Quantitative research on software development practice often depends on the results of anonymous online surveys. However, it is difficult to be sure that the respondents to such surveys are, in fact, developers. The problem is greater if survey responses are paid for, incentivising respondents to game the system and attracting the attention of bots and professional survey farms. One solution is to include screening questions in the survey to assess respondents’ coding skills. However, this introduces the risk of unfairly excluding considered responses from developers who spent time and energy on the survey. It also risks skewing results by excluding hasty developers and those who are technically weak. We reviewed the responses of all 86 developers who were excluded from analysis of a large unpaid developer survey (n=1,100) because they answered screening questions incorrectly. Based on this review, we estimate that up to 86% of the developers who were excluded were genuine developers. The advent of LLMs casts further doubt on the use of screening questions. Investigating LLM ability, we found that five sample LLMs could answer the first code screening question correctly, and three were able to answer the second. We explored other screening questions and techniques that are typically used in SE surveys, finding that most are susceptible to fraud via LLM use, bots and survey farms. Recruitment strategy may be a better screening technique. We recommend that if survey respondents are compensated only known developers should be invited. Surveys that are widely distributed, e.g. on social media, should not compensate respondents. Instead, researchers should focus on good survey design and imaginative recruitment strategies.