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Oracle 1Z0-1122-24 Exam Questions

Exam Name: Oracle Cloud Infrastructure 2024 AI Foundations Associate
Exam Code: 1Z0-1122-24
Related Certification(s):
  • Oracle Cloud Certifications
  • Oracle Cloud Infrastructure Certifications
Certification Provider: Oracle
Actual Exam Duration: 60 Minutes
Number of 1Z0-1122-24 practice questions in our database: 41 (updated: Dec. 10, 2024)
Expected 1Z0-1122-24 Exam Topics, as suggested by Oracle :
  • Topic 1: Intro to AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.
  • Topic 2: Intro to ML Foundations: This section covers Machine Learning (ML) which is a critical area within AI, and understanding its fundamentals is crucial for anyone interested in this field. The section covers delving into the basics of ML allowing for a better grasp of how machines learn from data.
  • Topic 3: Intro to DL Foundations: This section covers Deep Learning (DL) is a subset of ML that focuses on neural networks with many layers, and understanding its core concepts is vital for working with complex models.
  • Topic 4: Intro to Generative AI & LLMs: This section is about covering generative AI which represents a powerful area of AI that involves creating new content or data. Exploring the overview of Generative AI helps in understanding its potential and applications.
  • Topic 5: Get Started with OCI AI Portfolio: This section is about the OCI AI Portfolio which offers a comprehensive suite of services and infrastructure for developing and deploying AI models. Exploring the overview of OCI AI Services provides insight into the tools available for AI development.
  • Topic 6: OCI Generative AI and Oracle 23ai: This section covers CI Generative AI Services that are a key component of Oracle's AI offerings, and exploring these services provides a clear understanding of how Oracle supports generative AI applications.
  • Topic 7: Intro to OCI AI Services: This section is about exploring OCI AI Services and their related APIs, such as those for Language, Vision, Document Understanding, and Speech, which are essential for developers and businesses looking to integrate AI into their operations.
Disscuss Oracle 1Z0-1122-24 Topics, Questions or Ask Anything Related

Art

11 days ago
I just cleared the Oracle Cloud Infrastructure 2024 AI Foundations Associate exam. Pass4Success practice questions were a great help. One question that caught me off guard was about OCI AI Services, asking to list the primary services and their use cases. I wasn't confident but still passed.
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Bo

16 days ago
Cleared the OCI AI Foundations exam with flying colors. Couldn't have done it without Pass4Success!
upvoted 0 times
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Alyssa

26 days ago
Excited to announce that I passed the OCI 2024 AI Foundations Associate exam! The practice questions from Pass4Success were invaluable. There was a question about DL Foundations, specifically asking to differentiate between convolutional and recurrent neural networks. I had to guess, but I passed!
upvoted 0 times
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Ma

1 months ago
I passed the Oracle Cloud Infrastructure 2024 AI Foundations Associate exam with the help of Pass4Success practice questions. One question that stumped me was about ML Foundations, asking to describe the steps involved in a typical machine learning pipeline. I wasn't entirely sure but still made it through.
upvoted 0 times
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Carman

2 months ago
Oracle certification in the bag! Pass4Success made studying a breeze with their relevant materials.
upvoted 0 times
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Yong

2 months ago
Just passed the OCI 2024 AI Foundations Associate exam! The Pass4Success practice questions were spot on. There was a question on Generative AI & LLMs, asking to explain the difference between a transformer model and a traditional neural network. I wasn't sure about the specifics but managed to pass.
upvoted 0 times
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Pamella

2 months ago
I successfully cleared the Oracle Cloud Infrastructure 2024 AI Foundations Associate exam. Thanks to Pass4Success practice questions, I felt well-prepared. One challenging question was about OCI Generative AI and Oracle 23ai, specifically how Oracle 23ai integrates with other OCI services. I had to guess, but it worked out!
upvoted 0 times
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Sabra

3 months ago
Aced the OCI AI Foundations test! Thanks Pass4Success for the spot-on practice questions.
upvoted 0 times
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Ona

3 months ago
The exam tests knowledge of OCI's AI operations (AIOps) capabilities. Study how OCI uses AI for IT operations and monitoring.
upvoted 0 times
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Glory

3 months ago
Thrilled to share that I passed the OCI 2024 AI Foundations Associate exam! The practice questions from Pass4Success were a lifesaver. There was a tricky question about the OCI AI Portfolio, asking to identify the primary services included in it. I wasn't entirely confident about the answer, but I still passed!
upvoted 0 times
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Viola

3 months ago
Finally, know about OCI's AI research initiatives. Understand how Oracle contributes to open-source AI projects and collaborates with research institutions.
upvoted 0 times
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Freeman

3 months ago
I just passed the Oracle Cloud Infrastructure 2024 AI Foundations Associate exam! The Pass4Success practice questions were incredibly helpful. One question I remember was about the key components of AI Foundations, specifically asking to differentiate between supervised and unsupervised learning. I was a bit unsure about the exact differences but managed to get through it.
upvoted 0 times
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Arthur

4 months ago
Just passed the Oracle Cloud Infrastructure AI Foundations exam! Pass4Success really helped me prepare quickly.
upvoted 0 times
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Free Oracle 1Z0-1122-24 Exam Actual Questions

Note: Premium Questions for 1Z0-1122-24 were last updated On Dec. 10, 2024 (see below)

Question #1

Which AI Ethics principle leads to the Responsible AI requirement of transparency?

