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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Intro to Generative AI and LLMs15%- Discuss Generative AI Overview
- Explain Prompt Engineering and Instruction Tuning
- Explain Transformers Fundamentals
- Explain LLM Fine Tuning
- Discuss Large Language Models Fundamentals
Topic 2: Intro to DL Foundations15%- Explain Sequence Models (RNN and LSTM)
- Explain Convolutional Models (CNN)
- Discuss Deep Learning Fundamentals
Topic 3: Intro to AI Foundations10%- Discuss AI Applications and Types of Data
- Explain AI vs ML vs DL
- Discuss AI Basics
Topic 4: Intro to OCI AI Services20%- OCI Speech
- OCI Document Understanding
- OCI Language
- OCI Vision
Topic 5: OCI Generative AI and Oracle 23ai10%- Describe OCI Generative AI Services
- Discuss Oracle Vector Search
- Discuss Autonomous Database Select AI
Topic 6: Intro to ML Foundations15%- Discuss Reinforcement Learning Fundamentals
- Explain Machine Learning Basics
- Discuss Unsupervised Learning Fundamentals
- Discuss Supervised Learning Fundamentals
  • 1. Classification
    • 2. Regression
      Topic 7: Get started with OCI AI Portfolio15%- Discuss OCI ML Services Overview
      - Discuss OCI AI Services Overview
      - Discuss OCI AI Infrastructure Overview
      - Explain Responsible AI

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      Oracle Cloud Infrastructure 2026 AI Foundations Associate 1z0-1122-26 Prüfungsfragen mit Lösungen (Q12-Q17):

      12. Frage
      What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?

      Antwort: D


      13. Frage
      Which AI domain can be employed for identifying patterns in images and extract relevant features?

      Antwort: B

      Begründung:
      Computer Vision is the AI domain specifically employed for identifying patterns in images and extracting relevant features. This field focuses on enabling machines to interpret and understand visual information from the world, automating tasks that the human visual system can perform, such as recognizing objects, analyzing scenes, and detecting anomalies. Techniques in Computer Vision are widely used in applications ranging from facial recognition and image classification to medical image analysis and autonomous vehicles.


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

      Antwort: A


      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: C

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

      Antwort: A

      Begründung:
      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.


      17. Frage
      ......

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