How Interviewers Use Framing Effects to Test Your Reasoning

SECTION 1 - The Psychology of Framing: Why Your First Interpretation Controls Your Reasoning

Every ML, data science, or software engineering interview question comes with two layers:

Layer 1: The Literal Question

The words spoken out loud - e.g.,
“Design a model to predict delivery time.”

Layer 2: The Cognitive Frame

The implicit problem your mind thinks you’re solving.

This second layer is where most candidates lose the interview before they even begin to speak.

Your brain doesn’t interpret questions neutrally.
It uses shortcuts, heuristics, to create a quick mental model of what the problem "should" be. These shortcuts are efficient but dangerous in interviews, because they compress complexity into guesswork.

A delivery-time prediction question sounds like:
“A regression problem.”

A search-ranking task sounds like:
“A standard ranking model.”

A fraud detection scenario sounds like:
“Anomaly detection.”

These interpretations are not wrong.
But they are incomplete, and often misleading.

The framing effect influences how your brain:

• interprets goals
• selects constraints
• assumes data properties
• predicts interviewer expectations
• defaults to familiar patterns

By the time you begin answering, the frame has already shaped your direction.

Interviewers know this, and many intentionally use framing to observe whether you can resist the cognitive autopilot.

Why Your First Interpretation Shapes Your Entire Answer

Humans think in compressed representations.
This is what makes us efficient, and also vulnerable.

The moment your brain decides:

“This is a classification problem,”
or
“This is an LLM question,”
or
“This is a metrics discussion,”

…it filters the entire conversation through those assumptions.

Weak candidates never challenge their initial framing.
Strong candidates interrogate it.

They ask:

“Before I jump in, let me reframe the problem.”
“Is this really classification, or is this a ranking objective?”
“Is the main challenge modeling, or data quality?”
“What’s the actual goal from the business perspective?”

Interviewers love this because it demonstrates metacognition, the ability to recognize and adjust your own thinking.

This is the same skill highlighted in:
➡️The Hidden Metrics: How Interviewers Evaluate ML Thinking, Not Just Code

Framing-awareness is one of the top traits interviewers look for in senior engineers.

The Trap of “Implicit Assumptions”

Most framing mistakes come from unspoken assumptions:

• Thinking data is clean when it’s not
• Assuming labels exist
• Assuming latency doesn’t matter
• Assuming interpretability isn’t required
• Assuming the system scale is trivial
• Assuming the goal is accuracy

Interviewers never list all constraints up front.
They want to see which ones you automatically assume, and which ones you question.

A senior ML engineer knows there is no such thing as a “simple model.”
There is only a simple framing of a complex system.

The candidate who treats the initial framing as truth exposes themselves.
The candidate who questions the frame reveals depth.

The Frame Can Be More Important Than the Answer

Consider this:

Two candidates propose identical models.
One framed the problem poorly.
The other reframed it elegantly.

The second gets the offer.

Why?

Because the interviewer isn’t measuring the model, they’re measuring the quality of the mind that generated it.

Framing tells the interviewer:

• how you think
• whether you reason structurally
• whether you can handle ambiguity
• whether you default to templates
• whether you understand real-world ML complexity
• whether you operate like someone who can lead
• whether you can build systems, not just answers

Framing is the X-ray into your cognition.

Interviewers don’t use framing tricks to deceive candidates.
They use framing to reveal candidates.

SECTION 2 - Why Framing Effects Are the Interviewer’s Most Powerful Cognitive Tool

Most candidates assume interview difficulty comes from the content of the question, the math, the model choice, the metrics, the system design layers. But interviewers know something far more subtle: the hardest part of an ML interview isn’t the question. It’s the frame around the question. The way a problem is presented determines how a candidate thinks, what they prioritize, what they ignore, and how deeply they reason.

This is why skilled interviewers across FAANG, OpenAI, Anthropic, and top AI-first startups rely on framing effects, subtle shifts in how a problem is introduced, ordered, emphasized, or contextualized, to reveal a candidate’s cognitive maturity. They’re not just asking you to solve a problem. They’re watching how you interpret the problem.

Framing isn’t decoration.
Framing is the test.

It exposes whether you think linearly or structurally, whether you chase assumptions instead of clarifying them, whether you jump into models prematurely, whether you understand the business objective before the technical one, and whether your default mode is to react or investigate.

Strong candidates are not the ones who know the most.
Strong candidates are the ones who reframe.

