Microsoft SDET (Software Development Engineer in Test) Entry Level Interview Preparation Guide

Direct answer

Below is a reusable HTTP GET function with retry and exponential backoff for transient server errors and network errors, with configurable attempt count and base delay.

Structured elaboration

The function distinguishes what's worth retrying (a timeout, a connection error, a 5xx that's likely transient) from what isn't (a 4xx, which means the request itself is wrong and retrying an unmodified request will just fail the same way again), and backs off exponentially between attempts so a struggling server isn't hit with an immediate retry storm.

Worked example

import time
import requests


def resilient_get(url, max_attempts=5, initial_backoff=0.5, backoff_factor=2,
                   retry_statuses=(500, 502, 503, 504)):
    last_exception = None
    delay = initial_backoff

    for attempt in range(1, max_attempts + 1):
        try:
            resp = requests.get(url, timeout=5)
            if resp.status_code not in retry_statuses:
                return resp  # success, or a non-retryable error (e.g. 4xx): return as-is
            last_exception = None
        except (requests.ConnectionError, requests.Timeout) as e:
            last_exception = e
            resp = None

        if attempt == max_attempts:
            if last_exception:
                raise last_exception
            return resp  # exhausted retries on a retryable status; return the last response

        time.sleep(delay)
        delay *= backoff_factor

    raise RuntimeError("unreachable")  # defensive; loop always returns or raises above

Thread-safety. As written, resilient_get has no shared mutable state at all: delay, attempt, and last_exception are all local to each call, so many test threads calling it concurrently don't interact with each other in any way. The one thing worth being deliberate about in concurrent use is the underlying requests session: this version uses the module-level requests.get, which creates a new connection per call and is safe under concurrency but doesn't reuse connections. If you switch to a shared requests.Session() for connection pooling (a reasonable optimization under high concurrency), the Session object itself needs to be either one per thread or explicitly documented as thread-safe for your use case, since requests.Session is not guaranteed thread-safe for concurrent use by requests' own documentation.

Verified with a fixture that fails twice with a 503 and then succeeds:

call_count = [0]

def flaky_get(url, timeout):
    call_count[0] += 1
    class FakeResp:
        status_code = 503 if call_count[0] <= 2 else 200
    return FakeResp()

requests.get = flaky_get  # monkeypatched into requests.get for this test

resp = resilient_get("http://fake/x", initial_backoff=0.01)
print(f"attempts made: {call_count[0]}")
print(f"final status_code: {resp.status_code}")

Running resilient_get against this fixture (with initial_backoff=0.01 to keep the test fast) returns a 200 after exactly 3 attempts, confirming the retry loop and the eventual-success path both work:

attempts made: 3
final status_code: 200

Trade-offs and pitfalls

A 4xx status code deliberately does NOT trigger a retry in this implementation: retrying an unmodified request that the server has already rejected as invalid wastes time and, in the worst case, can look like an attempted abuse pattern to the server (repeated requests to an endpoint that keeps rejecting them). If a caller genuinely wants to retry a 429 (rate-limited) specifically, that status needs to be added to retry_statuses deliberately, and ideally the delay should respect a Retry-After header if the server provides one, rather than blindly following the function's own generic backoff schedule.

Direct answer
The situation I'd describe is a mid-sized project where my manager wanted me to present a set of results to a client as more conclusive than the underlying data actually supported, because the client relationship was under strain and a confident-sounding update would help. My personal value was straightforward accuracy in what I present, even when the more cautious version is less comfortable to deliver; my manager's approach prioritized relationship repair over precision in that specific moment. I did not treat it as a fight to win outright; I looked for a version of the update that was honest and still served the relationship.

