1Z0-1110-26専門知識訓練、1Z0-1110-26対応受験

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Oracle 1Z0-1110-26 Exam Syllabus Topics:

SectionWeightObjectives
Model Development and Deployment30%- Model deployment
  • 1. Deployment creation
    • 2. Prediction endpoints
      - Model training
      • 1. Hyperparameter optimization
        • 2. Experiments
          Machine Learning Fundamentals20%- Supervised learning
          • 1. Regression
            • 2. Classification
              - Unsupervised learning
              • 1. Dimensionality reduction
                • 2. Clustering
                  OCI Data Science Service30%- Model catalog
                  • 1. Model versioning
                    • 2. Model metadata
                      - Projects and notebooks
                      • 1. Notebook sessions
                        • 2. Conda environments
                          MLOps and OCI Integration20%- Automation and pipelines
                          • 1. CI/CD integration
                            • 2. Model lifecycle management
                              - OCI ecosystem
                              • 1. IAM and security
                                • 2. Object Storage

                                  >> 1Z0-1110-26専門知識訓練 <<

                                  最新1Z0-1110-26|一番優秀な1Z0-1110-26専門知識訓練試験|試験の準備方法Oracle Cloud Infrastructure Data Science Professional対応受験

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                                  Oracle Cloud Infrastructure Data Science Professional 認定 1Z0-1110-26 試験問題 (Q105-Q110):

                                  質問 # 105
                                  Which model has an open-source, open model format that allows you to run machine learning models on different platforms?

                                  正解:B

                                  解説:
                                  Detailed Answer in Step-by-Step Solution:
                                  Objective: Identify an open model format for cross-platform ML model execution.
                                  Evaluate Options:
                                  A . PySpark: A big data framework, not a model format.
                                  B . PyTorch: An ML framework with its own format, not inherently cross-platform without conversion.
                                  C . TensorFlow: An ML framework with its SavedModel format, not universally open across platforms.
                                  D . ONNX: Open Neural Network Exchange, an open-source format for model interoperability across frameworks.
                                  Reasoning: ONNX is designed for portability (e.g., convert PyTorch to ONNX, run in TensorFlow), unlike framework-specific options.
                                  Conclusion: D is the correct choice.
                                  ONNX (D) is &#x201C;an open-source model format that enables interoperability between ML frameworks like PyTorch and TensorFlow,&#x201D; per OCI documentation. PySpark (A) is a processing tool, while PyTorch (B) and TensorFlow (C) are frameworks with native formats&#x2014;only ONNX ensures cross-platform compatibility.
                                  1: Oracle Cloud Infrastructure Data Science Documentation, &quot;Supported Model Formats&quot;.


                                  質問 # 106
                                  As a data scientist for a hardware company, you have been asked to predict the revenue demand for the upcoming quarter. You develop a time series forecasting model to analyze the dat a. Select the correct sequence of steps to predict the revenue demand values for the upcoming quarter.

                                  正解:D

                                  解説:
                                  Detailed Answer in Step-by-Step Solution:
                                  Prepare Model: Build and train the time series model using historical data.
                                  Verify: Validate the model&#x2019;s accuracy (e.g., using metrics like MAE or RMSE).
                                  Save: Store the trained model (e.g., in the OCI Model Catalog).
                                  Deploy: Make the model available for predictions (e.g., via OCI Model Deployment).
                                  Predict: Generate revenue forecasts for the upcoming quarter.
                                  Evaluate Options: D follows this logical flow; others (e.g., A starts with &#x201C;verify&#x201D; before preparation) don&#x2019;t.
                                  In OCI Data Science, the workflow for time series forecasting involves preparing the model (training), verifying its performance, saving it to the catalog, deploying it, and then predicting. This sequence is standard for ML deployment in OCI, as per the documentation. (Oracle Cloud Infrastructure Data Science Documentation, &quot;Time Series Forecasting Workflow&quot;).


                                  質問 # 107
                                  You are a data scientist working inside a notebook session and you attempt to pip install a package from a public repository that is not included in your conda environment. After running this command, you get a network timeout error. What might be missing from your network configuration?