This section explores why interviewers intentionally manipulate frames, how those frames reveal your reasoning patterns, and why some candidates collapse while others shine.

Framing Effect #1: Interviewers Use Vagueness to Expose Your Cognitive Anchors

Interviewers sometimes describe a problem in vague language:

“We want to predict engagement.”
“We need to improve ranking.”
“We’re trying to detect anomalies.”
“We need a model for customer health.”

Weak candidates immediately grab onto the first mental anchor:

“Engagement → CTR prediction → let’s do a binary classifier.”
“Ranking → maybe pairwise ranking loss?”
“Anomalies → isolation forest?”

The problem is that these assumptions are often wrong, or at least incomplete.

The vagueness is intentional.

Interviewers want to see if you:

• clarify definitions
• explore interpretations
• resist premature solutioning
• ask smart questions
• avoid anchoring bias

They are testing your ability to create clarity, not just operate with it.

Strong candidates respond with:

“Before diving in, what specifically do we mean by engagement here, scrolling, likes, comments, or time spent?”
“What is the operational objective driving this ranking?”

This alone separates them from 80% of the field.

Because ML interviews are not model contests, they’re clarity contests.
The interviewer is watching whether you build clarity or assume it.

This is the same behavior that demonstrates senior-level thinking, as explored in:
➡️Beyond the Model: How to Talk About Business Impact in ML Interviews

Framing Effect #2: Interviewers Change Constraints Mid-Question to Test Adaptability

This is one of the most powerful framing effects.

You begin solving the problem. You frame it, you outline data needs, you propose a baseline model. And then the interviewer says something like:

“Oh, I should mention the system must operate under 50ms latency.”
“Actually, labels aren’t fully trustworthy.”
“Let’s assume the dataset is much smaller than expected.”
“We should optimize for recall, not precision.”
“The model must be explainable to regulators.”

These aren’t curveballs.
They’re frame shifts.

They test:

• whether you panic
• whether your reasoning breaks
• whether you cling to your original idea
• whether you’re attached to “being right”
• whether you can gracefully adjust
• whether you treat modeling as a living process, not a fixed template

Weak candidates freeze or defend their original solution:

“Oh… okay… but I guess we can still use XGBoost?”
“Even with latency constraints, I think the transformer might still work?”

This signals rigidity.

Strong candidates pivot elegantly:

“With a 50ms latency budget, deep models might be too expensive. Let me rethink the architecture with a simpler or precomputed representation.”

This signals adaptability, which is one of the strongest indicators of real-world ML maturity.

Framing Effect #3: Interviewers Introduce Irrelevant Details to See If You Chase Noise

Interviewers sometimes pepper scenarios with distracting details:

“The data comes from 40 cities… but that may not matter much.”
“We store it in a Snowflake warehouse for now.”
“The team has been experimenting with GANs, though that’s not directly related.”

These details test:

• your sense of relevance
• your ability to filter noise
• your awareness of what actually impacts modeling
• your resistance to over-indexing on keywords

Weak candidates latch onto the noise:

“Oh! GANs? Should we generate synthetic samples?”
“Snowflake? Does that affect training pipelines?”

Strong candidates ignore noise effortlessly:

“I’ll focus on the modeling objective first. Storage format or prior team experiments aren’t critical yet.”

Interviewers use irrelevant framing elements to test whether your mind is driven by focus or FOMO.

Framing Effect #4: Interviewers Withhold the Business Objective to See If You Notice

Many ML candidates jump straight into the modeling layer:

“We can use a random forest!”
“I think we should treat this as regression.”
“I’d probably start with XGBoost.”

Interviewers intentionally don’t tell you the goal, because they want to see if you ask.

Weak candidates assume.
Strong candidates seek meaning.

“What business outcome are we optimizing for?”
“What does success look like here?”
“Is this tied to revenue, retention, cost, or user experience?”

This is one of the most important questions in ML, and yet one of the least asked.

Why do interviewers use this framing?
Because ML engineering is not about building models.
It is about solving business problems using models.

Candidates who skip the business layer reveal themselves instantly.
Candidates who insist on clarifying it demonstrate system-level thinking.

Framing Effect #5: Interviewers Present Tradeoffs in a Slanted Way to Observe Your Reasoning Independence

Sometimes an interviewer frames the problem in a biased direction:

“We care a lot about accuracy here.”
“Latency is probably the main issue.”
“We think precision is the more important metric.”
“We’ve been leaning toward deep learning for this.”

