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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q28-Q33):

NEW QUESTION # 28
What are some examples of generative Al technologies? Note: There are 2 correct answers to this question.

A. Al models that generate new content based on training data
B. Rule-based algorithms
C. Robotic process automation
D. Foundation models

Answer: A,D

Explanation:
Generative AI encompasses technologies that create new content by learning from existing data.
1. AI Models That Generate New Content Based on Training Data:
* Definition:These models analyze large datasets to produce original outputs, such as text, images, or music, that resemble the patterns found in the training data.
* Examples:Models like GPT-4 generate human-like text, while DALL E creates images from textual descriptions.
2. Foundation Models:
* Definition:Foundation models are large-scale AI models trained on extensive data across various domains. They serve as a base for fine-tuning on specific tasks, enabling versatility in applications.
* Examples:Models such as BERT and GPT-3 are foundation models that can be adapted for tasks like translation, summarization, or content generation.

NEW QUESTION # 29
Match the components of a Retrieval Augmented Generation architecture to the diagram.

Answer:

Explanation:

NEW QUESTION # 30
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. Generate random examples for language model training
B. Train a language model from scratch
C. Evaluate the performance of a language model using few-shot learning
D. Preprocess a dataset for machine learning

Answer: C

NEW QUESTION # 31
What are some benefits of using an SDK for evaluating prompts within the context of generative Al? Note: There are 3 correct answers to this question.

A. Creating custom evaluators that meet specific business needs
B. Providing metrics to quantitatively assess response quality
C. Automating prompt testing across various scenarios
D. Maintaining data privacy by using data masking techniques
E. Supporting low code evaluations using graphical user interface

Answer: A,B,C

NEW QUESTION # 32
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.

A. Infrastructure resources such as CPUs or GPUs
B. Deployment templates for SAP AI Launchpad
C. User scripts to manually execute pipeline steps
D. Pipeline containers to be used

Answer: A,D

NEW QUESTION # 33
......

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