Providing 1 year monthly data (12 points), how to forecast next month I got an interview question: 
Providing 1 year monthly data (12 points), can be traffic, product consumption, etc. How to forecast next month?
I am confused. This question doesn't looks like a time series question, because only 12 points are provided. If I just answer using January data in the last year as January data of this year, plus average yearly trend movement, is it too simple? 
 A: There's not nearly enough information to suggest a model, or to judge if a model might be too simple. With so little data, subject knowledge (how that particular kind of data tends to behave) becomes critical. 
In an interview, you might take the strategy of suggesting several potential models - "If there's expected to be strong seasonality, and not a strong trend, maybe you could do this; if strong seasonality and strong trend seem likely, maybe do that; if seasonality and trend would be expected to be weak and noise high, perhaps do this ..." and so on. 
(Though if it were me, I'd narrow it down finer than that.) 
One might then say something like "if we really don't know what it is we're dealing with, and with little data, very simple models tend to forecast better than complex ones; perhaps exponential smoothing or double exponential smoothing might be one choice if we don't have more indication of what kind of model might be suitable."

(Added later in response to the request in comments)
As support for the claim that "very simple models tend to forecast better than complex ones" (particularly with little data), see for example, Makridakis and Hibon (2000) [1], discussing Makridakis and Hibon (1979) [2]:

The major conclusion of the Makridakis and Hibon study was that simple methods, such as exponential smoothing, outperformed sophisticated ones. 

The statement was controversial in 1979, but results from the subsequent M-competitions broadly supported that conclusion (though the statements became somewhat more nuanced); similar sentiments can be found (for example) in the forecasting book by Makridakis, Wheelwright and Hyndman.
More broadly, see (for example) Green and Armstrong (2016) [3]:

Our review of studies comparing simple and complex methods — including those in this special issue — found 97 comparisons in 32 papers. None of the papers provide a balance of evidence that complexity improves forecast accuracy. Complexity increases forecast error by 27 percent on average in the 25 papers with quantitative comparisons. The finding is consistent with prior research to identify valid forecasting methods: all 22 previously identified evidence-based forecasting procedures are simple

[More recently, averages of forecasts have in many cases been found to perform quite well, but again those model-average forecasts have often tended to average over fairly simple models]
[1] Makridakis, S and Hibon, M (2000).
"The M-3 Competition: results, conclusions, and implications".
International Journal of Forecasting, 16 (October–December), 451-476
[2] Makridakis, S., & Hibon, M. (1979).
Accuracy of forecasting: an empirical investigation (with discussion).
Journal of the Royal Statistical Society A, 142,  97–145
[3] Kesten C. Green, K.C., and Armstrong, J. S. (2015),
Simple versus complex forecasting: The evidence
March 1, 2015
(forthcoming in Journal of Business Research)
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2643534
