Free AWS AIP-C01 Actual Exam Questions - Question 12 Discussion
Scenario: Visualize recommendation results across four dimensions in SageMaker Canvas: X-axis (interest score), Y-axis (conversion rate), Color (product category), and Size (number of impressions). Question- Which approach best satisfies the given requirements? Options:
If Canvas doesn’t auto-handle categorical colors, A might need extra steps—does that affect its fit here?
Option B feels off because Data Wrangler is a separate tool, and the question specifically mentions SageMaker Canvas. Also, while scatter plots are great for multiple dimensions, I'm not sure if Data Wrangler’s scatter plot can handle all four dimensions as neatly as Canvas might. Option C’s box plot and D’s bar chart don't seem suitable since box plots focus on distributions, and bar charts struggle representing continuous variables like size. So, even if categorical color support is questionable, A still seems like the best fit given the axes, color, and size requirements in Canvas.
D imo, bar charts don't handle four continuous dimensions well here; size and conversion rate can't both be encoded clearly. A scatter plot (A) fits better because it naturally shows size, color, and two axes.
A imo because scatter plots naturally handle X, Y, color, and size dimensions, matching all four requirements directly. B uses Data Wrangler but might not map size effectively, so less fitting here.
It’s A since scatter plots let you map color and size to different dimensions easily.
Option D makes the most sense here because it directly ties permissions to each user's specific notebook instance, which naturally limits access. The other options either focus on shared instances or lifecycle configs that don’t strictly enforce per-user access. This way, developers get isolated notebook environments but still share access to Rekognition and S3 through broader policies. Seems cleaner and more secure than trying to handle it inside the notebooks themselves.
Maybe D, since it limits access via IAM specifically to assigned notebooks only.
Does the question specify if the notebook instances are in different AWS accounts or all under the same account? That could affect the IAM policy setup mentioned in D. Also, is there any info on whether JupyterLab role-based permissions are supported in this context for option B?