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D-GAI-F-01 Practice Questions

Question # 1
You are designing a Generative Al system for a secure environment. Which of the following would not be a core principle to include in your design?

A. Learning Patterns
B. Creativity Simulation
C. Generation of New Data
D. Data Encryption


B. Creativity Simulation

Explanation:

In the context of designing a Generative AI system for a secure environment, the core principles typically include ensuring the security and integrity of the data, as well as the ability to generate new data. However, Creativity Simulation is not a principle that is inherently related to the security aspect of the design.

The core principles for a secure Generative AI system would focus on:

Learning Patterns: This is essential for the AI to understand and generate data based on learned information.

Generation of New Data: A key feature of Generative AI is its ability to create new, synthetic data that can be used for various purposes.

Data Encryption: This is crucial for maintaining the confidentiality and security of the data within the system.

On the other hand, Creativity Simulation is more about the ability of the AI to produce novel and unique outputs, which, while important for the functionality of Generative AI, is not a principle directly tied to the secure design of such systems. Therefore, it would not be considered a core principle in the context of security1.

The Official Dell GenAI Foundations Achievement document likely emphasizes the importance of security in AI systems, including Generative AI, and would outline the principles that ensure the safe and responsible use of AI technology2. While creativity is a valuable aspect of Generative AI, it is not a principle that is prioritized over security measures in a secure environment. Hence, the correct answer is B. Creativity Simulation.


Question # 2
What is Artificial Narrow Intelligence (ANI)?
A. Al systems that can perform any task autonomously
B. Al systems that can process beyond human capabilities
C. Al systems that can think and make decisions like humans
D. Al systems that can perform a specific task autonomously


D. Al systems that can perform a specific task autonomously

Explanation:

Artificial Narrow Intelligence (ANI) refers to AI systems that are designed to perform a specific task or a narrow set of tasks. The correct answer is option D. Here's a detailed explanation:
Definition of ANI: ANI, also known as weak AI, is specialized in one area. It can perform a particular function very well, such as facial recognition, language translation, or playing a game like chess.

Characteristics: Unlike general AI, ANI does not possess general cognitive abilities. It cannot perform tasks outside its specific domain without human intervention or retraining.

Examples: Siri, Alexa, and Google's search algorithms are examples of ANI. These systems excel in their designated tasks but cannot transfer their learning to unrelated areas.

References:

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25.


Question # 3
Why should artificial intelligence developers always take inputs from diverse sources?

A. To investigate the model requirements properly
B. To perform exploratory data analysis
C. To determine where and how the dataset is produced
D. To cover all possible cases that the model should handle


D. To cover all possible cases that the model should handle

Explanation:

 Diverse Data Sources: Utilizing inputs from diverse sources ensures the AI model is exposed to a wide range of scenarios, dialects, and contexts. This diversity helps the model generalize better and avoid biases that could occur if the data were too homogeneous.

[: "Diverse data sources help AI models to generalize better and avoid biases." (MIT Technology Review, 2019),  Comprehensive Coverage: By incorporating diverse inputs, developers ensure the model can handle various edge cases and unexpected inputs, making it robust and reliable in real-world applications., Reference: "Comprehensive data coverage is essential for creating robust AI models that perform well in diverse situations." (ACM Digital Library, 2021),  Avoiding Bias: Diverse inputs reduce the risk of bias in AI systems by representing a broad spectrum of user experiences and perspectives, leading to fairer and more accurate predictions.,

Reference:

"Diverse datasets help mitigate bias and improve the fairness of AI systems." (AI Now Institute, 2018), , ]


Question # 4
A company is considering using deep neural networks in its LLMs. What is one of the key benefits of doing so?
A. They can handle more complicated problems

B. They require less data
C. They are cheaper to run
D. They are easier to understand


A. They can handle more complicated problems


Explanation:

Deep neural networks (DNNs) are a class of machine learning models that are particularly well-suited for handling complex patterns and high-dimensional data. When incorporated into Large Language Models (LLMs), DNNs provide several benefits, one of which is their ability to handle more complicated problems.

Key Benefits of DNNs in LLMs:

Complex Problem Solving: DNNs can model intricate relationships within data, making them capable of understanding and generating human-like text.

Hierarchical Feature Learning: They learn multiple levels of representation and abstraction that help in identifying patterns in input data.

Adaptability: DNNs are flexible and can be fine-tuned to perform a wide range of tasks, from translation to content creation.

Improved Contextual Understanding: With deep layers, neural networks can capture context over longer stretches of text, leading to more coherent and contextually relevant outputs.

In summary, the key benefit of using deep neural networks in LLMs is their ability to handle more complicated problems, which stems from their deep architecture capable of learning intricate patterns and dependencies within the data. This makes DNNs an essential component in the development of sophisticated language models that require a nuanced understanding of language and context.


Question # 5
A team is analyzing the performance of their Al models and noticed that the models are reinforcing existing flawed ideas. What type of bias is this?
A. Systemic Bias
B. Confirmation Bias
C. Linguistic Bias
D. Data Bias


A. Systemic Bias

Explanation:

When AI models reinforce existing flawed ideas, it is typically indicative of systemic bias. This type of bias occurs when the underlying system, including the data, algorithms, and other structural factors, inherently favors certain outcomes or perspectives. Systemic bias can lead to the perpetuation of stereotypes, inequalities, or unfair practices that are present in the data or processes used to train the model.

