Oracle Cloud Infrastructure 2025 AI Foundations Associate

Which statement best describes the pretraining process of a Generative AI model?

it learns patterns in unstructured data without requiring labeled training data

What is the purpose of the hidden layer in an artificial neural network?

This layer is optional, and it processes and transforms inputs from the networks weights and activation functions

How does Select AI enhance the interaction with Oracle Autonomous Database?

By enabling natural language prompts instead of SQL code

Which of these summarizes the three guiding principles for AI to be trustworthy?

AI should be lawful, ethical and robust

You need a suitable GPU for massive-scale HPC AI training and inference workloads. Which NVIDIA GPU are you most likely to use

GB200

What is the primary limitation of Recurrent Neural Networks when processing long sequences?

RNN struggle with long-range dependencies due to the vanishing gradient problem.

How does normalization improve the readability of transcriptions in OCI speech?

It converts elements like numbers, dates, and URLs into standard readable formats

What role do tokens play in Large Language Models (LLMs)?

They are individual units into which a piece of text is divided during processing by the model

You are writing poems. You need your computer to help you complete your lines by suggesting right words. Which deep learning model is best suited for this task?

Recurrent Neural Network (RNN)

What is the role of the loss function in supervised learning algorithms

It measures the similarity between predictions and actual targets

A streaming service wants to recommend TV shows based on user behavior. Which machine learning approach should be used?

Supervised Learning

What technique is used to predict the price of a house based on its feautres

Regression

How does Select AI generate SQL queries from natural language questions?

It connects to an LLM, infers the query intent, and formulates the SQL command

A company wants to automate its email filtering system to reduce spam. Which AI technique would you recommend?

Machine Learning

Which algorithm is a non-parametric approach for supervised learning

K-nearest Neighbors (KNN)

A self driving car needs to detect pedestrians and make safe lane changes. Which AI concept is being applied here?

Artificial intelligence

What is the primary goal of fine-tuning a large language model (LLM)

To adjust the pretrained model's parameters using a smaller, task-specific dataset, improving its performance on specific tasks

John has successfully trained a machine learning model using OCI. He now needs to deploy it for real-time predictions where it can process user inputs and generate responses. Which OCI service should he use for deployment?

OCI Data Science

Which statement best describes the primary difference between Large Language Models (LLMs) and traditional machine learning (ML) Models

LLMs are pretrained on a large test corpus whereas ML models need to be trained on custom data

What is the role of a target variable in supervised learning?

It contains the desired output or class labels

Which of these is NOT a common application of unsupervised machine learning

Spam Detection

Which technique involves providing explicit examples in a prompt to guide an LLMs reponse?

Few-shot prompting

T-few fine tuning in OCI generative AI service reduces cost and training time as compared to traditional fine-tuning. Which statement correctly explains the reason behind it?

It selectively updates only a fraction of the model''s weights

What is the primary function of the inference process in machine learning?

Predicting outcomes from new data points

How does Oracle database 23ai allow the use of pretrained AI models for vector search?

By loading ONNX models directly into the database

John needs to analyze the accuracy of OCI speech transcriptions for a legal case. he wants to evaluate how sure the model is about each word in the transcription. Which feature should he use?

Confidence scoring

John works in a news aggregation platform and want to automatically categorize articles into topics like "politics" "technology" and "sports". Which feature of OCI language would help him

Text classification

Which OCI vision feature is useful for identifying whether a document is an invoice, receipt or resume based on its appearance and keywords?

Document Classification

You are training a deep learning model to recognize faces. what type of neural network is best suited for this task?

Convolutional neural Network (CNN)

Which of these components is NOT a part of OCI AI infrastructure

OCI Vault

Emma works for a media company that produces video content for online platforms. She needs to add closed captions to their videos for accessibility. Which OCI speech feature should Emma use?

SRT file support

You need a suitable GPU for a small or medium scale AI training and inference workloads. Which NVIDIA GPU are you most likely to choose?

A100

What is the purpose of the model catalog in OCI Data science

It serves as a repository for storing, tracking, and managing machine learning models

What type of data is most likely to be used with deep learning algorithms

Complex data with non-human interpretable features

Lisa runs an automated security system that monitors parking lots using cameras. She wants to locate and label vehicles and license plates in each frame. Which OCI Vision feature should she use

Object Detection

Mark is analyzing customer receipts and wants to automatically find and save details such as merchant name, transaction date, and total amount for record keeping. Which OCI Vision feature should he use?

Key- Value extraction

David is transcribing a customer support call using OCI speech. The call contains some profane language, and he wants to retain the original words but mark them as inappropriate rather than discarding the,.. Which profanity filtering should David use?

Tagging

You're developing an image classification software that can identify specific objects. Which AI subset would you use?

Deep Learning

You are training a deep model to predict the stock prices. What type of data is this an example of?

Sequential data

You are working on a deep learning project to generate music. Which type of neural network is best suited for this task?

