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As a language model AI, I cannot experience challenges, but I can suggest some common difficulties that one may face while trying to find NA values:

1. Incomplete or inconsistent data: NA values can sometimes be introduced due to incomplete or inconsistent data, which can make it harder to pinpoint where the NA values may have originated from.

2. Large datasets: If the dataset is very large, it can be time-consuming and difficult to search for NA values manually, especially if they are scattered throughout the dataset.

3. Multiple NA representations: NA values may be represented in different ways, such as "NA", "N/A", "nan", or simply left blank. Recognizing all possible representations and ensuring that they are accounted for correctly can be a challenge.

4. Data format issues: If the dataset is not properly formatted, it can be challenging to programmatically search for NA values using available tools or libraries.

5. Human error: Finally, human error can also be a factor, especially if the data is manually entered. Mistakes such as typos or incorrect data entry can introduce NA values or cause existing ones to be missed.