Free CompTIA DA0-002 Actual Exam Questions - Question 6 Discussion

Probably C again, but thinking differently: standardization (A) just scales the data and doesn’t actually split customers into groups. Concatenate (B) and appending (D) are more about combining datasets, not segmenting. So if the goal is to create clear customer groups based on spending, binning (C) is the only option that actually cuts the continuous spending data into segments. It’s the only one that directly addresses grouping rather than just transforming or merging data.

Good point about binning, but also think about what concatenation (B) and appending (D) do—they just combine data, so they won’t help with segmenting customers by spending. Standardization (A) normalizes the data but doesn’t create groups on its own. So really, binning (C) is the only technique that turns continuous spending values into distinct segments, which fits the goal perfectly.

It’s C for sure. Binning cuts the continuous spending data into clear groups, which is exactly what segmentation needs, unlike standardization that just rescales numbers without grouping.

C. Binning is the clear choice here since it directly categorizes spending amounts into groups, which matches the goal of segmenting customers. Standardization (A) helps to normalize data but doesn’t create segments by itself. B and D don’t make sense because concatenation combines strings and appending adds rows, neither of which segments data. With a large dataset like this, binning can efficiently create meaningful categories for customer spending behavior.

Maybe C makes the most sense since binning groups spending into categories, which is what segmentation is about. Standardization just adjusts scales but doesn’t create clear segments.

A/C? Standardization (A) is crucial if you plan to use clustering later, but binning (C) gives clear categories right away. Since the question just says segment based on spending, binning might be the most direct route.

Maybe D? Appending is usually about adding rows together, like combining datasets, so it doesn’t really segment customers by spending. B is about joining strings, which doesn’t fit here. That leaves A and C. Standardization is just scaling, so it’s more of a prep step, not the segmentation itself. C (binning) actually groups the customers into spending categories, which fits the segmentation goal much better. So D definitely isn’t right for this task.

Makes sense to pick C here since binning directly divides customers into spending groups, which fits segmentation better than just scaling the data. Standardization (A) is more like a prep step, not the actual segmentation. C

Guessing A here since standardization is a key step before any clustering or segmentation, making the spending data comparable across customers. B and D don’t really apply to segmentation.

A vs C? Standardization (A) helps prep data for clustering by scaling, but binning (C) actually segments customers into spend groups directly. Since they want segmentation, C seems more straightforward here.

Looks like this question is about grouping customers by spending, so it's about segmentation. D (Appending) doesn’t really fit since that’s about adding rows, and B (Concatenate) is more for joining data. Standardization (A) helps with scaling data but doesn’t create segments itself. C (Binning) makes sense because it groups continuous data into categories, so the team can segment customers based on spending ranges. So, I’d pick C here.