Introduction
If you’ve interviewed recently, you may have felt it instinctively:
Technical interviews in 2026 don’t feel harder, but they feel different.
Candidates still prepare for:
- Coding questions
- System design
- ML fundamentals
- Behavioral rounds
Yet many leave interviews confused:
- “I answered correctly, but didn’t pass.”
- “They pushed back more than I expected.”
- “The questions felt open-ended and subjective.”
- “It didn’t feel like they were testing knowledge.”
That feeling is accurate.
In 2026, companies are no longer primarily testing what you know.
They are testing how you think when knowledge alone isn’t enough.
Why Interview Trends Changed So Sharply
Three forces reshaped technical interviews over the last few years:
- AI dramatically lowered the cost of baseline screening
- Production systems became more complex and risky
- Hiring mistakes became more expensive than hiring delays
Together, these forces shifted interviews away from recall and toward judgment, reasoning, and decision-making under uncertainty.
The End of “Knowledge as Differentiation”
By 2026, companies assume:
- You can Google syntax
- You can reference docs
- You can use AI copilots
- You can learn tools on the job
As a result, knowledge itself stopped being scarce.
What is scarce:
- Clear reasoning
- Stable judgment
- Tradeoff awareness
- Communication under pressure
Technical interviews evolved to surface those qualities.
Why Interviews Feel More Ambiguous (On Purpose)
Candidates often complain:
“The question wasn’t well defined.”
That ambiguity is intentional.
Modern technical roles require:
- Working with incomplete requirements
- Making decisions before all data is available
- Adjusting when assumptions break
Interviews now simulate that reality.
If a question feels vague, interviewers are often testing:
- Whether you ask clarifying questions
- How you frame the problem
- Which constraints you prioritize
- How you proceed without certainty
Candidates waiting for “the right answer” often stall.
Candidates who reason transparently move forward.
AI Didn’t Replace Interviewers-It Changed Them
AI is now deeply embedded in hiring:
- Resume screening
- Online assessments
- Coding evaluations
- Pattern analysis across rounds
This didn’t remove humans from interviews.
It changed what humans focus on.
Because AI handles scale and consistency, human interviewers now spend their limited time probing:
- Weak reasoning
- Inconsistent assumptions
- Overconfidence
- Poor communication
- Fragile mental models
This makes interviews feel more intense, but also more revealing.
Why Old Prep Advice Is Failing Candidates
Many candidates still prepare using outdated advice:
- Memorize patterns
- Optimize for speed
- Cover as many questions as possible
- Focus on “expected answers”
This works poorly in 2026.
Why?
Because companies are explicitly trying to detect:
- Rehearsed answers
- Overfitting to templates
- Shallow understanding masked by fluency
Preparation that optimizes for coverage often backfires.
What Companies Are Actually Optimizing For Now
Across big tech, startups, and AI-native companies, interviews increasingly optimize for:
- Reasoning clarity over cleverness
- Tradeoff articulation over optimality
- Consistency over brilliance
- Adaptability over confidence
- Decision ownership over correctness
These signals correlate far more strongly with on-the-job success than raw technical knowledge.
Why This Matters for Candidates
Candidates who don’t update their mental model of interviews:
- Feel blindsided
- Misinterpret feedback
- Overcorrect in the wrong direction
- Burn out preparing harder instead of smarter
Candidates who do update their model:
- Prepare more efficiently
- Perform more consistently
- Understand why they pass or fail
- Improve faster between interviews
Understanding interview trends is now a competitive advantage.
A Simple Mental Shift That Helps Immediately
Instead of asking:
“How do I get the right answer?”
Start asking:
“What signal is this question designed to extract?”
That shift alone dramatically improves interview performance.
Section 1: From Coding Puzzles to Reasoning Under Constraints
In 2026, coding interviews look deceptively familiar.
Candidates still see:
- Arrays, strings, trees, graphs
- Data manipulation
- Basic algorithms
- Time and space complexity discussions
And yet, many leave these rounds feeling confused:
“The problem was easy… so why didn’t I pass?”
The answer lies in a fundamental shift: companies are no longer using coding questions to test algorithmic cleverness. They are using them to test reasoning under constraints.
The puzzle stayed.
The signal changed.