Structured elaboration

1. Name the actual tension precisely, not just "we disagreed." In this case it was not that my manager wanted me to lie; it was a difference in where to draw the line between appropriately confident communication and overstating certainty, which is a much more common and more defensible kind of workplace values conflict than an outright integrity violation.
2. Raise the concern directly and early, privately, before the moment it would matter (the client meeting), rather than either silently complying or making it a public confrontation. I asked my manager one on one what specifically in the data supported the stronger framing, which turned the conversation from a disagreement about values into a conversation about evidence.
3. Offer an alternative that serves the underlying goal your manager actually cares about. My manager's real goal was preserving the client relationship, not the specific wording; I proposed a version that led with the two results we were genuinely confident in, was transparent about the one metric still trending in the wrong direction, and paired it with a concrete next step and timeline. This served the relationship-repair goal without requiring me to overstate anything.
4. Be honest about what you would do if the answer had been no. If my manager had insisted on the original framing after that conversation, my actual next step would have been to ask to attach a short written appendix with the caveated numbers, so the honest version existed in the record even if it wasn't the headline; if that had also been refused, I would have escalated to my manager's manager rather than either comply silently or refuse outright, because the stakes (client trust, and my own credibility if the caveated number surfaced later) were high enough to warrant it.
5. Reflect honestly on what you learned, including about your own judgment, not only about the other person. I learned that raising the concern as a specific evidentiary question ("what supports this framing") got further, faster, than raising it as a values statement ("I'm not comfortable with this") would have, because it gave my manager something concrete to respond to.

Worked example
The client update, as originally proposed, said: "engagement is up and the rollout is on track." What the underlying data actually showed: two of three key metrics had improved meaningfully, but the third (a retention metric the client cared about specifically) had been flat to slightly down for three weeks running, with a plausible but unconfirmed hypothesis for why. The version I proposed and we ultimately sent said: "engagement and adoption are both up meaningfully this period; retention is currently flat, and we have identified a likely cause we're testing a fix for over the next two weeks, with a follow-up update once we have results." The client's actual reaction was more positive than my manager expected, specifically because the concrete next step read as more credible than an unqualified "on track" would have.

Trade-offs & pitfalls
The common failure in answering this question is picking an example that is really just "I disagreed with a decision," with no genuine values dimension, or the opposite extreme, an example so severe (fraud, safety, legal risk) that it reads as a one-time crisis story rather than the kind of ordinary, recurring tension this question is actually probing for. Another pitfall is describing the resolution as pure capitulation ("I raised it once, they said no, I dropped it") or pure martyrdom ("I refused and it cost me"), neither of which shows the judgment interviewers are actually testing for: the ability to find a version of the truth that serves both your own integrity and the legitimate underlying goal the other person had.

Situation & quick detection
I’d first rely on CI signals: failing integration/unit tests, sudden pipeline regressions, and monitoring alerts from contract tests (consumer-driven or schema validations). I’d triage by running the failing tests locally with verbose logs and API diffs (compare request/response against recorded contracts).

Communicate rapidly

• Post a concise incident note in the team channel (what failed, test IDs, failing assertions, timestamp).
• Open a high-priority ticket linking CI logs, request/response diffs, and a minimal repro.
• Ping the owning service on-call and include suggested rollback if the change is blocking release.

Coordinate a fix

• Offer a short hotfix: revert the public API change or add backward-compatible shim on the service while devs implement contract-aligned changes.
• If rollback isn’t possible, propose a temporary adapter in the test framework to accept both shapes and add strict validation toggles.

Short-term remedies

• Add a feature-flagged adapter in tests to restore pass-through and unblocks pipeline.
• Run a focused CI gate that validates both old and new contracts before merging further changes.

Medium-term fixes

• Implement consumer-driven contract tests (e.g., Pact) and enforce them in PR checks.
• Add schema validation and automated contract diff alerts to the CI with clear ownership.
• Improve release process: require API change RFCs and a coordinated deprecation window.

Outcome & learnings
I’d aim to minimize downtime by unblocking CI quickly, coordinate a safe fix with devs, and then harden the pipeline to prevent recurrence.

Direct answer

A healthy suite is one the team trusts, that gives fast feedback, that catches real defects, and that costs a sustainable amount to maintain. No single number shows all four, so I track a small set of metrics, one group per question, and for each I know how it is measured, which trend worries me, and the one action it triggers. (CI means continuous integration, the automated system that builds and tests every change. A flaky test is one that passes and fails on the same code. Quarantine means moving an unreliable test out of the gate that blocks merges. p95 is the value 95% of observations fall below, so it describes the slow tail rather than the average, and p50 is the median. Test rot is a suite decaying as the product changes and tests stop matching it. Escaped defects are bugs found after release. Wall-clock time is elapsed real time, not CPU time. To shard and parallelise is to split the suite into pieces that run at once on several machines. A runner is the machine that executes CI jobs. A triage rota is a rotating on-call duty to look at red builds. Hard-coded waits are fixed sleeps, such as waiting 5 seconds for a page, instead of waiting for a condition. If you are starting from nothing, begin with pass rate, flake rate and duration; the cost and deflection rows are organisation-level extras.)

