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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q37-Q42):
NEW QUESTION # 37
What capabilities does the Exploration and Development feature of the generative Al hub provide?
Note: There are 2 correct answers to this question.
- A. Develop and debug ABAP code
- B. Al playground and chat
- C. Prompt editor and management
- D. Automatic model selection
Answer: B,C
NEW QUESTION # 38
Where can you configure language models in generative Al hub?
- A. The Orchestration tab in SAP AI Launchpad
- B. The Configuration tab within ML Operations in SAP AI Launchpad
- C. The Configuration tab of the SAP BTP cockpit
- D. The Models tab in Prompt Editor
Answer: B
NEW QUESTION # 39
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
' --- '.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"])
- A. Preprocess a dataset for machine learning
- B. Generate random examples for language model training
- C. Train a language model from scratch
- D. Evaluate the performance of a language model using few-shot learning
Answer: D
NEW QUESTION # 40
What are the applications of generative Al that go beyond traditional chatbot applications? Note: There are 2 correct answers to this question.
- A. To produce outputs based on software input.
- B. To interpret human instructions and control software systems without necessarily producing output for human consumption.
- C. To follow a specific schema - human input, Al processing, and output for human consumption.
- D. To interpret human instructions and control software systems always producing output for human consumption.
Answer: B,D
Explanation:
* C. To interpret human instructions and control software systems without necessarily producing output for human consumption.This is a key area where generative AI is breaking new ground. Think of it as AI acting as a "middleman" between you and software. Here are some examples:
* Automating complex tasks:You could tell the AI to "optimize this database for performance" or
"find and fix security vulnerabilities in this code." The AI would then interact with the software systems to carry out these instructions, without needing to show you every step or result.
* Controlling robots or IoT devices:Imagine instructing an AI to "adjust the lighting in the meeting room" or "have the robot retrieve the package from the warehouse." The AI translates your instructions into actions for those systems.
* Managing cloud resources:AI could dynamically allocate cloud resources based on your needs, scaling them up or down without your direct intervention.
* D. To interpret human instructions and control software systems always producing output for human consumption.This is more in line with traditional chatbot interactions, but with a broader scope. It's about AI generating outputs that are directly useful or informative for humans. Examples include:
* Creating realistic images or videos:Based on your description, the AI could generate a photorealistic image of a new product design or a short video clip for a marketing campaign.
* Writing different kinds of creative text formats:AI can generate stories, poems,articles, summaries, and even code, all tailored to your specifications.
* Providing personalized recommendations:AI can analyze your preferences and provide recommendations for products, services, or information.
Why the other options are incorrect:
* A. To produce outputs based on software input.This is a general capability of AI, not something specific to generative AI or beyond chatbots. Many AI systems analyze software input (like sensor data or log files) to produce outputs.
* B. To follow a specific schema - human input, AI processing, and output for human consumption.
This describes the basic interaction pattern of many AI systems, including chatbots. It's not something that specifically differentiates generative AI or goes beyond typical chatbot applications.
NEW QUESTION # 41
Why would a user include formatting instructions within a prompt?
- A. To increase the faithfulness of the output
- B. To ensure the model's response follows a desired structure or style
- C. To redirect the output to another software program
- D. To force the model to separate relevant and irrelevant output
Answer: B
NEW QUESTION # 42
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