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| Section | Weight | Objectives |
|---|---|---|
| OCI Generative AI and Oracle 23ai | 10% | - Discuss Oracle Vector Search - Describe OCI Generative AI Services - Discuss Autonomous Database Select AI |
| Intro to ML Foundations | 15% | - Discuss Unsupervised Learning Fundamentals - Explain Machine Learning Basics - Discuss Reinforcement Learning Fundamentals - Discuss Supervised Learning Fundamentals
|
| Intro to Generative AI and LLMs | 15% | - Discuss Large Language Models Fundamentals - Explain Prompt Engineering and Instruction Tuning - Discuss Generative AI Overview - Explain Transformers Fundamentals - Explain LLM Fine Tuning |
| Intro to OCI AI Services | 20% | - OCI Language - OCI Vision - OCI Speech - OCI Document Understanding |
| Intro to AI Foundations | 10% | - Discuss AI Applications and Types of Data - Explain AI vs ML vs DL - Discuss AI Basics |
| Intro to DL Foundations | 15% | - Discuss Deep Learning Fundamentals - Explain Convolutional Models (CNN) - Explain Sequence Models (RNN and LSTM) |
| Get started with OCI AI Portfolio | 15% | - Discuss OCI AI Services Overview - Explain Responsible AI - Discuss OCI ML Services Overview - Discuss OCI AI Infrastructure Overview |
>> 1z0-1122-26 Prüfungsaufgaben <<
Die Oracle 1z0-1122-26 (Oracle Cloud Infrastructure 2026 AI Foundations Associate) Zertifizierungsprüfung ist eine Prüfung, die Fachkenntnisse und Fertigkeiten eines Menschen testet. Wenn Sie einen Job in der IT-Branche suchen, werden Sie viele Personalmanager nach den relevanten Oracle 1z0-1122-26 IT-Zertifikaten fragen. Wenn Sie das Oracle 1z0-1122-26 (Oracle Cloud Infrastructure 2026 AI Foundations Associate) Zertifikat haben, können Sie sicher Ihre Wettbewerbsfähigkeit verstärken.
11. Frage
How is " Prompt Engineering " different from " Fine-tuning " in the context of Large Language Models (LLMs)?
Antwort: B
Begründung:
In the context of Large Language Models (LLMs), Prompt Engineering and Fine-tuning are two distinct methods used to optimize the performance of AI models.
* Prompt Engineering involves designing and structuring input prompts to guide the model in generating specific, relevant, and high-quality responses. This technique does not alter the model ' s internal parameters but instead leverages the existing capabilities of the model by crafting precise and effective prompts. The focus here is on optimizing how you ask the model to perform tasks, which can involve specifying the context, formatting the input, and iterating on the prompt to improve outputs .
* Fine-tuning , on the other hand, refers to the process of retraining a pretrained model on a smaller, task- specific dataset. This adjustment allows the model to adapt its parameters to better suit the specific needs of the task at hand, effectively " specializing " the model for particular applications. Fine-tuning involves modifying the internal structure of the model to improve its accuracy and performance on the targeted tasks .
Thus, the key difference is that Prompt Engineering focuses on how to use the model effectively through input manipulation, while Fine-tuning involves altering the model itself to improve its performance on specialized tasks.
12. Frage
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?
Antwort: D
Begründung:
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.
13. Frage
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?
Antwort: B
Begründung:
In an Artificial Neural Network (ANN) designed for recognizing handwritten digits, the hidden layers serve the crucial function of capturing the internal representation of the raw image data. These layers learn to extract and represent features such as edges, shapes, and textures from the input pixels, which are essential for distinguishing between different digits. By transforming the input data through multiple hidden layers, the network gradually abstracts the raw pixel data into higher-level representations, which are more informative and easier to classify into the correct digit categories.
14. Frage
Which feature of OCI Speech helps make transcriptions easier to read and understand?
Antwort: D
Begründung:
The text normalization feature of OCI Speech helps make transcriptions easier to read and understand by converting spoken language into a more standardized and grammatically correct format. This process includes correcting grammar, punctuation, and formatting, ensuring that the transcribed text is clear, accurate, and suitable for various use cases. Text normalization enhances the usability of transcriptions, making them more accessible and easier to process in downstream applications.
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15. Frage
You are training a logistic regression model to classify emails as spam or not spam. The model is currently classifying too many emails as spam. What would you do to adjust the model?
Antwort: B
Begründung:
In binary classification, the decision threshold determines how much predicted probability is required before an observation is assigned to the positive class. Here, spam represents the positive class, and the model is producing too many spam classifications, indicating excessive positive predictions or false positives.
Increasing the classification threshold requires a higher predicted probability before an email is labeled as spam, reducing the number of positive classifications. Oracle Machine Learning documentation defines the probability threshold as the decision point used for binary classification and explains that changing this threshold changes true-positive and false-positive behavior. Oracle Docs Altering individual feature weights, iteration counts, or regularization affects model training rather than directly controlling the classification decision boundary. Therefore, increasing the classification threshold is the most appropriate adjustment.
16. Frage
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