The metrics, grouped by the question they answer

Reading the table by question: can I trust the suite (pass rate, flake rate, quarantine size and age); is it fast (duration and queue time, time to green); does it find bugs (defect escape rate); is it affordable (maintenance cost per test, manual-test deflection).

Targets, cadence and the organisation view

• I set targets against each team's own baseline first (for example, "no quarantined test older than a fixed number of days"), not a universal number borrowed from another company.
• The team reviews a one-page view weekly; an engineering manager reviews the trends monthly with the same definitions rolled up across teams. The organisation dashboard adds maintenance cost per test and manual-test deflection, because those are the numbers that show whether automation is paying for itself.

Worked example

A team has 1,200 tests. A quarter ago it spent 42 engineer-hours a month on test upkeep. Now it spends 60.

• Then: 42 h x 60 min / 1,200 tests = 2.1 minutes per test per month.
• Now: 60 h x 60 min / 1,200 tests = 3.0 minutes per test per month, about 43% more (60 / 42 = 1.43).
• Meanwhile pass rate stayed at 98% and quarantine grew from 10 to 45 tests. The pass rate alone says "fine"; the cost and quarantine metrics say the suite is quietly decaying. The action is to review the 45 quarantined tests, delete or fix them by their expiry date, and look at which tests take the most upkeep.

Defect escape rate traced: a release had 15 defects, 12 caught before release and 3 escaped, so 3 / (3 + 12) = 20%. If pass rate stayed 98% while that rate went from 20% to 30% over two releases, the suite is passing yet missing more.

Trade-offs and pitfalls

• Any single metric can be gamed (skipping tests lifts pass rate, deleting tests lifts everything), so I pair each with a counter-metric, a second number that exposes the cheat: pass rate with skipped and quarantined counts, duration with escape rate (a suite made fast by deleting tests will show escapes rising).
• Too many metrics means none is acted on. Each one must map to a decision; if it never changes a decision I drop it.
• Raw line coverage is deliberately absent: it says code was executed, not that behaviour was checked. I prefer coverage of critical user paths.

Approach (brief)

• Use TestNG DataProvider to supply browser names and capabilities.
• Use ThreadLocal<WebDriver> for thread-safe driver per test.
• Annotate tests and DataProvider to run in parallel.

Code example

import org.testng.annotations.*;
import org.openqa.selenium.*;
import org.openqa.selenium.chrome.*;
import org.openqa.selenium.firefox.*;
import org.testng.Assert;

public class CrossBrowserTest {
  private static ThreadLocal<WebDriver> tlDriver = new ThreadLocal<>();

  private WebDriver getDriver() { return tlDriver.get(); }

  @DataProvider(name = "browsers", parallel = true)
  public Object[][] browsers() {
    return new Object[][] {
      {"chrome"}, {"firefox"}
    };
  }

  @BeforeMethod
  @Parameters("browser")
  public void setUp(Object[] params) {
    String browser = (String) params[0];
    WebDriver driver;
    if ("chrome".equalsIgnoreCase(browser)) {
      ChromeOptions opts = new ChromeOptions();
      opts.addArguments("--headless=new"); // example capability
      driver = new ChromeDriver(opts);
    } else {
      FirefoxOptions opts = new FirefoxOptions();
      opts.setHeadless(true);
      driver = new FirefoxDriver(opts);
    }
    tlDriver.set(driver);
    getDriver().manage().window().maximize();
  }

  @Test(dataProvider = "browsers")
  public void sampleE2ETest(String browser) {
    WebDriver d = getDriver();
    d.get("https://example.com");
    Assert.assertTrue(d.getTitle().contains("Example"));
  }

  @AfterMethod
  public void tearDown() {
    if (getDriver() != null) { getDriver().quit(); tlDriver.remove(); }
  }
}

Notes

• Run TestNG with parallel="methods" or "tests" in testng.xml or configure suites in CI.
• Use remote WebDriver/DesiredCapabilities for Selenium Grid/Cloud providers.