Interviewers aren’t telling you what to do.
They’re testing whether you:

• blindly agree
• push back with reasoning
• analyze the tradeoff instead of accepting it
• demonstrate independent judgment
• think critically under persuasion

Weak candidates accept the framing without question:

“Yes, accuracy is key.”
“Right, so we should reduce latency.”

Strong candidates pause and investigate:

“Is accuracy the primary KPI, or is there a specific business impact tied to it?”
“Before fully optimizing for latency, what’s the tolerance for degradation in quality?”

Interviewers often intentionally misframe a requirement to see if you think critically or compliantly.

They’re not looking for obedience.
They’re looking for reasoning.

SECTION 3 - The Hidden Filters: How Framing Exposes Your Cognitive Defaults

If you think framing effects are just subtle question-rewordings, you’re underestimating how deeply they shape interview performance. Skilled ML interviewers don’t use framing to trick you, they use it to reveal who you are as a thinker. Framing acts like a prism: the same question shines different colors depending on how you interpret it. And THAT interpretation is what interviewers read to understand your cognitive defaults.

Every candidate walks into an interview with an unconscious “reasoning template”:
a habitual way of understanding problems, making assumptions, structuring information, and deciding what matters. Most candidates don’t even know they have one.
Interviewers do.

This section breaks down how framing exposes four core cognitive defaults interviewers care deeply about, defaults that determine whether a candidate is seen as junior, mid-level, or genuinely senior.

1. Framing Reveals Whether You Think in Problems or Patterns

The very first moment you’re presented with a question, your brain makes a near-instant decision:

“Do I recognize this?”
or
“What is this really asking?”

One mindset is pattern-driven:
You try to match the question to a familiar template.

“This is a ranking problem.”
“This is classification.”
“This is just a regression with noise.”

The other mindset is reasoning-driven:
You look inside the problem before labeling it.

“What is the underlying objective?”
“What constraints shape the solution?”
“What do I know, and what must I infer?”

Interviewers pay close attention to which direction your mind jumps first.

Because pattern-driven candidates may sound quick, but they collapse when the pattern breaks, when the interviewer reframes the problem in a way that doesn’t neatly fit prior templates.

For example:

Frame A:
“Predict if a user will churn next month.”

Frame B:
“Identify accounts likely to become disengaged, but assume labels are unreliable.”

Most template thinkers fall apart when Frame B arrives, suddenly the neat classification paradigm doesn’t hold. Reasoning-driven thinkers remain stable. They slow down, clarify assumptions, and rebuild the problem from first principles.

This distinction is exactly what separates “memorized performance” from “true reasoning,” a difference explored deeply in:
➡️The Hidden Metrics: How Interviewers Evaluate ML Thinking, Not Just Code

Framing reveals which type you are long before you realize it.

2. Framing Exposes How You Handle Ambiguity and Missing Information

Every ML interview includes deliberate ambiguity. Interviewers want to see whether you fight ambiguity, fear ambiguity, or use ambiguity.

The moment a problem is framed with missing information, no metric, unclear constraints, incomplete data, your reasoning defaults activate.

Weak candidates rush forward anyway:

“Well, I’ll assume…”
“I guess we can say that…”
“I think the dataset probably contains…”

They resolve ambiguity prematurely instead of exploring it.
This is a sign of brittle reasoning.

Strong candidates pause and examine the ambiguity itself:

“What are the possible interpretations here?”
“What information would change the decision?”
“What constraints are unknown, and what does that mean for the solution?”

This kind of response signals seniority because it mirrors real-world ML work.
Nothing in production ML arrives fully specified. Everything is an uncertainty landscape.

Interviewers deliberately frame problems with incomplete information to see whether you:

• panic
• ignore the ambiguity
• patch it with assumptions
• or structure it strategically

A candidate with strong framing sensitivity turns ambiguity into a roadmap.

A candidate without framing awareness turns it into confusion.

3. Framing Reveals Your Model of Causality vs. Correlation

One of the most significant cognitive defaults interviewers test, often without mentioning it, is whether you naturally think in correlation or causation.

They might ask:

“Given this dataset, how would you determine if a promotion actually increases engagement?”

The question can be framed as an ML modeling challenge or as a causal inference problem. How you interpret the frame tells the interviewer everything.

