C100DEV Aggregation Framework Practice Question
You are building an aggregation pipeline on a collection of sensor readings where each document has a `timestamp` field and a `value` field. You need to compute, for each calendar day, the highest `value` and the average `value` across all readings for that day. Which `$group` stage accomplishes this?
⚠ Common exam trap
The trap here is assuming that grouping by the raw `timestamp` field automatically produces daily buckets, when in fact it creates one group per distinct timestamp.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
{ $group: { _id: { $dateToString: { format: "%Y-%m-%d", date: "$timestamp" } }, maxValue: { $max: "$value" }, avgValue: { $avg: "$value" } } }
The `$group` stage must group by a day-level key while applying the correct accumulators to the `value` field. Using `$dateToString` in `_id` collapses all readings for a calendar day into one document, and `$max` plus `$avg` return the highest and mean values. Grouping by raw timestamps or using `$sum` fails to satisfy the daily maximum and average requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
{ $group: { _id: { $dateToString: { format: "%Y-%m-%d", date: "$timestamp" } }, maxValue: { $max: "$value" }, avgValue: { $sum: "$value" } } }
Why it's wrong here
The grouping key is correct, but `$sum` adds values rather than averaging them. The scenario requires the average `value` per day, which is the `$avg` accumulator. Using `$sum` would return the total of all readings for the day, not the mean, so the result would not meet the stated requirement.
- ✗
{ $group: { _id: "$timestamp", maxValue: { $max: "$value" }, avgValue: { $avg: "$value" } } }
Why it's wrong here
Grouping by the raw `timestamp` value creates one group per distinct timestamp, not per calendar day. Since sensor readings occur at many different times within a day, this would produce far too many groups and would not aggregate by day. `$dateToString` or a date truncation expression is needed to collapse readings into daily buckets.
- ✗
{ $group: { _id: { $dateToString: { format: "%Y-%m-%d", date: "$timestamp" } }, maxValue: { $max: "$value" }, avgValue: { $avg: "$timestamp" } } }
Why it's wrong here
The `$avg` accumulator is applied to `timestamp` instead of `value`. Averaging timestamps produces a meaningless average time, not the average sensor reading. The scenario asks for the average `value`, so the accumulator must reference the `value` field to return the correct result.
- ✓
{ $group: { _id: { $dateToString: { format: "%Y-%m-%d", date: "$timestamp" } }, maxValue: { $max: "$value" }, avgValue: { $avg: "$value" } } }
Why this is correct
This stage groups by the formatted date string derived from `timestamp` and applies the `$max` and `$avg` accumulators to `value`. `$max` returns the highest value per group and `$avg` returns the mean, both valid accumulators. The `_id` expression using `$dateToString` correctly produces one group per calendar day, which is exactly what the scenario requires.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official MongoDB exam blueprint
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