well-tested increments, clear module boundaries and feature flags keep a late change local instead of sweeping.
Tell me about a time you had to explain a complex technical concept to a non-technical stakeholder or clinicia
easy
Tell me about a time you had to explain a complex technical concept to a non-technical stakeholder or clinician.
Approach
- The goal is to turn technical detail into what the listener needs to decide or do, while respecting their expertise; for a clinician that means their workflow and time pressure. Pick a story where their understanding changed a decision or behavior.
- Choose a concept that was genuinely hard, e.g. why data isn't available instantly, why a change needs downtime, or why an output is a probability rather than a certainty. Avoid framing the listener as slow or saying you "dumbed it down".
- Set the scene briefly: who they were by role, what they needed to decide, which concept stood in the way, and what had happened with earlier explanations, if any.
- Show your technique: start from their goal and the impact on them rather than the architecture, use one analogy from their world, drop jargon or define it once, add a simple visual or demo, and have them explain it back or walk through their own scenario.
- State the outcome and quantify it: the decision made, adoption, fewer support tickets or escalations. Add what you learned about that audience, such as leading with workflow impact.
- An illustrative shape, e.g.: a report lagged 15 minutes because data syncs in batches; you compared it to a scheduled mail pickup, and the stakeholder then chose the two views that truly needed live data, which scoped the work down.
Follow-up
- How did you check they really understood? Have them restate it in their own terms or walk through their scenario, rather than asking "does that make sense?"
- What if they rejected the technical constraint? Separate the constraint from the options: show what removing it would cost and let them weigh that against the benefit.
- How would you adapt for an executive versus a front-line user? Executives need the decision, cost and risk in a minute; front-line users need what changes in their daily steps.
- 01
Describe your experience with Microsoft Azure services and how you secure cloud-hosted applications.
- 02
Describe a time when you had a conflict with a coworker. How did you resolve it, and what was the outcome?
- 03
How do you handle a situation where project requirements change late in the development cycle?
- 04
Tell me about a time you had to explain a complex technical concept to a non-technical stakeholder or clinician.
How difficult is the technical interview at UPMC?
Candidates generally describe the technical interview as easy to average. The focus is heavily on fundamental computer science concepts, basic coding, SQL queries, and OOP principles rather than ultra-complex algorithmic puzzles.
Upmc Software Engineer candidate reports ↗What technology stack does UPMC primarily use?
While technologies can vary by team, UPMC is largely a Microsoft-centric shop. You can expect a heavy emphasis on C#,.NET, SQL Server, PowerShell, and Microsoft Azure.
Upmc Software Engineer candidate reports ↗How long does the hiring process take?
The process typically takes between three to six weeks from the initial phone screen to an offer. However, some candidates have experienced administrative delays, so active follow-up via the candidate portal is recommended.
Upmc Software Engineer candidate reports ↗Is there a dress code for virtual or onsite interviews?
Although the day-to-day working environment at UPMC is typically business casual or casual, it is highly recommended to dress professionally (business formal or smart business casual) for all interview rounds, whether virtual or in-person.
Upmc Software Engineer candidate reports ↗What topics does Upmc test in interviews?
Upmc interviews most often cover SQL, Behavioral Interviewing, Stakeholder Management, Data Structures, and Panel Interviewing. The exact emphasis depends on the specific role you apply for.
Upmc 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.
- 01Upmc 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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