The Official Dell GenAI Foundations Achievement document likely covers various types of biases and their impacts on AI systems. It would discuss how systemic bias affects the performance and fairness of AI models and the importance of identifying and mitigating such biases to increase the trust of humans over machines123. The document would emphasize the need for a culture that actively seeks to reduce bias and ensure ethical AI practices.

Confirmation Bias (Option OB) refers to the tendency to process information by looking for, or interpreting, information that is consistent with one’s existing beliefs. Linguistic Bias (Option OC) involves bias that arises from the nuances of language used in the data. Data Bias (Option OD) is a broader term that could encompass various types of biases in the data but does not specifically refer to the reinforcement of flawed ideas as systemic bias does. Therefore, the correct answer is A. Systemic Bias.


Question # 6
What is Transfer Learning in the context of Language Model (LLM) customization?
A. It is where you can adjust prompts to shape the model's output without modifying its underlying weights.
B. It is a process where the model is additionally trained on something like human feedback.
C. It is a type of model training that occurs when you take a base LLM that has been trained and then train it on a different task while using all its existing base weights.
D. It is where purposefully malicious inputs are provided to the model to make the model more resistant to adversarial attacks.


C. It is a type of model training that occurs when you take a base LLM that has been trained and then train it on a different task while using all its existing base weights.

Explanation:

Transfer learning is a technique in AI where a pre-trained model is adapted for a different but related task. Here’s a detailed explanation:

Transfer Learning: This involves taking a base model that has been pre-trained on a large dataset and fine-tuning it on a smaller, task-specific dataset.

Base Weights: The existing base weights from the pre-trained model are reused and adjusted slightly to fit the new task, which makes the process more efficient than training a model from scratch.

Benefits: This approach leverages the knowledge the model has already acquired, reducing the amount of data and computational resources needed for training on the new task.

References:

Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018). A Survey on Deep Transfer Learning. In International Conference on Artificial Neural Networks.

Howard, J., & Ruder, S. (2018). Universal Language Model Fine-tuning for Text Classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).


Question # 7
What is the difference between supervised and unsupervised learning in the context of training Large Language Models (LLMs)?
A. Supervised learning feeds a large corpus of raw data into the Al system, while unsupervised learning uses labeled data to teach the Al system what output is expected.
B. Supervised learning is common for fine tuning and customization, while unsupervised learning is common for base model training.
C. Supervised learning uses labeled data to teach the Al system what output is expected, while unsupervised learning feeds a large corpus of raw data into the Al system, which determines the appropriate weights in its neural network.
D. Supervised learning is common for base model training, while unsupervised learning is common for fine tuning and customization.


C. Supervised learning uses labeled data to teach the Al system what output is expected, while unsupervised learning feeds a large corpus of raw data into the Al system, which determines the appropriate weights in its neural network.

Explanation:

 Supervised Learning: Involves using labeled datasets where the input-output pairs are provided. The AI system learns to map inputs to the correct outputs by minimizing the error between its predictions and the actual labels.

[: "Supervised learning algorithms learn from labeled data to predict outcomes." (Stanford University, 2019),  Unsupervised Learning: Involves using unlabeled data. The AI system tries to find patterns, structures, or relationships in the data without explicit instructions on what to predict. Common techniques include clustering and association., Reference: "Unsupervised learning finds hidden patterns in data without predefined labels." (MIT Technology Review, 2020),  Application in LLMs: Supervised learning is typically used for fine-tuning models on specific tasks, while unsupervised learning is used during the initial phase to learn the broad features and representations from vast amounts of raw text., Reference: "Large language models are often pretrained with unsupervised learning and fine-tuned with supervised learning." (OpenAI, 2021), , ]


Question # 8
What are the three key patrons involved in supporting the successful progress and formation of any Al-based application?
A. Customer facing teams, executive team, and facilities team
B. Marketing team, executive team, and data science team
C. Customer facing teams, HR team, and data science team
D. Customer facing teams, executive team, and data science team


D. Customer facing teams, executive team, and data science team

Explanation:

 Customer Facing Teams: These teams are critical in understanding and defining the requirements of the AI-based application from the end-user perspective. They gather insights on customer needs, pain points, and desired outcomes, which are essential for designing a user-centric AI solution.

[: "Customer-facing teams are instrumental in translating user requirements into technical specifications." (Forbes, 2022),  Executive Team: The executive team provides strategic direction, resources, and support for AI initiatives. They are responsible for aligning the AI strategy with the overall business objectives, securing funding, and fostering a culture that supports innovation and technology adoption.,

Reference:

"Executive leadership is crucial in setting the vision and securing the resources necessary for AI projects." (McKinsey & Company, 2021),  Data Science Team: The data science team is responsible for the technical development of the AI application. They handle data collection, preprocessing, model building, training, and evaluation. Their expertise ensures the AI system is accurate, efficient, and scalable., Reference: "Data scientists play a pivotal role in the development and deployment of AI systems." (Harvard Business Review, 2020), , ]


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EMC D-GAI-F-01 Exam Dumps

Exam Name: Dell GenAI Foundations Achievement
Certification Name: Generative AI

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  • Total Questions: 58
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