Why Coding Puzzles Didn’t Disappear (But Changed Purpose)
Companies didn’t abandon coding questions because:
- They are fast to administer
- They are familiar to candidates
- They allow controlled comparison
What changed is why they are asked.
In earlier years, coding rounds primarily tested:
- Knowledge of algorithms
- Speed of implementation
- Pattern recognition
In 2026, those are assumed.
Coding problems now act as containers for deeper signals:
- How you reason when requirements are incomplete
- How you handle tradeoffs
- How you communicate while thinking
- How you respond to constraint changes
The code is the medium, not the message.
The Rise of Constraint-Driven Evaluation
Modern interviewers care less about what you code and more about:
- Why you chose this approach
- What you considered and rejected
- How your solution adapts under pressure
This is why interviewers increasingly:
- Add constraints mid-solution
- Ask “what if” questions
- Push on edge cases
- Change requirements late
They are not trying to trick you.
They are simulating reality.
What “Reasoning Under Constraints” Actually Means
Constraint-driven evaluation tests how you operate when:
- Time is limited
- Information is incomplete
- Goals conflict
- There is no perfect solution
For example, interviewers now care deeply about whether you:
- Ask clarifying questions before coding
- State assumptions explicitly
- Justify tradeoffs (simplicity vs performance)
- Adjust calmly when constraints change
A technically correct solution with poor reasoning often fails.
A slightly imperfect solution with strong reasoning often passes.
Why Optimal Solutions Matter Less Than Defensible Ones
In many 2026 coding interviews:
- Multiple solutions are acceptable
- “Optimal” depends on unstated priorities
- Tradeoffs are unavoidable
Interviewers increasingly ask:
- “Why not this other approach?”
- “What would break first?”
- “How would this behave at scale?”
- “What if memory was limited?”
Candidates who chase the theoretically optimal answer often:
- Overcomplicate
- Get defensive
- Lose clarity
Candidates who explain why their choice fits the constraints build trust.
This reflects a broader shift in technical evaluation that mirrors ML interviews, where judgment increasingly outweighs correctness, as discussed in Cracking the Machine Learning Coding Interview: Tips Beyond LeetCode for FAANG, OpenAI, and Tesla.
Why Speed Is No Longer the Primary Signal
Speed still matters, but not in isolation.
Interviewers now penalize:
- Fast coding with weak explanation
- Rushing into implementation
- Skipping assumptions to “save time”
They reward:
- Thoughtful setup
- Clear reasoning
- Incremental progress
- Willingness to pause and clarify
A slower candidate with stable reasoning often outperforms a faster but brittle one.
The New Failure Mode: “Looks Correct, Feels Risky”
A common interviewer reaction in 2026:
“The solution works, but I wouldn’t trust them in production.”
This usually happens when candidates:
- Don’t explain decisions
- Ignore edge cases
- Can’t adapt when challenged
- Treat the problem as a puzzle instead of a system
Coding interviews are now risk assessments, not trivia contests.
What Interviewers Are Watching While You Code
While you code, interviewers are tracking:
- How you structure the problem
- Whether you narrate your thinking
- How you handle uncertainty
- How you react to feedback
They are asking themselves:
- Would this person debug calmly at 2 a.m.?
- Would they ask for clarification instead of guessing?
- Would they own tradeoffs responsibly?
Syntax correctness is table stakes.
Why This Change Catches Candidates Off Guard
Candidates struggle because:
- Prep materials still emphasize patterns and speed
- Mock platforms reward correctness over reasoning
- Past experience trained them for different signals
So when interviewers push on reasoning, candidates feel:
- Interrupted
- Challenged
- Uncertain why follow-ups keep coming
Those follow-ups are the interview.
What Strong Candidates Do Differently
Candidates who succeed in 2026 coding rounds:
- Treat problems as design exercises
- Explain before implementing
- Adapt openly when constraints change
- Stay calm under pushback
They don’t aim to impress.
They aim to be predictable, clear, and trustworthy.
Section 1 Summary
In 2026, coding interviews:
- Still use puzzles
- But no longer test puzzle-solving skill
They test:
- Reasoning under constraints
- Tradeoff awareness
- Communication clarity
- Adaptability and judgment
Candidates who continue preparing for “coding puzzles” alone will keep failing.
Candidates who prepare for decision-making under pressure will pass consistently.
Section 2: System Design Interviews Are Now About Judgment, Not Architecture
System design interviews used to reward candidates who could draw impressive diagrams.
