NCA-AIIO Question 73
Select 3You are working as a junior AI operations specialist and are tasked with analyzing a dataset under the supervision of a senior team member. The dataset contains logs from an ML model's training runs, including metrics like accuracy, loss, GPU utilization, and memory usage. The senior team member asks you to identify potential performance bottlenecks. What steps should you take to properly conduct the analysis?
- A
Examine GPU utilization and memory usage patterns to identify hardware-related bottlenecks.
- B
Directly optimize the ML model’s architecture without consulting the senior team member.
- C
Visualize the loss and accuracy trends over the training epochs to identify training issues.
- D
Randomly discard data points from the dataset to make it smaller for easier analysis.
- E
Document your findings and discuss them with the senior team member for confirmation.
Show answer and explanation
Correct answers: A, C, E
Explanation
To properly conduct data analysis under supervision, it is essential to assess hardware utilization and training metrics, document findings, and communicate them with the senior team member for validation. This ensures that the analysis is thorough, accurate, and aligns with team workflows, while avoiding premature optimization or improper handling of the dataset.
- A. Correct.
Examining GPU utilization and memory usage patterns can help identify hardware constraints, such as under-utilization or memory bottlenecks, which is a critical step in diagnosing performance issues.
- B. Incorrect.
Directly optimizing the ML model without consulting the senior team member is not appropriate in this scenario, as you are working under supervision and should follow their guidance.
- C. Correct.
Visualizing loss and accuracy trends provides insights into potential training issues, such as overfitting, underfitting, or instability in the learning process.
- D. Incorrect.
Randomly discarding data points can lead to biased analysis and is not a recommended practice for proper data analysis.
- E. Correct.
Documenting your findings and discussing them with the senior team member ensures that your analysis is reviewed and validated, aligning with the collaborative workflow expected in AI infrastructure and operations tasks.