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Technical Interview Preparation Dashboard Template: Questions, Mocks, Weaknesses, and Progress

Copy a free technical interview preparation dashboard to track coding questions, mock scores, weak areas, retests, and weekly progress.

A useful technical interview preparation dashboard is not a vanity score. It is a single system that connects question attempts, comparable mock interviews, recurring weaknesses, scheduled retests, and weekly progress. That makes it easier to decide what to practice next instead of mistaking activity for readiness.

Make a copy of the free Technical Interview Preparation Dashboard, then replace the fictional sample rows with your own practice.

What should a technical interview preparation dashboard track?

The dashboard should answer four practical questions:

What did I practice? Record the question or mock, topic, round, difficulty, date, and conditions.
How did I perform? Separate first attempts from repeats, note whether the result was unassisted, and preserve the evidence behind each score.
What keeps going wrong? Classify the primary weakness, write the root cause, and define a specific corrective drill.
What should I do next? Schedule a retest and use the weekly view to choose the next high-value task.

Those layers matter more than a single “readiness percentage.” A person can complete many questions while repeatedly missing edge cases, relying on hints, or avoiding system design. The dashboard keeps the underlying rows visible so every chart can be traced back to actual practice.

Dashboard layer	Useful fields	Decision it supports
Question practice	Topic, new or repeat, result, unassisted status, time, hints	Which patterns need retrieval practice or a new example?
Mock interviews	Round, conditions, rubric version, dimension ratings, evidence	Are comparable mocks improving, and which dimension is limiting the result?
Weaknesses	Category, root cause, corrective drill, retest date, recurrence	Which failure pattern should receive the next focused session?
Weekly progress	Attempts, novel unassisted rate, mocks, retests, practice time	Is the plan producing better evidence, not just more activity?

Get the editable Google Sheets dashboard

The template is designed as an editable Google Sheet rather than a static screenshot. Use File → Make a copy to create a private version in your Drive:

Copy the Technical Interview Preparation Dashboard

The workbook separates raw practice data from summaries:

Start Here defines each metric and explains how to replace the sample data.
Question Log records individual coding, system design, and behavioral attempts.
Mock Log keeps mock conditions, rubric dimensions, feedback, and next actions together.
Weakness Summary counts recurring categories across both logs.
Weekly Progress shows recent activity and transparent success ratios.
Dashboard presents the charts and headline metrics.
Lists supports the formulas and dropdowns without hiding the categories that drive them.

Keep the original tabs and column names until you understand the formulas. You can add rows, change dropdown values, or extend the charts later. Delete the fictional examples before entering anything sensitive.

Log questions so first attempts and repeats mean different things

A correct repeat does not provide the same evidence as solving a novel problem without help. Repetition can show that a correction stuck, while a first attempt tests transfer to a less familiar prompt. Both are useful, but combining them produces an inflated rate.

For each question, record whether it was New or Repeat, whether the solution was unassisted, the number of hints, elapsed time, and the outcome. Add the primary topic and weakness only after reviewing the attempt. A failed dynamic-programming problem might reflect state-definition confusion, but it could instead be a requirements, implementation, testing, complexity, or time-management issue.

The dashboard uses a bounded metric:

Novel unassisted rate = successful new unassisted attempts ÷ all new attempts

Show the numerator and denominator, such as 7/12, instead of only 58%. Twelve attempts are more informative than two, and the underlying rows reveal whether the sample covers several topics or one narrow pattern.

Research on retrieval practice found that testing can improve delayed retention compared with additional study, although the original experiment used prose passages rather than coding problems. Applying that result to interview preparation is a practical inference: close the solution, reconstruct the reasoning, and schedule an unassisted re-solve instead of counting rereading as mastery. Spacing research also suggests that the best interval depends on how long knowledge must be retained, so the template uses editable review dates rather than claiming one universal schedule.

Score mock interviews only when the conditions are comparable

Mock scores become misleading when a relaxed untimed session is plotted beside a strict 45-minute interview. Before comparing two mocks, capture a comparability signature: round type, target level, duration, difficulty or prompt family, allowed tools, hint policy, rubric version, and rater mode.

Use dimension ratings with evidence rather than one pseudo-precise score. For a coding mock, the dimensions might be requirements, correctness, testing, complexity, and communication. For system design, use requirements, estimates, architecture, data and APIs, reliability, scalability, trade-offs, and communication. Behavioral mocks can use structure, specificity, ownership, reflection, and relevance.

The template uses a small ordinal scale. A rating such as 2 — Partially demonstrated is a category, not a claim that the performance was exactly 50%. Write one observable note beside it: “identified the cache but did not define invalidation,” or “found the edge case only after a prompt.” This makes the score actionable and reduces false precision.

