Data Validity Debug: Interview Drills

Interview drills

Interview Drills for Data Validity Debug focuses on invalid sample bursts, clipping incidents, stale-data escapes. The goal is to connect observed symptom to boundary mechanism, ownership, and signoff risk.

PROMPT
You see invalid sample bursts, clipping incidents, stale-data escapes on Data Validity Debug. Walk through root cause and fix.

STRONG ANSWER
1. Names boundary and mode.
2. Explains Data validity must combine framing correctness, saturation/overflow signaling, and synchronization sanity so software trusts converter samples.
3. Requests validity checker report, sampled trace set, anomaly triage memo.
4. Proposes one bounded fix and full regression.

WEAK ANSWER
Jumps to random tuning or tool commands without proving mechanism and ownership.

Diagram to draw on whiteboard

Data validity checks

frame valid + saturation flag + sequence integrity + freshness
all must pass before data is trusted.

Root-cause tree to narrate

ROOT-CAUSE TREE — Data Validity Debug

invalid sample bursts, clipping incidents, stale-data escapes regressed
        |
same silicon / run tags?
   /            \
 no              yes
 |                |
env mismatch    boundary contract or
tag mismatch    true physical issue
 /   \             |
clk   reset      isolate first failing
map   sequence   boundary transition

AMS deep dive

Wrapper and sampling contracts determine data trustworthiness.

Concept diagram

CONVERTER DATA FLOW

sampling clock -> wrapper -> sync boundary -> system consumer

Metric graph

INVALID SAMPLE BURSTS

time --->  _|¯¯|__|¯¯¯|____|¯|_

Reports and artifacts

• sample validity ratio

• calibration convergence

• CDC audit

• overflow/stale sample counters

Mini case study

Mode transition changed sample phase and invalidated wrapper framing assumptions.

Debug branches

• Sample timing contract

• Calibration hook behavior

• Wrapper CDC integrity

Senior review question

Ask: what boundary condition proves this topic is actually closed?

Key takeaways

• State boundary, mode, and evidence tag with every claim.

• Always align analog, digital, and physical owners before signoff decisions.

Common pitfalls

• Fixing averages while tails still fail.

• Skipping package/supply evidence in jitter or SerDes issues.

• Shipping with waivers that lack owner and expiration criteria.

Principal AMS review addendum

Data validity must combine framing correctness, saturation/overflow signaling, and synchronization sanity so software trusts converter samples.

Metric: invalid sample bursts, clipping incidents, stale-data escapes