What type of AI is designed to perform a single or limited set of tasks, such as voice assistants? Artificial Superintelligence (ASI) Artificial Narrow Intelligence (ANI) Machine Learning Artificial General Intelligence (AGI)

Artificial Narrow Intelligence (ANI)

What is the term for hypothetical AI that can perform any intellectual task a human can? Cognitive Computing Artificial Superintelligence (ASI) Artificial Narrow Intelligence (ANI) Artificial General Intelligence (AGI)

Artificial General Intelligence (AGI)

What type of neural network is optimized for image recognition and processing using special filters? Convolutional Neural Network (CNN) Feedforward Neural Network Recurrent Neural Network (RNN) Radial Basis Function Network

Convolutional Neural Network (CNN)

What is a computational model inspired by the human brain, consisting of layers of artificial neurons? Support Vector Machine Deep Belief Network Artificial Neural Network (ANN) Biological Neural Network

Artificial Neural Network (ANN)

What are the three types of AI commonly discussed in the field of artificial intelligence? _____ Weak AI, Strong AI, Applications Supervised Learning, Unsupervised Learning, Reinforcement Learning Neural Networks, Machine Learning, Deep Learning Data Mining, Big Data, Cloud Computing

Weak AI, Strong AI, Applications

How does the K-Nearest Neighbor algorithm classify or predict new observations? Identifies nearest instances and uses the most common outcome Applies a linear regression model Calculates the mean of all features Uses a decision tree to classify data

Identifies nearest instances and uses the most common outcome

What is the primary objective of the K-Nearest Neighbor algorithm? To reduce dimensionality of data To cluster data into groups To optimize hyperparameters To predict or classify a new observation based on nearest neighbors

To predict or classify a new observation based on nearest neighbors

What are some advantages of using the K-Nearest Neighbor algorithm? Automatically scales features Easy to explain, intuitive, and applicable to any type of data Requires no data preprocessing Provides high accuracy without tuning

Easy to explain, intuitive, and applicable to any type of data

What are some disadvantages of the K-Nearest Neighbor algorithm? Requires a large number of features Limited to linear relationships Computationally expensive and requires storage for the training set Cannot handle missing data

Computationally expensive and requires storage for the training set

What is the effect of choosing a smaller value for k in the K-Nearest Neighbor algorithm? Results in a more generalized model Increases computational efficiency Leads to a higher variance model that may overfit the data Reduces the need for data normalization

Leads to a higher variance model that may overfit the data

What happens when you select a larger value for k in the K-Nearest Neighbor algorithm? Reduces the need for feature scaling Results in a higher bias model, which tends to underfit the data Increases model complexity Improves model accuracy

Results in a higher bias model, which tends to underfit the data

What is a common starting point for selecting the parameter k in the K-Nearest Neighbor algorithm? k equal to the square root of n k equal to the number of features k equal to the total number of data points k equal to the number of classes

k equal to the square root of n

How should you fine-tune the parameter k in the K-Nearest Neighbor algorithm? By increasing k until accuracy decreases By trial and error Using a fixed value for all datasets Using a validation set or through cross-validation

Using a validation set or through cross-validation

What is Multidimensional Scaling (MDS) used for in data analysis? Visualizing the distance or dissimilarity between data points in lower dimensions Optimizing data storage Classifying data into categories Predicting future data trends

Visualizing the distance or dissimilarity between data points in lower dimensions

Which of the following architectures is primarily used by LLMs? Recurrent Neural Networks (RNN) Support Vector Machines (SVM) Convolutional Neural Networks (CNN) A neural network architecture known as transformer

A neural network architecture known as transformer

How can businesses directly benefit from using the LLMs in the SAP AI Core? They can eliminate the need for data scientists. They can receive tailored responses or forecasts. They can reduce all operational costs. They can automate all business processes.

They can receive tailored responses or forecasts.

What is the main focus of the Ideation phase in SAP's product development process? Identifying potential use cases for LLMs within SAP's ecosystem Marketing the product to customers Developing the final product Testing the product with users

Identifying potential use cases for LLMs within SAP's ecosystem

What is a key focus area in the Validation Phase for data engineers and software architects? Market analysis Technical evaluation and architectural guidelines Product launch strategies User interface design

Technical evaluation and architectural guidelines

How does the ToT technique differ from the CoT technique? ToT focuses on a single thread of thought. ToT is a method for model evaluation. ToT allows the model to handle different strands of thought simultaneously, similar to branches on a tree. ToT is used for data preprocessing.

ToT allows the model to handle different strands of thought simultaneously, similar to branches on a tree.

What are the categories of inference parameters in AI models? Input and output Speed and accuracy Randomness and diversity, Length Memory and storage

Randomness and diversity, Length

Under randomness and diversity, what is the temperature parameter in AI models? It determines the speed of processing. It controls the input data size. It influences how creative or predictable the model's output will be. It sets the memory usage limit.

It influences how creative or predictable the model's output will be.

What is the Top K inference parameter in AI models? The maximum number of iterations allowed. The number of most likely candidates that the model considers for the next token. The threshold for error tolerance. The percentage of data processed at once.

The number of most likely candidates that the model considers for the next token.

What is the Top P inference parameter in AI models? The size of the input data batch. The number of layers in the model. The percentage of most likely candidates that the model considers for the next token. The maximum processing time allowed.

The percentage of most likely candidates that the model considers for the next token.

What is classification for prompt engineering use cases? The model must respond with the correct choice from several possible answers. The model generates a summary of the input text. The model predicts future data trends. The model translates text from one language to another.

The model must respond with the correct choice from several possible answers.

What is question-answer without context for prompt engineering use cases? The model provides a detailed explanation of the question. The model must answer the question with its internal knowledge without any context or document. The model uses external documents to find the answer. The model generates a list of possible answers.

The model must answer the question with its internal knowledge without any context or document.

What is question-answer with context for prompt engineering use cases? The model ignores the input text and answers based on its training. The user provides an input text with a question, and the model must answer based on the input text. The model generates a random answer. The model uses a predefined set of answers.

The user provides an input text with a question, and the model must answer based on the input text.

What is summarization for prompt engineering use cases? The model translates the input text into another language. The model responds with a shorter passage that captures the main points of the input. The model predicts the next sentence in the input text. The model generates a list of keywords from the input.

The model responds with a shorter passage that captures the main points of the input.

What is open-ended text generation for prompt engineering use cases? The model generates a summary of the input text. The model predicts the next word in the input text. The model responds with a passage of original text that matches the description. The model translates the input text into another language.

The model responds with a passage of original text that matches the description.

What is code generation for prompt engineering use cases? The model optimizes existing code for performance. The model predicts the next line of code in a script. The model translates code from one language to another. The model generates code based on user specifications, such as text-to-SQL or text-to-Python.

The model generates code based on user specifications, such as text-to-SQL or text-to-Python.

What is the definition of conditional probability Pr(A | B)? \[ \frac{Pr(A \cup B)}{Pr(B)} \] \[ \frac{Pr(A \cap B)}{Pr(B)} \] \[ \frac{Pr(B)}{Pr(A \cap B)} \] \[ \frac{Pr(A)}{Pr(B)} \]

\[ \frac{Pr(A \cap B)}{Pr(B)} \]

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