Hi again Sangay,
There are two ways to provide predictor data to Maxent, either in a SWD (samples with data, p 21 of the Maxent Tutorial), or as a directory of ASCII files. For the SWD approach, you would need to intersect each occurrence point with each of the predictor variables you're including in your model, so that you end up with a .csv file that has columns for 'species', 'longitude', 'latitude', as well as a column for each of the predictor variables. Each row is a separate occurrence record. You can also provide background data manually in SWD format, by generating a random sample of background points and intersecting with your enviro layers. Alternatively you can provide a directory of ASCII files, and let Maxent take its own random sample (default is 10,000 points, I believe) of the 'background'. This SWD method can save on processing time, so it's useful if the extent of the area you're modelling is very large.
If you are NOT using the SWD approach, then you simply make sure the ASCII files for all your environmental predictors (Bioclim, slope, aspect, elevation, etc.) are in the same directory, and specify that as your Environmental Layers directory.
For a discussion of pixel size, the following might be worth a read... I'm sure there are other useful refs, but it's midnight and can't access that part of my brain right now ;)
Guisan et al. Sensitivity of predictive species distribution models to change in grain size. Diversity and Distribution 2007
Good luck,
John