Reveal Solution Hide Solution
Correct Answer: A

Explicability is the AI Ethics principle that leads to the Responsible AI requirement of transparency. This principle emphasizes the importance of making AI systems understandable and interpretable to humans. Transparency is a key aspect of explicability, as it ensures that the decision-making processes of AI systems are clear and comprehensible, allowing users to understand how and why a particular decision or output was generated. This is critical for building trust in AI systems and ensuring that they are used responsibly and ethically.

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Question #2

Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

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Correct Answer: B

The OCI Generative AI service offers various categories of pretrained foundational models, including Embedding models, Chat models, and Generation models. These models are designed to perform a wide range of tasks, such as generating text, answering questions, and providing contextual embeddings. However, Translation models, which are typically used for converting text from one language to another, are not a category available in the OCI Generative AI service's current offerings. The focus of the OCI Generative AI service is more aligned with tasks related to text generation, chat interactions, and embedding generation rather than direct language translation.


Question #3

Which AI Ethics principle leads to the Responsible AI requirement of transparency?

Reveal Solution Hide Solution
Correct Answer: A

Explicability is the AI Ethics principle that leads to the Responsible AI requirement of transparency. This principle emphasizes the importance of making AI systems understandable and interpretable to humans. Transparency is a key aspect of explicability, as it ensures that the decision-making processes of AI systems are clear and comprehensible, allowing users to understand how and why a particular decision or output was generated. This is critical for building trust in AI systems and ensuring that they are used responsibly and ethically.

Top of Form

Bottom of Form


Question #4

What is the key feature of Recurrent Neural Networks (RNNs)?

Reveal Solution Hide Solution
Correct Answer: C

Recurrent Neural Networks (RNNs) are a class of neural networks where connections between nodes can form cycles. This cycle creates a feedback loop that allows the network to maintain an internal state or memory, which persists across different time steps. This is the key feature of RNNs that distinguishes them from other neural networks, such as feedforward neural networks that process inputs in one direction only and do not have internal states.

RNNs are particularly useful for tasks where context or sequential information is important, such as in language modeling, time-series prediction, and speech recognition. The ability to retain information from previous inputs enables RNNs to make more informed predictions based on the entire sequence of data, not just the current input.

In contrast:

Option A (They process data in parallel) is incorrect because RNNs typically process data sequentially, not in parallel.

Option B (They are primarily used for image recognition tasks) is incorrect because image recognition is more commonly associated with Convolutional Neural Networks (CNNs), not RNNs.

Option D (They do not have an internal state) is incorrect because having an internal state is a defining characteristic of RNNs.

This feedback loop is fundamental to the operation of RNNs and allows them to handle sequences of data effectively by 'remembering' past inputs to influence future outputs. This memory capability is what makes RNNs powerful for applications that involve sequential or time-dependent data.


Question #5

What role do Transformers perform in Large Language Models (LLMs)?

Reveal Solution Hide Solution
Correct Answer: C

Transformers play a critical role in Large Language Models (LLMs), like GPT-4, by providing an efficient and effective mechanism to process sequential data in parallel while capturing long-range dependencies. This capability is essential for understanding and generating coherent and contextually appropriate text over extended sequences of input.

Sequential Data Processing in Parallel:

Traditional models, like Recurrent Neural Networks (RNNs), process sequences of data one step at a time, which can be slow and difficult to scale. In contrast, Transformers allow for the parallel processing of sequences, significantly speeding up the computation and making it feasible to train on large datasets.

This parallelism is achieved through the self-attention mechanism, which enables the model to consider all parts of the input data simultaneously, rather than sequentially. Each token (word, punctuation, etc.) in the sequence is compared with every other token, allowing the model to weigh the importance of each part of the input relative to every other part.

Capturing Long-Range Dependencies:

Transformers excel at capturing long-range dependencies within data, which is crucial for understanding context in natural language processing tasks. For example, in a long sentence or paragraph, the meaning of a word can depend on other words that are far apart in the sequence. The self-attention mechanism in Transformers allows the model to capture these dependencies effectively by focusing on relevant parts of the text regardless of their position in the sequence.

This ability to capture long-range dependencies enhances the model's understanding of context, leading to more coherent and accurate text generation.

Applications in LLMs:

In the context of GPT-4 and similar models, the Transformer architecture allows these models to generate text that is not only contextually appropriate but also maintains coherence across long passages, which is a significant improvement over earlier models. This is why the Transformer is the foundational architecture behind the success of GPT models.


Transformers are a foundational architecture in LLMs, particularly because they enable parallel processing and capture long-range dependencies, which are essential for effective language understanding and generation.


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