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In today's highly competitive Oracle market, having the 1Z0-1122-25 certification is essential to propel your career forward. To earn the Oracle 1Z0-1122-25 certification, you must successfully pass the 1Z0-1122-25 Exam. However, preparing for the Oracle 1Z0-1122-25 exam can be challenging, with potential hurdles like exam anxiety and time constraints.

Oracle 1Z0-1122-25 Exam Syllabus Topics:

Topic	Details
Topic 1	Intro to DL Foundations: This section assesses the expertise of Deep Learning Engineers in understanding deep learning frameworks and architectures. It covers fundamental concepts of deep learning, introduces convolutional neural networks (CNN) for image processing, and explores sequence models like recurrent neural networks (RNN) and long short-term memory (LSTM) networks for handling sequential data.
Topic 2	Intro to ML Foundations: This section evaluates the knowledge of Machine Learning Engineers in understanding machine learning principles and methodologies. It explores the basics of supervised learning, focusing on regression and classification techniques, along with unsupervised learning methods such as clustering and anomaly detection. It also introduces reinforcement learning fundamentals, helping professionals grasp the different approaches used to train AI models.
Topic 3	OCI Generative AI and Oracle 23ai: This section evaluates the skills of Cloud AI Architects in utilizing Oracle’s generative AI capabilities. It includes a deep dive into OCI Generative AI services, Autonomous Database Select AI for enhanced data intelligence and Oracle Vector Search for efficient information retrieval in AI-driven applications.
Topic 4	Intro to AI Foundations: This section of the exam measures the skills of AI Practitioners and Data Analysts in understanding the fundamentals of artificial intelligence. It covers key concepts, AI applications across industries, and the types of data used in AI models. It also explains the differences between artificial intelligence, machine learning, and deep learning, providing clarity on how these technologies interact and complement each other.

Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q37-Q42):

NEW QUESTION # 37
Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?

A. Random Forest
B. Gradient Descent
C. Backpropagation
D. Support Vector Machine

Answer: C

Explanation:
Backpropagation is the algorithm primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN). It is a supervised learning algorithm that calculates the gradient of the loss function with respect to each weight by applying the chain rule, propagating the error backward from the output layer to the input layer. This process updates the weights to minimize the error, thus improving the model's accuracy over time.
Gradient Descent is closely related as it is the optimization algorithm used to adjust the weights based on the gradients computed by backpropagation, but backpropagation is the specific method used to calculate these gradients.

NEW QUESTION # 38
What distinguishes Generative AI from other types of AI?

A. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.
B. Generative AI uses algorithms to predict outcomes based on past data.
C. Generative AI focuses on making decisions based on user interactions.
D. Generative AI involves training models to perform tasks without human intervention.

Answer: A

Explanation:
Generative AI is distinct from other types of AI in that it focuses on creating new content by learning patterns from existing data. This includes generating text, images, audio, and other types of media. Unlike AI that primarily analyzes data to make decisions or predictions, Generative AI actively creates new and original outputs. This ability to generate diverse content is a hallmark of Generative AI models like GPT-4, which can produce human-like text, create images, and even compose music based on the patterns they have learned from their training data.

NEW QUESTION # 39
What is the purpose of Attention Mechanism in Transformer architecture?

A. Apply a specific function to each word individually.
B. Convert tokens into numerical forms (vectors) that the model can understand.
C. Weigh the importance of different words within a sequence and understand the context.
D. Break down a sentence into smaller pieces called tokens.

Answer: C

Explanation:
The purpose of the Attention Mechanism in Transformer architecture is to weigh the importance of different words within a sequence and understand the context. In essence, the attention mechanism allows the model to focus on specific parts of the input sequence when producing an output, which is crucial for understanding context and maintaining coherence over long sequences. It does this by assigning different weights to different words in the sequence, enabling the model to capture relationships between words that are far apart and to emphasize relevant parts of the input when generating predictions.
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NEW QUESTION # 40
What can Oracle Cloud Infrastructure Document Understanding NOT do?

A. Extract tables from documents
B. Classify documents into different types
C. Generate transcript from documents
D. Extract text from documents

Answer: C

Explanation:
Oracle Cloud Infrastructure (OCI) Document Understanding service offers several capabilities, including extracting tables, classifying documents, and extracting text. However, it does not generate transcripts from documents. Transcription typically refers to converting spoken language into written text, which is a function associated with speech-to-text services, not document understanding services. Therefore, generating a transcript is outside the scope of what OCI Document Understanding is designed to do .

NEW QUESTION # 41
What does "fine-tuning" refer to in the context of OCI Generative AI service?

A. Doubling the neural network layers
B. Upgrading the hardware of the AI clusters
C. Adjusting the model parameters to improve accuracy
D. Encrypting the data for security reasons

Answer: C

Explanation:
Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.

NEW QUESTION # 42
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1Z0-1122-25 Practice Mock - 1Z0-1122-25 Dumps Cost

Pass Guaranteed Fantastic Oracle - 1Z0-1122-25 Practice Mock

Oracle 1Z0-1122-25 Exam Syllabus Topics:

Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q37-Q42):

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