Direct answer

Name your actual top two or three non-compensation priorities, in a real priority order, and explain how you'd weigh them against each other when they point in different directions, since with pay and title held equal, that's exactly what the question is testing.

The framework

1. Pick priorities you can rank, not a flat list: product or mission impact, team and manager quality, learning and mentorship, technical or process maturity, and autonomy are the common axes; naming three and ranking them is stronger than naming six with equal weight.
2. Explain how you'd verify each one during the process, not just what you'd ask for in the offer letter: concrete sources like current employees, public engineering or product writing, or specific interview questions.
3. Show you understand the trade-off structure: many real choices are exactly two-offer comparisons where the axes conflict, mission-driven but slower-moving versus fast-growing but less defined, deep mentorship versus direct product impact, nonprofit versus commercial. Naming a real conflict you'd have to resolve is stronger than implying one company would win on everything.
4. Tie the ranking to where you actually are in your career right now, since the right answer changes over time and saying so is a sign of self-awareness, not indecision.

Worked example

Right now my top priority is product impact and ownership of a defined problem, ahead of brand or stability, because I want to build a track record of shipping things that mattered, not just being present. If [Company A] were mission-driven but slower-moving, with a clearer sense of purpose but less individual ownership, and [Company B] were fast-growing with a less defined mission but more scope handed to individual contributors, I'd weigh scope and ownership higher right now and lean toward [Company B], while checking during the process whether its speed comes at the cost of the kind of technical or process maturity I'd need to actually execute well.

Trade-offs and pitfalls

The weak version of this answer treats every factor as equally important, which reads as indecisive rather than thoughtful; the strong version picks a real ranking, names a genuine trade-off between two plausible offers, and explains why that ranking fits where you are right now, not a universal truth.

An 80%-UI-test regression suite is an inverted pyramid: the CI cost and flakiness live disproportionately at the most expensive, least precise level. The goal of a migration plan here is not "delete the UI tests," it is "prove the same business coverage more cheaply, then retire the UI test only once its replacement is proven equivalent."

1. Inventory

Catalog every UI test by what it actually verifies, not by its name: for each test, identify the underlying business assertion (for example, "a discount code reduces the order total correctly") separately from the UI mechanics used to exercise it (clicking through a cart page). Many UI tests will turn out to duplicate the same handful of business assertions through slightly different click paths, which is valuable information for step 2.

2. Selection criteria for migration candidates

Prioritize migrating a UI test to the API level when: (a) its business assertion does not depend on rendering, layout, or client-side interaction behavior itself, meaning the same assertion can be verified by calling the API directly; (b) it is one of several UI tests covering the same underlying business rule, since only one of them needs to stay at the UI level to prove the flow renders correctly, while the rest can move down; (c) it is currently a source of flakiness (timing-dependent, brittle selectors), since those are exactly the tests whose UI framing is adding risk without adding proportional confidence. Leave at the UI level anything whose actual subject IS the rendering or interaction behavior itself (does the button visibly disable during submission, does a validation message appear in the right place).

3. Incremental rollout strategy

Migrate in small batches grouped by business area (checkout, account management), running the new API-level test and the old UI test IN PARALLEL for one full release cycle before retiring the UI test, so you have a real comparison window rather than trusting the migration on faith. Start with the batch identified as most duplicative and most flaky in the inventory, since that batch gives the fastest CI-time win with the least coverage risk.

4. Metrics to track parity

Track, per migrated batch: the number of distinct production defects each UI test has caught historically (from incident postmortems or bug trackers) against whether the new API-level test would have caught the same defects if replayed against the historical bug; overall CI wall-clock time before and after; and flakiness rate (failures that resolve on rerun with no code change) before and after. A drop in caught-defect equivalence for a batch is the signal to keep more of that batch's UI coverage rather than fully retiring it.

5. Risk mitigation during the transition

Never retire a UI test until its replacement has run in parallel for a full cycle with no coverage gap identified; keep a small, deliberately curated UI layer for the handful of assertions that are genuinely about rendering and interaction, since no amount of API-level testing can verify those; and treat the migration as reversible, keeping the retired UI tests in version control (not deleted) for one additional cycle in case a gap surfaces late.