Structured-interview guidance from the U.S. Office of Personnel Management emphasizes consistent questions and rating standards. HackerRank's interview scorecards likewise support defined skills and required comments. These are hiring-assessment sources, not guarantees that a personal mock predicts an employer decision. In this dashboard, consistency is used only to make your own practice trend more interpretable.

Do not convert a mock trend into a pass probability. Different employers, interviewers, prompts, and hiring bars make that inference unsupported. Display the recent median or distribution for genuinely comparable sessions, include the sample size, and keep the evidence notes one click away.

Turn weaknesses into a correction and retest loop

A weakness label is useful only when it changes the next session. “Arrays” is a topic; “fails to test empty input before coding” is a diagnosable behavior. Use this loop:

Observed error → root cause → targeted drill → feedback or correction → delayed retest → recurrence check

For coding, useful categories include requirements, pattern selection, data structure, correctness, implementation, edge cases, testing, complexity, time management, and communication. For system design, consider requirements, capacity estimates, architecture and data flow, APIs, reliability, consistency, bottlenecks, operability, and trade-offs.

Write the smallest corrective action that addresses the cause. If you chose the wrong data structure, compare two alternatives and explain their operations. If you missed edge cases, create a five-case test checklist before another implementation. If a design became vague under scale questions, redo only the capacity estimate and bottleneck analysis before attempting another full mock.

The Weakness Summary should prioritize recurrence, unresolved retests, and recent evidence. It should not label a topic “weak forever” because of one old attempt. Once you complete a delayed retest, record the result. A recurring error deserves another intervention; a closed error can leave the active queue while remaining visible in history.

For deeper postmortems, use the Coding Interview Mistake Log Template. The dashboard is the cross-workstream control panel; the mistake log is the place for a detailed failure analysis. If you need help choosing the overall sequence, the Coding Interview Practice plan covers the broader study workflow.

Use weekly metrics that lead to a next action

Review the dashboard once a week, not after every row. A practical 15-minute review has five steps:

Confirm that recent questions and mocks have complete evidence.
Inspect novel unassisted attempts separately from repeats.
Find the most frequent unresolved weakness.
Check which delayed retests are due or overdue.
Schedule one targeted drill, one mixed set, and the next comparable mock.

Useful weekly metrics include question attempts, novel unassisted successes, hint rate, delayed-retest success, mock count, minutes practiced, error recurrence, and completed reviews. Coverage is an activity signal, not proof of skill. Average solve time is easier to interpret when new and repeated questions are separated and the number of observations is shown.

Interleaved practice—mixing problem types instead of completing one block at a time—can test whether you can select a method from the prompt. A frequently cited experiment found benefits for mathematics practice, so transferring that finding to coding should be treated as a bounded inference rather than a universal rule. Use mixed sets after learning the individual patterns, then let the error data decide whether to continue mixing or return to a focused drill.

Common dashboard mistakes

Tracking only completed questions. Completion hides hints, repeats, and unresolved weaknesses. Preserve the conditions and outcome.

Comparing unlike mocks. Filter by the comparability signature before calling a line chart a trend.

Using a universal readiness score. There is no defensible conversion from personal practice rows to an employer's pass probability.

Recording weak topics without root causes. “Graphs” does not explain what failed. Attach an observable error and a corrective drill.

Collecting more data than you review. If a field never changes a decision, remove it from your personal copy.

Sharing private notes accidentally. Keep your working copy private. Before sharing a dashboard or chart, remove interviewer names, proprietary prompts, employer communications, and any personal information.

Frequently asked questions

Is this technical interview dashboard free?

Yes. The linked Google Sheets template is designed to be copied and edited. Your copy can remain private in your own Google Drive. Review its sharing settings before adding interview notes or sharing a chart.

What is the difference between this dashboard and a LeetCode tracker?

A problem tracker usually records completion by question or topic. This dashboard also connects mocks, rubric evidence, weakness categories, corrective drills, retest dates, and weekly trends across coding, system design, and behavioral preparation.

Should repeated questions count toward my accuracy?

Keep repeats, but report them separately from first attempts. Repeat success can show retention or familiarity; novel unassisted performance provides different evidence about transfer. Combining them obscures both signals.

How often should I update the dashboard?

Log an attempt soon after practice while the evidence is fresh, then review the dashboard weekly. The weekly review should end with specific scheduled actions rather than more data entry.

Can mock interview scores predict whether I will pass?

No defensible pass probability follows from a personal mock score. Use comparable mocks to observe your own dimension-level changes, and keep the rater, rubric, conditions, sample size, and evidence visible.

Final takeaway

A technical interview preparation dashboard works when it shortens the distance between evidence and action. Track first attempts separately from repeats, compare only similar mocks, turn observable errors into targeted drills, and schedule delayed retests. Then use the weekly view to select the next practice task—not to manufacture a confidence score.

Start with the editable template: make your own Google Sheets copy.