最新的SAP Certified Associate - SAP Generative AI Developer - C-AIG-2412免費考試真題
問題1
Which of the following is a principle of effective prompt engineering?
正確答案: A
說明：（僅 VCESoft 成員可見）
問題2
What is the primary function of the embedding model in a RAG system?
正確答案: A
說明：（僅 VCESoft 成員可見）
問題3
What must be defined in an executable to train a machine learning model using SAP AI Core? Note: There are 2 correct answers to this question.
正確答案: B,C
說明：（僅 VCESoft 成員可見）
問題4
Which of the following capabilities does the generative Al hub provide to developers? Note: There are 2 correct answers to this question.
正確答案: B,C
說明：（僅 VCESoft 成員可見）
問題5
What is a significant risk associated with using LLMs?
正確答案: A
說明：（僅 VCESoft 成員可見）
問題6
You want to assign urgency and sentiment categories to a large number of customer emails. You want to get a valid json string output for creating custom applications. You decide to develop a prompt for the same using generative Al hub.
What is the main purpose of the following code in this context?
prompt_test = """Your task is to extract and categorize messages. Here are some examples:
{{?technique_examples}}
Use the examples when extract and categorize the following message:
{{?input}}
Extract and return a json with the following keys and values:
-"urgency" as one of {{?urgency}}
-"sentiment" as one of {{?sentiment}}
"categories" list of the best matching support category tags from: {{?categories}} Your complete message should be a valid json string that can be read directly and only contains the keys mentioned in t import random random.seed(42) k = 3 examples random. sample (dev_set, k) example_template = """<example> {example_input} examples
'\n---\n'.join([example_template.format(example_input=example ["message"], example_output=json.dumps (example[ f_test = partial (send_request, prompt=prompt_test, technique_examples examples, **option_lists) response = f_test(input=mail["message"])
What is the main purpose of the following code in this context?
prompt_test = """Your task is to extract and categorize messages. Here are some examples:
{{?technique_examples}}
Use the examples when extract and categorize the following message:
{{?input}}
Extract and return a json with the following keys and values:
-"urgency" as one of {{?urgency}}
-"sentiment" as one of {{?sentiment}}
"categories" list of the best matching support category tags from: {{?categories}} Your complete message should be a valid json string that can be read directly and only contains the keys mentioned in t import random random.seed(42) k = 3 examples random. sample (dev_set, k) example_template = """<example> {example_input} examples
'\n---\n'.join([example_template.format(example_input=example ["message"], example_output=json.dumps (example[ f_test = partial (send_request, prompt=prompt_test, technique_examples examples, **option_lists) response = f_test(input=mail["message"])
正確答案: A
說明：（僅 VCESoft 成員可見）
問題7
Which neural network architecture is primarily used by LLMs?
正確答案: B
說明：（僅 VCESoft 成員可見）