Trade-offs and pitfalls

The main pitfall is treating "80% UI tests" as inherently wrong without checking what those tests actually verify: if a genuinely large share of your business coverage requires rendering and interaction assertions (a highly visual, interaction-heavy product), a smaller UI share than 80% might still be too aggressive a cut. The inventory step exists precisely to avoid migrating tests whose real subject the API level cannot see.

Direct answer
Five Whys, fishbone (Ishikawa) diagrams, fault-tree analysis, and causal-chain or timeline analysis are all structured root-cause techniques, but they suit different incident shapes. Five Whys is fast and best for a single, mostly-linear chain of causation. Fishbone is best when you suspect several independent categories of cause (people, process, technology, environment) and want to brainstorm broadly before narrowing. Fault-tree analysis is best for complex, multi-path failures where you need to reason about combinations of conditions, not just one chain. Causal-chain or timeline analysis is best when the incident unfolded over a long period with many events, and reconstructing the sequence itself is most of the work.

Structured elaboration

• Five Whys. Strength: fast, requires no special tooling, good for straightforward incidents with a genuinely linear cause. Weakness: it forces a single narrative thread, so on an incident with multiple independent contributing factors it can stop at the first plausible-sounding chain and miss a second, unrelated gap that also mattered. Combining it with a causal-graph or fault-tree check on the resulting hypothesis (does this cause actually explain the full timeline, or just part of it) helps catch that failure mode.
• Fishbone (Ishikawa). Strength: structured brainstorming across categories (commonly people, process, technology, environment) surfaces candidates you might not think of starting from a single chain. Weakness: it's a divergent tool, good for generating hypotheses, but it doesn't by itself tell you which candidate cause is actually correct; you still need evidence to narrow down.
• Fault-tree analysis. Strength: models AND/OR combinations of conditions, so it's the right tool when the incident required several things to go wrong simultaneously (a database failover only failed because BOTH the standby was on an incompatible version AND the health check didn't catch the mismatch). Weakness: more effort and formalism than most incidents justify; overkill for a simple single-cause bug.
• Causal-chain or timeline analysis. Strength: best when the incident unfolded across many events over hours or days, and the real analytical work is establishing what happened when and in what order, which then makes the cause fairly evident once assembled. Weakness: doesn't add much analytical structure beyond reconstruction; you often still need Five Whys or fishbone on top of the assembled timeline to go from 'here's what happened' to 'here's why.'

Worked example
A multi-hour cascading outage across several services: causal-chain or timeline analysis is the right first tool, since the priority is establishing the sequence across services before anything else makes sense. A single service crashing on a specific malformed input: Five Whys is fast and sufficient. A database failover that should have worked but didn't: fault-tree analysis, since it likely required more than one condition (incompatible standby version AND a health check that didn't catch it) to align. A vague, hard-to-pin-down data-quality issue with no obvious single trigger: fishbone, to broadly brainstorm across categories (was it the data source, the pipeline code, a schema change, an environment difference) before narrowing with evidence.

Trade-offs and pitfalls
The most common mistake is defaulting to Five Whys for everything because it's the most familiar technique, even on incidents with multiple independent contributing factors where it will produce a tidy but incomplete story. Pick the technique to fit the shape of the incident, not out of habit, and don't hesitate to combine two (fishbone to generate candidates, then Five Whys or fault-tree to narrow and validate).

Direct answer

A useful set of test-reliability metrics includes a flakiness score (how often a test's result changes without a real code change), mean time to detect (MTTD) a genuinely failing test, mean time to repair (MTTR) once detected, and the pass-rate trend over time; each needs a precise, computable definition, not just a name, or it can't reliably feed a dashboard or gate a build.

Structured elaboration

Definitions:

• Flakiness score: a common, simple definition is the flip rate, the fraction of consecutive same-commit reruns of a test where its result changed (pass to fail or fail to pass) without any code change in between:
  $\text{flip rate} = \frac{\text{number of result flips observed}}{\text{total reruns observed}}$
  A test with a flip rate near 0 is stable; a test flipping on a meaningful fraction of reruns is flaky enough to warrant quarantine review.

