Question

if i survey students about their learning during the last period of the day, my survey may be subject to what?
parallel processing
uniformity
bias
messy data
question 10 1 pts
if your very large data set cannot be processed with the computing power you have available locally, which of the following approaches might help an algorithm finish processing in a reasonable time?
remove some of the data points
use distributed computing
simplify the algorithm
all of these are reasonable

Explanation:

• First question: Surveying students only during the last period of the day can lead to bias. Students might be tired, distracted (thinking about going home, end - of - day activities), or have different behaviors (e.g., rushing through work) compared to other periods. This non - representative sampling (only one time of day) can skew the survey results. Parallel processing is a computing concept, uniformity is about consistency (not relevant here as the sample is limited in time), and messy data is more about data quality (e.g., typos, missing values) rather than the sampling time issue.
• Second question:
• Removing some data points reduces the computational load. If there is less data, the algorithm has less to process.
• Distributed computing allows the data set to be split and processed across multiple computing resources (e.g., multiple servers), which can speed up processing.
• Simplifying the algorithm means it has fewer operations to perform on each data point. So, all of these approaches (removing data points, using distributed computing, simplifying the algorithm) can help an algorithm finish processing a large data set in a reasonable time when local computing power is insufficient.

Answer:

• First question: C. Bias
• Second question: D. All of these are reasonable