Part A It is easier to control the size of alpha and beta if the sample size is large, therefore in determining their value they need to insure a large sampe size. Because this has to deal with drug safety and efficacy, which are important for FDA approval, the alpha should be appropriately large--meaning that you have a higher chance of rejecting the hypothesis that the drug is safe when it fact actually is. Although it shouldn't be too large since you don't want to send to waste a good product. This would give less room for type II error, which would mean you would accept the null hypothesis when if fact it is false. They don't want to say a drug is safe and effective when it actually isn't.
Part B Type I error means that you reject the null hypothesis when it is true. Therefore for Set 1, you reject that the drug is safe when it actually is. And for Set 2 you reject that a drug is effective when it actually is. For each of these sets, a type I error would be of concern because you'd actually waste a good profitable product due to bad statistics.

Part C Type II error means that you accept the null hypothesis when it is false. For set 1 you would accept that the drug is safe when it actually isn't. For Set 2 you would accept that a drug is effective when it actually isn't For set 1, accepting safety for a drug that could be dangerous, can lead to injury death and subsequently law suits. That's unethical and costly. A type 2 error could ruin the company's reputation. For Set 2, accepting efficacy when it isn't not good for business, because doctor's would quickly stop prescribing your drug or if it is an over the counter, people would stop buying it. It would be a big waste of money, but something the company could recover from rather than something that would ruin their reputation. Either way, for Part B or C, a large sample size and a large alpha would be advisable.