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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q15-Q20):

NEW QUESTION # 15
How do resource groups in SAP AI Core improve the management of machine learning workloads? Note:
There are 2 correct answers to this question.

  • A. They enhance pipeline execution speeds through workload distribution.
  • B. They ensure workload separation for different tenants or departments.
  • C. They enable simultaneous orchestration of Kubernetes clusters.
  • D. They provide isolation for datasets and Al artifacts.

Answer: B,D

Explanation:
Resource groups in SAP AI Core play a vital role in managing machine learning workloads by offering mechanisms for separation and isolation, which are essential for maintaining efficiency and security.
1. Ensuring Workload Separation for Different Tenants or Departments:
* Multitenancy Support:Resource groups enable the segregation of workloads among various tenants or departments within an organization, ensuring that each unit's processes are isolated and managed independently.
* Operational Efficiency:This separation prevents interference between workloads, allowing for tailored resource allocation and management strategies that meet the specific needs of each tenant or department.


NEW QUESTION # 16
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"])

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

Answer: C


NEW QUESTION # 17
What are some SAP recommendations to evaluate pricing and rate information of model usage within SAP's generative Al hub?
Note: There are 2 correct answers to this question.

  • A. Adopt best practice pricing strategies, such as outcome-based pricing
  • B. Weigh the cost of using advanced models against the expected return on investment
  • C. Avoid subscription-based pricing models
  • D. Use pricing models that have fixed rates irrespective of the usage patterns

Answer: A,B


NEW QUESTION # 18
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. Supporting low code evaluations using graphical user interface
  • B. Maintaining data privacy by using data masking techniques
  • C. Creating custom evaluators that meet specific business needs
  • D. Providing metrics to quantitatively assess response quality
  • E. Automating prompt testing across various scenarios

Answer: C,D,E


NEW QUESTION # 19
Which of the following capabilities does the generative Al hub provide to developers? Note: There are 2 correct answers to this question.

  • A. Tools for prompt engineering and experimentation
  • B. Code generation to extend SAP BTP applications
  • C. Proprietary LLMs exclusively
  • D. Integration of foundation models into applications

Answer: A,D

Explanation:
* C. Tools for prompt engineering and experimentation:Generative AI hubs often provide tools and resources to help developers refine their prompts. This is crucial because the quality of the output from a generative AI model heavily depends on how well the prompt is crafted. These tools might include:
* Prompt libraries:Collections of effective prompts for various tasks.
* Prompt testing and analysis:Features to test different prompts and analyze the AI's response.
* Guides and tutorials:Resources to learn about prompt engineering best practices.
* D. Integration of foundation models into applications:Generative AI hubs make it easier for developers to integrate powerful foundation models (large language models like those from Google, OpenAI, etc.) into their own applications. This means developers don't have to build these complex models from scratch. Instead, they can leverage existing models and customize them for their specific needs. This might involve:
* APIs and SDKs:Providing easy-to-use interfaces to access and interact with the foundation models.
* Model customization:Tools to fine-tune existing models on specific datasets or for particular tasks.
* Deployment options:Support for deploying AI models in different environments (cloud, on- premises, etc.).
Why the other options are incorrect:
* A. Proprietary LLMs exclusively:While some generative AI hubs might offer their own proprietary models, they usually provide access to a variety of models, including open-source and those from other providers. This gives developers more flexibility and choice.
* B. Code generation to extend SAP BTP applications:While code generation is a common feature of generative AI, it's not the primary focus of a generative AI hub. The hub's main purpose is to provide access to and facilitate the use of foundation models, not to specifically extend SAP BTP applications.


NEW QUESTION # 20
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