Free CompTIA DataX DY0-001 Actual Exam Questions - Question 10 Discussion
A data scientist is preparing to brief a non-technical audience that is focused on analysis and results. During the modeling process, the data scientist produced the following artifacts:
Charts and dashboards
Model performance statistics (accuracy, precision, recall, F1 score, etc.)
Mathematical descriptions of clustering algorithms included in the selected model
Model selection, justification, and purpose
Code documentation
Data dictionary
Which of the following artifacts should the data scientist include in the briefing? (Choose two.)
It’s A and B for me. The charts and dashboards (A) are the easiest way to communicate results visually, and the model selection with justification (B) helps the audience understand why that approach was taken without getting too technical. Performance stats (E) might be too detailed for a non-technical crowd, and things like code or math (C and D) are definitely out. The data dictionary (F) feels unnecessary unless they specifically asked about data definitions, which they didn’t here.
I’d drop C and D for sure; non-tech folks won’t follow code or math. So, A and B.
It’s A and B for me. Charts quickly show outcomes, and explaining why the model was chosen ties the story together without overwhelming non-tech folks with numbers or code.
E imo, the performance stats give a quick snapshot of model quality without drowning in technical details. F could be useful too if the audience needs clarity on data terms, but less critical than E.
Maybe B and E make sense here. Explaining why the model was chosen (B) helps the audience understand the analysis, and performance stats (E) show how well it works without going too deep into technical stuff.
Makes sense to go for A and E here. The visuals (A) show results clearly, and the performance stats (E) give a measurable sense of how good the model is without too much technical jargon.
Option A and B seem the best fit since the audience wants analysis and results, not the technical nitty-gritty. Charts and dashboards (A) give a clear visual story, and explaining model selection plus purpose (B) helps them understand the “why” behind the results. Including things like code docs or math details would probably just confuse them or lose interest. Performance stats (E) might be too detailed unless simplified a lot, but it's not as essential as those two for non-technical people focused on outcomes rather than methods.
Charts and dashboards (A) are the easiest for non-technical folks to understand, and model selection plus justification (B) gives context why the model matters. Those two keep it clear and relevant here.
F imo, the data dictionary can help clarify terms for non-technical folks, making the presentation clearer. Also, including B is key since explaining why the model was chosen gives important context without jargon.
B imo, because explaining why the model was chosen helps the audience understand the decision-making process. Also, A makes sense since visual charts and dashboards are easier for non-technical people to grasp. I’d skip the code docs and math details—they’re too technical and won’t add value here. Performance stats (E) might be confusing without context, so better keep it simple with A and B.
A and B