Pattern-driven candidates say:

“We can train a model to predict engagement based on whether the user received the promotion.”

This reveals a correlation-first mindset.
It’s not “wrong,” but it’s shallow.

Reasoning-driven candidates look through the frame and ask:

“Are we trying to predict engagement or establish causal impact? The modeling strategy changes entirely depending on the objective.”

This signals nuanced thinking, the ability to differentiate between prediction and understanding, between ML and causal inference, between surface form and underlying goals.

Interviewers love this because causal framing awareness is one of the strongest indicators of senior-level cognitive maturity.

4. Framing Reveals How You Prioritize and Whether You Prioritize the Right Things

Consider how many ML interview questions involve conflicting requirements:

• accuracy vs latency
• recall vs precision
• model complexity vs explainability
• data quantity vs label quality
• performance vs cost

Interviewers test priority reasoning through framing.

They might frame a question around:

• business impact
• product constraints
• risk tolerance
• operational limits
• ethical considerations
• user experience

The framing invites you to prioritize some dimensions over others.
Interviewers aren’t looking for the “right” priority, they’re assessing whether your priorities are coherent and aligned with the frame.

For example:

If a question is framed in terms of high-stakes medical predictions, and the candidate prioritizes computational cost over false negatives, that’s a red flag.

If a question is framed as a real-time system, and the candidate begins discussing complex models without mentioning latency, that signals a blind spot.

Framing directs attention.
Your response reveals what your mind considers important.

Interviewers interpret this as a window into your engineering judgment, your sense of responsibility, and your ability to work with cross-functional teams.

5. Framing Exposes Biases You Don’t Know You Have

You might think you’re evaluating the problem objectively.
You aren’t.
No one is.

Your reasoning is influenced by:

• your personal ML background
• your favorite models
• your past project domains
• your fears
• your strengths
• your heuristics
• your implicit assumptions
• your beliefs about “correctness”

Interviewers use framing variations to surface these biases.

For example:

If they shift the question from tabular to sequential data, does your mind still cling to tree-based models?

If they frame the system as “mission-critical,” do you still reach for black-box deep learning?

If they introduce strict fairness constraints, do you change your evaluation strategy or ignore the ethical frame?

Most candidates answer from habit.
Strong candidates answer from reasoning.

This difference is what interviewers are hunting for.

SECTION 4 - How Interviewers Shift Frames to Reveal Your Blind Spots

If framing effects reveal your cognitive tendencies, then frame shifting reveals your cognitive blind spots. This is the part of the interview where even strong candidates begin to unravel—not because they lack knowledge, but because they don’t realize the rules of the mental game have been altered. Interviewers use frame shifts intentionally, strategically, and repeatedly. It’s the stress test inside the stress test: a direct glimpse into how you handle uncertainty, contradiction, and cognitive turbulence.

A frame shift is when the interviewer subtly or dramatically changes the context, constraints, perspective, or goal of the problem. Nothing has changed about the candidate’s intelligence. What changes is the lens through which the problem now exists—and how the candidate reacts to that new lens becomes the real evaluation.

Most candidates miss this entirely. They think they’re being “corrected” or “challenged.” They assume the interviewer is pushing back, disagreeing, or wanting a different answer. In reality, the interviewer is testing something far deeper:
Can you reorient when the foundation of your reasoning moves beneath your feet?

Frame shifting is the closest ML interviews get to simulating real-world ambiguity. Because in real projects, the frame shifts constantly:

• business priorities change mid-quarter
• a stakeholder redefines the metric
• data availability collapses after privacy changes
• modeling requirements shift from accuracy to interpretability
• latency budgets tighten when the system scales
• a team discovers that labels are inconsistent
• a model must now satisfy regulatory constraints

In industry, these shifts don’t wait for you to be ready. So interviewers test your ability to handle them, even in the room.

Let’s break down how frame shifts appear, how they expose blind spots, and how top ML candidates turn these moments into opportunities rather than traps.

1. The Constraint Flip - When the Problem Suddenly Favors the Opposite Tradeoff

Many candidates begin a question in the right direction, articulating constraints clearly. But then the interviewer introduces a new requirement:

You propose a complex model?
The interviewer asks for strict latency.

You propose a simple interpretable model?
The interviewer says accuracy is paramount.

You propose heavy feature engineering?
The interviewer reveals that feature freshness is expensive.

This isn’t contradiction.
This is evaluation.