In 2026, that approach fails more often than it passes.
Candidates still arrive prepared to:
- Sketch multi-tier architectures
- Name every component
- Reference industry-standard systems
- Optimize for theoretical scale
And yet, many are rejected, even after producing technically sound designs.
Why?
Because system design interviews are no longer about how much architecture you know. They are about how you make decisions when architecture forces tradeoffs.
Why Architecture Knowledge Stopped Being the Differentiator
Companies didn’t stop caring about architecture.
They stopped using interviews to teach it.
By 2026, interviewers assume candidates can:
- Look up reference architectures
- Learn internal systems quickly
- Use cloud primitives competently
What they can’t assume is:
- How you prioritize under ambiguity
- How you manage risk
- How you balance competing constraints
- How you decide what not to build
As a result, system design interviews shifted from diagram correctness to judgment under uncertainty.
What “Judgment” Means in System Design Interviews
Judgment shows up in moments like:
- Choosing simplicity over scalability (or vice versa)
- Deciding where to invest complexity
- Accepting known limitations deliberately
- Designing for failure, not perfection
Interviewers now listen for:
- Why a component exists
- What problem it solves
- What tradeoff it introduces
- What risk it mitigates
A beautiful architecture without justification feels risky.
Why Over-Design Is Now a Red Flag
One of the most common failure modes in 2026 is over-design.
Candidates often:
- Introduce microservices prematurely
- Add queues, caches, and sharding “just in case”
- Optimize for extreme scale without justification
Interviewers interpret this as:
- Poor cost awareness
- Weak prioritization
- Lack of real-world experience
Strong candidates do the opposite:
- Start with the simplest viable design
- Explain when and why it would break
- Describe how they’d evolve it over time
This evolutionary thinking signals maturity.
The New Core Question: “Would I Trust This Person With My System?”
Interviewers are no longer asking:
“Is this design technically correct?”
They’re asking:
“Would I trust this person to make system decisions under pressure?”
That trust depends on whether you:
- Understand operational realities
- Anticipate failure modes
- Communicate risks clearly
- Avoid unnecessary complexity
This mirrors broader interview evaluation trends where thinking quality outweighs output polish, a pattern also explored in Mastering ML System Design: Key Concepts for Cracking Top Tech Interviews.
Why Constraints Matter More Than Components
Modern system design interviews intentionally under-specify requirements.
Interviewers want to see whether you:
- Ask clarifying questions
- Identify key constraints early
- Reframe the problem when priorities conflict
For example:
- Is latency more important than consistency?
- Is cost more important than peak throughput?
- Is correctness more important than availability?
Candidates who jump into architecture without clarifying constraints often build the wrong system, no matter how elegant it looks.
How Interviewers Use Pushback as a Signal
Interviewers push back to test:
- How attached you are to your design
- Whether you can revise assumptions
- How calmly you adapt
When an interviewer says:
“What if traffic spikes 10x?”
“What if data is eventually consistent?”
“What if this fails silently?”
They’re not saying your design is wrong.
They’re evaluating how you reason when your design is challenged.
Candidates who defend aggressively lose points.
Candidates who adapt thoughtfully gain trust.
Why Whiteboard Skill Matters Less Than Narrative Skill
In 2026, interviewers care less about:
- How neatly you draw boxes
- Whether you remember exact service names
They care more about:
- How you explain system behavior
- How you narrate tradeoffs
- How you reason about evolution
A clear verbal narrative often outweighs a detailed diagram.
This is why candidates who explain how the system behaves under stress often outperform those who only describe structure.
Common Signals That Lead to Rejection
Candidates often fail system design interviews when they:
- Optimize prematurely
- Avoid committing to decisions
- Use buzzwords instead of reasoning
- Can’t explain why a design choice exists
These failures are rarely about missing knowledge.
They are about weak judgment signaling.
What Strong Candidates Do Consistently
Strong candidates in 2026 system design interviews:
- Clarify goals and constraints first
- Start simple and justify complexity
- Explain tradeoffs explicitly
- Discuss failure and recovery calmly
- Treat design as a living system
They don’t aim to impress.
They aim to be safe to trust.
Why This Shift Is Permanent
As systems grow more complex and AI-assisted, the cost of poor judgment increases.
Companies can teach tools.