Recurrent Neural Network (RNN)

What is the purpose of the Model Catalog in OCI data science

It serves as a repository for storing, tracking and managing machine learning models.

What task is a generative AI task

Writing a poem based on a given theme. Generative AI refers to AI system that can generate creative content, such as text, images, music.

Which task is an example of Speech related AI tasks

Speech-to-text conversion

Which type of Machine learning is used in autonomous car driving?

Reinforcement learning

Which is NOT an example of vision or image-related AI task?: Classify images, identify objects in images, facial recognition, Repair damaged images

Repair damage images

Which type of machine learning algorithms extracts trends from data?

Unsupervised Machine Learning

Which type of machine learning algorithm learns from outcomes to make decisions?

Reinforcement Learning

What type of machine learning algorithm is used when we want to predict the resale price on a residential property

Regression

What type of function is used in a logistic regression to predict a loan defaulter?

Sigmoidal Function

Which application does NOT require a machine learning solution?

Password Validation

Which algorithm is used for predicting continuous numerical values

Linear regression

Which type of Recurrent Neural Network (RNN) architecture is used for machine translation

Many-to-many

Which essential component of artificial neural network performs weighted summation and applies activation function on input data to produce an output?

Neuron

Which Neural Network has a feedback loop and is designed to handle sequential data?

Recurrent Neural Network

How do hidden layers in neural networks help with character recognition

Enabling the network to learn complex features like edges and shapes

Which sequence model can maintain relevant information over long sequences?

Long short-term memory neural networks

Sequence models are used to solve problems involving sequentially ordered data points or events. Which is NOT the best use case for sequence models?

Image classification and object recognition

What is "in-context learning" in the context of large language models?

Providing a few examples of a target task via the input prompts

Which statement accurately describes generative AI?

Creates new content without making predictions

What aspect of Large Language Models significantly impacts their capabilities, performance, and resource requirements??

Model size and parameters including number of tokens and weights

Fine-tuning is unnecessary for Large Language Models (LLMs) if your application does not involve which specific aspect?

Task specific adaptation

Which OCI Data science feature allows you to use catalogued models as HTTP endpoints on fully manage infrastructure?

Model Deployments

What is the advantage of using OCI Superclusters for AI workloads?

deliver exceptional performance and scalability for complex AI tasks

Which data type is used in Oracle Database 23ai to compare documents?

Vector

Which OCI Data science feature enables you to define and run repeatable machine learning tasks on fully managed infrastructure?

Jobs

Which is NOT and Oracle Cloud Infrastructure AI service?

Translator

Artificial Intelligence

Machines imitate human intelligencce

Machine Learning

Algorithms learn from past data and predict outcome on new data or to identify trends from past data

Deep Learning Algorithim

Algorithms learn from complex data using neural networks and predict outcomes or generate new data

Reinforcement Learning

Learn by reward. It learns to make decisions by trying different actions and receiving feedback

Classification

A supervised ML technique used to categorize or assign data points into predefined classes based on their features or attributes ex) Spam classifier for emails

What does Logistic Regression do?

Helps in predicting something is true or false

Supervised Learning

Classify data or make predictions (disease detection, weather forecasting, spam detection, credit scoring). Learns from labeled data

Unsupervised Learning

understand relationships within datasets (Fraudulent transactions detection, customer segmentation, outlier detection, targeted marketing campaigns). No labelled outputs, algorithm learn the patterns in data and group similar data items

Reinforcement

Make decisions or choices (automated robots, autonomous cars, video games)

Classification

Spam Detector (categorical)

regression

Continuous (House Price Predictor)

Deep Learning is…

Subset of machine learning that focuses on training artificial neural networks (ANNs) with multiple layers. Allows them to automatically learn and extract intricate representations from data

Recurrent Neural Network (RNN)

Handles sequential data, maintains a hidden state or memory, allows information to persist using a feedback loop, captures dependencies

Long Short--Term memory

Works by using specialized memory cell and gating mechanisms to capture long-term dependencies in sequential data. Selectively remembers or forgets info over time

Convolution Neural Network (CNN)

A type of deep learning model designed for processing grid-like data. Reduces images into an easier-to-process form

Gen AI

Learns the underlying patterns in a given data set and uses that knowledge to create new data that shares those patterns

Large Language model

Probabilistic model of text

Tokens

Language model understand tokens rather than words. One token can be a part of a word, an entire word, or punctuation. Number of tokens/word depend on the complexity of the text

Embeddings

Numerical representations of a piece of text converted to number sequences

Encoders

Model that convert a sequence of words to an embedding (primary uses - embedding tokens, sentences and documents)

Decoder

Take a sequence of words and output next word (primary use: text generation, chat-style models)

encoder-decoder

encoder encodes a sequence of words to a set of vectors and the decoder generates the output sequence from the set of vectors. Ideal for sequence to sequence tasks like machine translation

Hallucination

model generated text that is non-factual and or ungrounded