The interviewer wants to see:

• Do you cling defensively to your original choice?
• Do you panic and switch models without justification?
• Do you blame the ambiguity?
• Or do you reframe the problem instantly?

Weak candidates freeze or justify their initial answer harder.
Strong candidates pivot smoothly:

“Given the new constraint, the previous approach becomes suboptimal. Let me re-evaluate the tradeoffs…”

This tells the interviewer everything they need to know:
You reason, not guess.

2. The Metric Shift - Testing Whether You Understand the Problem or Just Recognized a Pattern

Metric shifts expose shallow reasoning more efficiently than any other technique. You choose accuracy as your metric, and the interviewer says:

“Actually, accuracy doesn’t matter. Precision does.”
Or,
“The real business cost is in false negatives.”
Or,
“The metric must be stable across demographic slices.”

This forces you to reveal whether you:

• understood the core objective
• tied your reasoning to the business
• interpreted the real-world context
• or just matched the problem to a familiar ML pattern

Metric shifts are devastating to pattern-matchers because they reveal that the candidate never understood why they chose a particular model or approach.

Strong candidates show cognitive elasticity:

“If precision is the priority, that shifts our modeling objective. We may need threshold tuning, cost-sensitive learning, or alternative loss functions…”

They don’t lose their footing.
They simply change the frame.

3. The Data Reality Check - Turning Idealized Thinking into Production Thinking

Interviewers often introduce a subtle but brutal frame shift:
removing, reducing, or corrupting the data you assumed existed.

Candidate: “We’ll train a supervised model using historical labels.”
Interviewer: “What if half the labels are unreliable?”

Candidate: “Let’s use embeddings from user history.”
Interviewer: “What if the history is sparse?”

Candidate: “We’ll use an LLM to classify.”
Interviewer: “What if the latency budget is 20 ms?”

These shifts expose the biggest interview blind spot:
over-idealization.

Weak candidates think ML interviews are algorithm questions.
Strong candidates know ML interviews are reality questions.

When data changes, the entire problem shifts and interviewers use this deliberately to test your mental adaptability.

4. The Perspective Shift - Moving You From Engineer to Product Thinker

Another powerful framing technique is switching your perspective mid-answer:

“What if you were the PM - how would you justify the model’s cost?”
“What if you were the SRE - how would you monitor this system?”
“What if you were the user - what failure modes would matter?”

These perspective shifts test:

• empathy
• system-level awareness
• multi-stakeholder reasoning
• real-world tradeoff thinking
• ability to generalize beyond your technical bubble

Weak candidates respond with surface-level hand-waving.
Strong candidates switch roles with ease, because they’ve internalized a broader view of ML systems.

Perspective shifts reveal whether you think like an IC…
…or like a senior engineer.

5. The Red Herring - Testing Whether You Anchor to Irrelevant Details

Interviewers sometimes intentionally introduce irrelevant information:

“We have 500M rows of data, but the label quality is perfect.”
“We use Spark, but training time isn’t the bottleneck.”
“We store everything in S3, but latency isn’t the issue.”

Most shallow thinkers anchor heavily to the irrelevant detail:

“Oh, 500M rows? We need distributed training.”

Strong candidates ignore noise and seek the signal.
They identify the framing trap and refocus on what matters:

“Even with large data volume, if the bottleneck is not training or latency, the primary constraint must lie elsewhere, let’s clarify evaluation or cost.”

Frame filters = senior-level cognition.

Conclusion - Framing Isn’t a Trick. It’s the X-Ray Machine Interviewers Use to See How You Think.

If you look closely at how ML and software engineering interviews are structured today, from FAANG to high-growth AI startups, you’ll notice a subtle but powerful thread running through every question, follow-up, constraint change, and scenario: framing.

Framing is not a psychological trick. It’s the single most reliable way interviewers can reveal your inner cognitive architecture.

Because when an interviewer changes the frame, the goal, the metric, the constraint, the data shape, the operational requirement, something happens inside your mind. Either your reasoning adapts…or collapses.

Weak candidates cling to their first interpretation.
Strong candidates reshape their understanding.
Exceptional candidates reframe the problem themselves, before the interviewer even asks.

