While the general rule-of-thumb is O.K. to answer #3, it might be helpful to think a little deeper about the meaning of specificity and sensitivity.
Sensitivity is the ability of a test to classify patients as having the disease when they actually have the disease. In other words: the number of correctly positive test results over the number of truly sick patients who were tested: TP / (TP+FN). If you're one for probability, it's simply the probability of a positive test result given the patient is ill; P(+|Sick). If this value is close to 1 (100%), the test is very likely to classify a truly sick person as sick (positive test). So, we know beforehand, if you are sick, it's very likely the test will say you are sick. Once we administer the test, and if it doesn't say you're sick (yields a negative result), it's more probable that you don't have the disease (rule it out). Layman's terms: If I'm very good at calling a dog a dog, and I can't call this animal a dog, it's probably not a dog (rule out dog). Another way to look at this is from the false negative perspective; saying a patient is healthy, when the patient is actually sick. If the sensitivity is high, this means the false negative rate (probability) is low (1-sensitivity= false negative rate). If it's very unlikely to be called healthy when you're actually sick, and if that test shows you a healthy result, you can feel more comfortable that you don't have the disease (rule out).
Specificity is the ability of a test to classify patients as healthy (negative result) when they are actually healthy (no disease). Following the same process as before: the number of correctly negative test results over the number of truly healthy patients: TN / (TN+FP). In probability notation: it's the probability of a negative test result, given the patient is healthy; P(-|Healthy). If this value is close to 1 (100%), the test is very likely to classify a truly healthy person as healthy (show a negative test result). We have a test that is very good at calling truly healthy people healthy, and if it doesn't say you're healthy (it yields a positive result, indicating disease), it's more probable that you do have the disease (rule it in). Layman's terms: If I'm very good at saying what isn't a zebra, and I can't say this animal isn't a zebra, then it's more likely that this animal is a zebra (rule in zebra). And, to be complete, we can look at this from the false positive perspective: calling a healthy patient sick (getting a positive result). A high specificity implies a low false positive rate (probability) (1-specificity= false positive rate). If your probability of being called sick when you are truly healthy is low, and you receive a sick test result (positive), then it's more likely you're sick (rule in).
Realistically, though, you want a test to be highly specific and highly sensitive. Putting too much stock in a test with only high sensitivity or only high specificity isn't likely to lead to optimal decisions.
I hope this helps build a little more intuition behind these terms.