They can’t teach decision-making easily.
That’s why system design interviews now test judgment above all else, and why this trend isn’t going away.
Section 2 Summary
In 2026, system design interviews:
- Are no longer architecture trivia
- Are judgment evaluations in disguise
Interviewers prioritize:
- Constraint reasoning
- Tradeoff clarity
- Simplicity over spectacle
- Adaptability under pushback
Candidates who focus on why decisions are made, not just what is built, consistently pass.
Section 3: ML Interviews Focus on Decision-Making, Not Model Trivia
In 2026, ML interviews look familiar on the surface, and radically different underneath.
Candidates are still asked about:
- Models and algorithms
- Evaluation metrics
- Data pipelines
- Training and deployment considerations
But many technically strong candidates still fail. The reason is not missing knowledge. It’s that ML interviews no longer reward model trivia. They reward decision-making under real constraints.
Why Model Trivia Lost Its Power
A few years ago, being able to explain:
- How a model works
- When to use it
- Its theoretical properties
Was a strong differentiator.
In 2026, that knowledge is assumed. Interviewers know you can:
- Look up architectures
- Read papers
- Use libraries and tools
- Fine-tune models with assistance
What they don’t know is whether you can choose wisely.
So ML interviews evolved to answer a harder question:
“Would I trust this person to make ML decisions that affect users, revenue, and risk?”
What “Decision-Making” Means in ML Interviews
Decision-making shows up when interviewers ask:
- Why this model, not another?
- What tradeoffs did you accept?
- What happens when the data changes?
- How do you know when the model is wrong?
- What would you do if metrics improve but outcomes worsen?
These questions don’t have a single right answer.
They test whether you can:
- Frame the problem clearly
- Align modeling choices with business goals
- Anticipate failure modes
- Adjust decisions when assumptions break
Candidates who answer with definitions fail.
Candidates who reason through context pass.
From “Which Model?” to “Which Decision?”
A common trap is treating ML interviews as model-selection quizzes.
Interviewers aren’t asking:
“Do you know the best model?”
They’re asking:
“Do you know what decision this model supports, and whether it should be trusted?”
For example, when discussing evaluation:
- Listing metrics is not enough
- Explaining why a metric matters, and what it hides, is
This is why ML interviews now emphasize impact over accuracy, a theme explored in Beyond the Model: How to Talk About Business Impact in ML Interviews.
Why Tradeoffs Matter More Than Performance
In production ML, improving one dimension often degrades another:
- Accuracy vs latency
- Fairness vs precision
- Stability vs responsiveness
- Complexity vs maintainability
Interviewers probe these tensions deliberately.
Candidates who insist on “best performance” without acknowledging cost, risk, or user impact appear inexperienced, even if their models are strong.
Candidates who articulate tradeoffs calmly signal readiness.
The New Center of Gravity: Data and Evaluation Judgment
Model details are increasingly secondary to:
- Data quality
- Label reliability
- Distribution shift
- Evaluation strategy
Interviewers now ask:
- How would you detect data drift?
- What if labels are delayed or noisy?
- How do you validate without ground truth?
- What does a false positive actually cost here?
These questions test judgment, not memorization.
Candidates who understand how metrics connect to decisions, and how they can mislead, stand out. This aligns with how interviewers evaluate ML thinking beyond code, as discussed in The Hidden Metrics: How Interviewers Evaluate ML Thinking, Not Just Code.
Why “Correct” Answers Can Still Fail
Many ML interview questions are intentionally open-ended.
Candidates who hunt for the “correct” answer:
- Get stuck
- Hedge excessively
- Over-optimize explanations
Candidates who choose a reasonable path and explain it:
- Build trust
- Demonstrate ownership
- Show adaptability
Interviewers are less interested in whether your choice matches theirs than whether your reasoning is defensible.
How Pushback Is Used in ML Interviews
Interviewers often challenge assumptions:
- “What if this data is biased?”
- “What if user behavior changes?”
- “What if leadership wants faster results?”
They are not saying your approach is wrong.
They are testing:
- How you revise decisions
- Whether you can recalibrate confidence
- How you communicate uncertainty
Candidates who become defensive lose points.
Candidates who adapt thoughtfully gain them.
The Most Common ML Interview Failure Pattern
The most common failure in 2026 ML interviews is:
Sounding knowledgeable, but not trustworthy.