This is why framing effects are so deeply embedded in the interview playbook:

• They expose whether you rely on memorized templates
• They surface whether you can deconstruct ambiguity
• They show whether your assumptions are stable or shaky
• They reveal whether you think in systems or steps
• They highlight whether you can shift perspective without losing coherence
• They demonstrate whether your reasoning holds when the environment changes

A single frame shift exposes your cognitive flexibility.
Multiple frame shifts expose your cognitive maturity.

The best ML candidates aren’t just technically fluent, they’re frame-aware. They know that the question you hear first is rarely the real question. They expect reframing. They welcome it. They use it to show the interviewer their ability to think like a designer, not just an implementer.

Framing is not the enemy.
Framing is your canvas.

Because what interviewers really want to know is simple:

Can you think clearly when the ground moves beneath you?

If you can, you signal senior-level reasoning.
If you can’t, you signal fragility, even if your ML knowledge is strong.

This is why mastering framing effects does more than improve interview performance. It improves your engineering instincts. It sharpens your communication. It deepens your ability to collaborate with PMs, researchers, and other engineers. It builds the mental elasticity required in real ML systems, which are messy, noisy, ambiguous, and ever-changing.

Interviews don’t test what you know.
They test how you re-interpret what you know.

And framing, the shift, the twist, the redefinition, is the instrument interviewers use to see the true shape of your thinking.

Master framing, and interviewers don't just remember you.
They trust you.

FAQs - Framing Effects in ML & Tech Interviews

1. What exactly is a framing effect in interviews?

A framing effect is when the presentation of a problem changes your interpretation of it. Interviewers use this to see whether you can adjust your reasoning when the context or constraints shift. It reveals flexibility, not knowledge.

2. Why do interviewers deliberately frame questions vaguely?

Because real engineering work rarely comes with complete information. A vague question forces you to clarify assumptions, structure ambiguity, and shape the problem before solving it. This is a senior-level skill.

3. How can I tell when an interviewer is intentionally reframing the problem?

Look for:

• “But what if…”
• “Let’s assume the data isn’t clean…”
• “Now imagine latency becomes critical…”
• “Actually, the labels are imperfect…”
  These aren't curveballs, they're cognitive probes.

4. Why do my answers collapse when the interviewer shifts the frame?

Because you were relying on a pattern match, not a first-principles understanding. When the pattern breaks, your reasoning breaks. This is extremely common among mid-level candidates.

5. How can I practice reframing effectively?

Take any ML problem and force yourself to reinterpret it: change the metric, remove labels, add constraints, shift latency, or redefine the objective. Practicing reframing builds adaptive reasoning.

6. Are framing effects used more in ML interviews than in other disciplines?

Yes, because ML problems have deeply interconnected variables, data, modeling, constraints, metrics, drift, operations, making framing a powerful diagnostic tool for how candidates think.

7. What does “self-reframing” mean in an interview?

It means you proactively reshape the problem before solving it:
“Let me interpret the objective this way…”
or
“There are two ways to frame this problem, user-centric or system-centric…”
Interviewers love this.

8. Why do interviewers care so much about how I handle shifting constraints?

Because real-world ML systems always shift. Requirements change, metrics change, data changes. If your reasoning breaks during the interview, it will break on the job.

9. What’s the biggest mistake candidates make with framing effects?

Locking onto the initial interpretation and refusing to adjust. This signals rigidity, the opposite of what ML teams need.

10. How do strong candidates respond to a reframed question?

They slow down, verbalize the shift, and reorganize the structure:
“Given this new constraint, the tradeoff space changes, here’s how.”
This shows meta-cognition and cognitive stability.

11. How do I avoid sounding unsure when a frame changes?

Normalize the shift:
“That changes the modeling approach. Let me reason through the implication.”
This sounds confident, not uncertain.

12. What does a framing-sensitive answer look like?

It acknowledges the environment:
“If fairness becomes a priority, accuracy is no longer the primary metric. We need to rebalance the objective.”
This signals maturity.

13. Can framing effects ever be used unfairly by interviewers?

Only when done excessively without purpose. But well-trained interviewers use framing to explore reasoning, not to trick you. FAANG interviewers are specifically trained in this technique.

14. How can I train myself to avoid being rattled by reframing?

Practice thought elasticity:

• re-expressing the problem
• redefining constraints
• revisiting assumptions
• exploring alternate frames
  This builds adaptability.

15. What’s the single strongest framing skill I can demonstrate in interviews?

Reframing out loud.
If you can articulate how the frame shifts your reasoning, interviewers instantly see that your thinking is structured, flexible, and senior-caliber.