• Mean time to detect (MTTD): the average time between when a test would first genuinely fail due to a real regression and when that failure is actually surfaced and actioned (not merely re-run and ignored):
  $\text{MTTD} = \frac{1}{n}\sum_{i=1}^{n} \left(t_{\text{detected},i} - t_{\text{introduced},i}\right)$
  This depends on being able to identify $t_{\text{introduced}}$ retrospectively (often via bisection once a regression is found), so it's typically computed after the fact from a sample of known regressions rather than in real time.

• Mean time to repair (MTTR) for test failures: the average time from a test failure being flagged to the underlying test (or the code it covers) being fixed:
  $\text{MTTR} = \frac{1}{n}\sum_{i=1}^{n} \left(t_{\text{fixed},i} - t_{\text{flagged},i}\right)$

• Pass-rate trend: the rolling pass rate over a moving window (e.g. trailing 7 days), tracked over time to spot a slow degradation before it becomes a crisis, rather than looking only at a single day's snapshot.

Presentation and alerting: flakiness score feeds a per-test quarantine threshold (above a certain flip rate, flag for quarantine review); MTTD and MTTR feed team-level or suite-level health dashboards, with an alert if either trends upward meaningfully over a rolling window, since a rising MTTD in particular means regressions are sitting undetected longer, a leading indicator of risk rather than a lagging one.

Worked example

A test with 20 observed reruns across recent commits, 3 of which showed a result flip with no underlying code change, has a flip rate of 3/20 = 0.15, likely above a reasonable quarantine threshold (commonly set somewhere in the 0.1-0.2 range depending on the team's risk tolerance) and worth flagging for investigation.

Trade-offs & pitfalls

MTTD specifically is hard to measure precisely in real time (you often only know $t_{\text{introduced}}$ in retrospect, once you've found and bisected a regression), so it's usually a periodically-computed, retrospective metric rather than a live dashboard number; presenting it as if it were live and precise overstates the confidence you actually have in it.

Direct answer

Capture the browser's console log and network activity during the test run using Selenium 4's Chrome DevTools Protocol (CDP) integration, or the simpler driver.get_log('browser') API for console-only capture, and attach both to the CI failure report so a teammate can see the client-side JavaScript error or failed network call without ever needing to reproduce the flaky run locally.

Structured elaboration

Two complementary capture mechanisms exist, at different levels of depth. driver.get_log('browser') is the lighter-weight option: it returns the browser's own console log entries (JavaScript errors, warnings, console.log output) as a simple list, with no setup beyond enabling browser logging in the driver's capabilities. Selenium 4's native CDP integration (driver.execute_cdp_cmd(...), or the higher-level driver.get_log/Network domain listeners in newer bindings) goes further, giving access to the full Chrome DevTools Network domain, request/response headers, timing, and failed requests, which is what you need when the suspected cause is a failed or slow NETWORK call rather than a pure JavaScript error.

The most useful log types for this scenario specifically: console logs (to catch a JavaScript error that prevented an event listener from attaching, matching this question's premise), and network/CDP Network.responseReceived/Network.loadingFailed events (to catch a background request that failed or never completed, which can silently break functionality that depended on its result without ever showing up as a visible error). Attaching both to the CI report, alongside the existing screenshot-on-failure artifact, means a teammate reading the failure the next morning has the same diagnostic picture you would have had watching it fail live.

Worked example

def capture_browser_diagnostics(driver):
    console_logs = driver.get_log('browser')
    driver.execute_cdp_cmd('Network.enable', {})
    # In a real run, CDP network events are captured via a listener registered before
    # navigation; a fuller CDP-based capture pipeline records request/response pairs as
    # they occur rather than fetching them after the fact.
    return {
        "console": console_logs,
        # network events would be appended here by the listener during the test
    }

Attaching this to the CI report (as a JSON artifact alongside the existing screenshot) means a JavaScript error or a failed background request is visible without needing to reproduce the failure interactively.

Trade-offs and pitfalls

The most common mistake is capturing ONLY the console log and assuming that covers "client-side issues": a failed background network call that the application handles silently (retries without logging, or simply drops the result) produces NO console error at all, so relying on console logs alone would miss exactly the class of bug this question's premise describes. A second pitfall is enabling verbose CDP network capture on every single test run regardless of pass/fail, which adds real overhead at scale; a common middle ground is capturing lightweight console logs always, and only spinning up the heavier CDP network capture path when a test actually fails, similar to how screenshot capture is usually failure-triggered rather than always-on.