This happens when candidates:
- Recite models without context
- Overemphasize technical sophistication
- Avoid discussing failure
- Can’t explain the real-world consequences of being wrong
Interviewers interpret this as risk.
What Strong Candidates Do Differently
Strong candidates:
- Start with the decision, not the model
- Tie metrics to outcomes
- Acknowledge uncertainty explicitly
- Discuss failure modes proactively
- Adjust when constraints change
They don’t try to impress with trivia.
They aim to demonstrate sound judgment.
Why This Trend Is Accelerating
As ML systems become more powerful:
- The blast radius of mistakes grows
- Ethical, legal, and reputational risks increase
- Human oversight becomes critical
Companies cannot afford ML practitioners who optimize blindly.
That’s why ML interviews in 2026 prioritize decision-making, and why this shift is permanent.
Section 3 Summary
In 2026, ML interviews:
- Assume model knowledge
- Test decision-making instead
Interviewers prioritize:
- Tradeoff reasoning
- Evaluation judgment
- Data awareness
- Impact thinking
- Adaptability under pushback
Candidates who prepare for decisions, not definitions, consistently pass.
Section 4: Behavioral and Technical Interviews Are Merging
One of the most confusing changes candidates experience in 2026 is this:
“Why am I being evaluated on communication and judgment during a technical round?”
The answer is simple, and intentional.
In 2026, companies no longer believe that technical skill and behavioral skill are separable in real work. As a result, interviews no longer separate them cleanly either.
What used to be distinct interview types, coding, system design, ML, behavioral, are now blended evaluations of how you think, decide, and communicate while solving technical problems.
Why the Old Separation Broke Down
Historically, interviews followed a neat structure:
- Technical rounds tested correctness
- Behavioral rounds tested culture fit
- System design tested architecture
This model failed in practice.
Companies repeatedly saw candidates who:
- Passed technical rounds but struggled in production
- Solved problems correctly but made poor decisions
- Had strong resumes but weak ownership and judgment
The realization was uncomfortable but clear:
Technical excellence without sound judgment is a liability.
So interviews evolved to surface both simultaneously.
What “Behavioral Signals” Look Like in Technical Rounds
Behavioral evaluation no longer happens through questions like:
- “Tell me about a conflict”
- “What is your biggest weakness?”
Instead, it appears through how you behave while solving problems.
Interviewers now watch for:
- How you respond to ambiguity
- Whether you ask clarifying questions
- How you handle pushback
- Whether you acknowledge uncertainty
- How you recover from mistakes
These behaviors are far more predictive of job performance than rehearsed stories.
Why Communication Became a Technical Signal
In 2026, communication is no longer a “soft skill.”
It is a core technical signal.
Modern systems are:
- Complex
- Interdependent
- Risk-sensitive
- Built by cross-functional teams
Engineers and ML practitioners must explain:
- Why a decision was made
- What tradeoffs were accepted
- What risks remain
Candidates who cannot articulate reasoning clearly, even if technically correct, are seen as risky hires.
This is why many candidates who “got the answer right” still fail, a pattern explored in The Psychology of Interviews: Why Confidence Often Beats Perfect Answers.
How Interviewers Blend Behavioral Evaluation Into Technical Questions
Interviewers deliberately embed behavioral evaluation into technical prompts by:
- Leaving requirements vague
- Changing constraints mid-solution
- Asking “what would you do if…”
- Challenging assumptions
They are not testing politeness.
They are testing:
- Ownership
- Adaptability
- Emotional regulation
- Intellectual honesty
A calm response to pushback is often more important than the original solution.
The New Red Flags Candidates Don’t Recognize
Because candidates still expect clean separation, they often miss new failure modes.
Common red flags include:
- Defensiveness when challenged
- Overconfidence without evidence
- Refusal to commit to decisions
- Blaming vague requirements
- Treating pushback as disagreement
Interviewers interpret these as indicators of poor collaboration or decision risk.
Why “Culture Fit” Became “Decision Fit”
Companies increasingly avoid vague culture-fit language.
Instead, they evaluate:
- How you reason under pressure
- How you balance speed and safety
- How you own outcomes
- How you interact with uncertainty
This is not about personality.
It is about whether your decision-making style aligns with how the company operates.
That’s why behavioral signals now appear inside technical discussions.