                                  正解:B

                                  解説:
                                  Detailed Answer in Step-by-Step Solution:
                                  Objective: Diagnose a network timeout during pip install in a notebook session.
                                  Understand Notebook Networking: Sessions run in a VCN; internet access requires specific configs.
                                  Analyze Timeout: Indicates failure to reach the public PyPI repository&#x2014;likely no internet outbound route.
                                  Evaluate Options:
                                  A: NAT Gateway&#x2014;Provides internet access for private subnets&#x2014;correct fix.
                                  B: Service Gateway&#x2014;Accesses OCI services privately, not public internet.
                                  C: FastConnect&#x2014;Links to on-premises, not public internet.
                                  D: VNIC&#x2014;Essential but present by default; doesn&#x2019;t solve internet access.
                                  Reasoning: NAT Gateway enables outbound traffic to public repos like PyPI.
                                  Conclusion: A is correct.
                                  OCI documentation notes: &#x201C;Notebook sessions in a private subnet require a NAT Gateway to access public internet resources, such as PyPI, for package installation via pip. Without it, network timeouts occur.&#x201D; Service Gateway (B) is for OCI services, FastConnect (C) is irrelevant, and VNIC (D) is standard&#x2014;only A resolves the issue.
                                  1: Oracle Cloud Infrastructure Data Science Documentation, &quot;Networking for Notebook Sessions&quot;.


                                  質問 # 108
                                  You realize that your model deployment is about to reach its utilization limit. What would you do to avoid the issue before requests start to fail? Which THREE steps would you perform?

                                  正解:B、C、D

                                  解説:
                                  Detailed Answer in Step-by-Step Solution:
                                  Objective: Prevent deployment failure due to utilization limits.
                                  Understand Utilization: High load requires capacity or throttling.
                                  Evaluate Options:
                                  A: More instances&#x2014;Scales horizontally&#x2014;correct.
                                  B: Delete&#x2014;Stops service, not a fix&#x2014;incorrect.
                                  C: Fewer instances&#x2014;Worsens issue&#x2014;incorrect.
                                  D: Larger VM&#x2014;Scales vertically&#x2014;correct.
                                  E: Reduce bandwidth&#x2014;Controls load&#x2014;correct.
                                  Reasoning: A and D increase capacity, E manages demand&#x2014;effective trio.
                                  Conclusion: A, D, E are correct.
                                  OCI documentation states: &#x201C;To avoid utilization limits, increase instances (A), use a larger compute shape (D), or reduce load balancer bandwidth (E) to manage request rates.&#x201D; B stops service, C reduces capacity&#x2014;only A, D, E align with OCI&#x2019;s deployment scaling options.
                                  1: Oracle Cloud Infrastructure Data Science Documentation, &quot;Model Deployment Scaling&quot;.


                                  質問 # 109
                                  Which architecture is based on the principle of &#x201C;never trust, always verify&#x201D;?

                                  正解:A

                                  解説:
                                  Detailed Answer in Step-by-Step Solution:
                                  Objective: Identify the architecture with &#x201C;never trust, always verify.&#x201D; Evaluate Options:
                                  A: Federated identity&#x2014;Shares auth, not verification-focused.
                                  B: Zero trust&#x2014;Explicitly &#x201C;never trust, always verify&#x201D;&#x2014;correct.
                                  C: Fluid perimeter&#x2014;Adaptive, not the core principle.
                                  D: Defense in depth&#x2014;Layered, not verification-centric.
                                  Reasoning: Zero trust matches the stated principle exactly.
                                  Conclusion: B is correct.
                                  OCI documentation states: &#x201C;Zero trust (B) architecture operates on &#x2018;never trust, always verify,&#x2019; requiring continuous authentication and authorization.&#x201D; A, C, and D have different focuses&#x2014;only B aligns with OCI&#x2019;s security philosophy.
                                  1: Oracle Cloud Infrastructure Security Documentation, &quot;Zero Trust Architecture&quot;.


                                  質問 # 110
                                  ......

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