How Strong Candidates Adapt to This Merge
Candidates who succeed in blended interviews:
- Narrate their thinking out loud
- Treat ambiguity as expected, not frustrating
- Accept pushback without ego
- Explain tradeoffs calmly
- Recover visibly from mistakes
They don’t compartmentalize “technical” and “behavioral.”
They understand that how they solve problems is part of the solution.
Why This Feels “Subjective” to Candidates
Many candidates describe modern interviews as subjective.
What they’re experiencing is not randomness, it’s multi-dimensional evaluation.
Instead of scoring:
- Correct vs incorrect
Interviewers now assess:
- Reasoning quality
- Communication clarity
- Judgment stability
- Risk awareness
These signals require human interpretation, which feels less mechanical, but is far more predictive.
Why This Trend Isn’t Reversible
As AI takes over more execution:
- Human judgment becomes more valuable
- Communication becomes more critical
- Risk tolerance becomes more explicit
Separating technical and behavioral evaluation no longer makes sense.
That’s why this merge is accelerating, not temporary.
Section 4 Summary
In 2026:
- Behavioral and technical interviews are no longer separate
- Communication is a technical signal
- Judgment is evaluated continuously
- Pushback is intentional, not adversarial
Candidates who treat interviews as holistic evaluations of thinking consistently outperform those who prepare for isolated rounds.
Conclusion
Technical interviews in 2026 are no longer about proving that you are smart.
They are about proving that you are safe to trust.
As AI lowered the cost of evaluating baseline skills, companies shifted their attention to what still differentiates strong hires:
- Judgment under uncertainty
- Clarity of reasoning
- Tradeoff awareness
- Communication during pressure
- Consistency across contexts
Coding puzzles, system design questions, ML discussions, and behavioral probes didn’t disappear, but their purpose changed.
They are now instruments for extracting signals about how you think, not just what you know.
Candidates who continue preparing for interviews as knowledge exams feel blindsided, frustrated, and confused by rejections that seem unjustified. Candidates who update their mental model, preparing for reasoning, judgment, and adaptability, find interviews more predictable and fair.
The core shift is simple but profound:
Companies are no longer hiring for answers.
They are hiring for decision-making quality.
Once you prepare for that, interview trends in 2026 stop feeling mysterious, and start working in your favor.
FAQs: Technical Interview Trends in 2026
1. Are technical interviews actually harder in 2026?
Not harder, more nuanced. They test judgment and reasoning, not just difficulty.
2. Why do interview questions feel more open-ended now?
Because ambiguity is intentional. Interviewers want to see how you reason without perfect information.
3. Does correctness still matter in coding interviews?
Yes, but correctness alone is no longer sufficient to pass.
4. Why do interviewers interrupt or push back so often?
Pushback is a signal extraction tool, not a disagreement.
5. Are companies moving away from LeetCode-style questions?
They still use them, but as containers for reasoning, not puzzles to solve optimally.
6. Why do system design interviews feel subjective?
Because they evaluate judgment, prioritization, and risk, not fixed architectures.
7. What’s the biggest mistake candidates make in ML interviews now?
Focusing on models instead of decisions and tradeoffs.
8. Why are behavioral signals evaluated during technical rounds?
Because real-world technical work always involves communication and judgment.
9. How do AI tools affect interview evaluation?
They amplify patterns across rounds, making inconsistency more visible.
10. Is speed still important in interviews?
Only when paired with clarity and correctness. Speed alone is a weak signal.
11. Why do “simpler” solutions often perform better in interviews?
They signal judgment, cost awareness, and operational maturity.
12. What signals outweigh correctness in hiring decisions now?
Reasoning clarity, adaptability, communication, and decision ownership.
13. How should interview preparation change in 2026?
Prepare around reasoning frameworks and decision-making, not just question coverage.
14. Why do some technically strong candidates keep failing?
Because they optimize for knowledge display instead of trust-building.
15. What’s the single most important mindset shift for candidates?
Treat interviews as evaluations of how you think, not what you recall.
Final Thought
In 2026, the best interview preparation is not more memorization.
It is learning how to:
- Think out loud
- Make defensible decisions
- Adapt under pressure
- Communicate uncertainty responsibly
When you do that, interviews stop being obstacles, and start becoming accurate reflections of how you’ll perform on the job.
