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Oracle 1Z0-1110-26 Exam Syllabus Topics:

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

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                                  Quiz 2026 Oracle 1Z0-1110-26: Oracle Cloud Infrastructure Data Science Professional Updated Free Vce Dumps

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                                  Oracle Cloud Infrastructure Data Science Professional Sample Questions (Q56-Q61):

                                  NEW QUESTION # 56
                                  You have created a conda environment in your notebook session. This is the first time you are working with published conda environments. You have also created an Object Storage bucket with permission to manage the bucket. Which TWO commands are required to publish the conda environment?

                                  Answer: D,E

                                  Explanation:
                                  Detailed Answer in Step-by-Step Solution:
                                  Objective: Publish a conda env to Object Storage.
                                  Process: Initialize bucket config, then publish env.
                                  Evaluate Options:
                                  A: Publishes env with slug&#x2014;correct final step.
                                  B: Lists envs&#x2014;unrelated to publishing.
                                  C: Sets bucket details&#x2014;required setup&#x2014;correct.
                                  D: Creates env&#x2014;not publishing.
                                  E: Activates env&#x2014;not for sharing.
                                  Reasoning: C sets up, A executes&#x2014;standard workflow.
                                  Conclusion: A and C are correct.
                                  OCI documentation states: &#x201C;To publish a conda environment, first run odsc conda init (C) with bucket namespace and name, then odsc conda publish (A) with a slug to upload to Object Storage.&#x201D; B, D, and E serve other purposes&#x2014;only A and C are required per OCI&#x2019;s process.
                                  1: Oracle Cloud Infrastructure Data Science CLI Reference, &quot;Publishing Conda Environments&quot;.


                                  NEW QUESTION # 57
                                  Which Oracle Accelerated Data Science (ADS) classes can be used for easy access to datasets fromreference libraries and index websites, such as scikit-learn?

                                  Answer: A

                                  Explanation:
                                  Detailed Answer in Step-by-Step Solution:
                                  Objective: Identify ADS class for accessing datasets (e.g., scikit-learn).
                                  Evaluate Options:
                                  A: DatasetBrowser&#x2014;Not an ADS class.
                                  B: DatasetFactory&#x2014;Loads datasets from sources like scikit-learn&#x2014;correct.
                                  C: ADSTuner&#x2014;Hyperparameter tuning, not data access.
                                  D: SecretKeeper&#x2014;Manages credentials, not datasets.
                                  Reasoning: DatasetFactory simplifies dataset loading (e.g., DatasetFactory.open()).
                                  Conclusion: B is correct.
                                  OCI documentation states: &#x201C;DatasetFactory in ADS SDK provides methods to easily load datasets from libraries like scikit-learn or other sources (e.g., DatasetFactory.open(&apos;sklearn.datasets:load_iris&apos;)).&#x201D; A isn&#x2019;t real, C tunes models, and D handles secrets&#x2014;only B fits.
                                  1: Oracle Cloud Infrastructure ADS SDK Documentation, &quot;DatasetFactory&quot;.


                                  NEW QUESTION # 58
                                  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.

                                  Answer: B

                                  Explanation:
                                  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;).


                                  NEW QUESTION # 59
                                  You are attempting to save a model from a notebook session to the model catalog by using ADS SDK, with resource principal as the authentication signer, and you get a 404 authentication error. Which TWO should you look for to ensure permissions are set up correctly?

                                  Answer: B,C

                                  Explanation:
                                  Detailed Answer in Step-by-Step Solution:
                                  Objective: Troubleshoot a 404 authentication error when saving a model using ADS SDK with resource principal.
                                  Understand Resource Principal: Allows notebook sessions to act as principals via dynamic groups and policies&#x2014;no user credentials needed.
                                  Analyze 404 Error: Indicates an authorization failure&#x2014;likely missing permissions or misconfigured resource principal.
                                  Evaluate Options:
                                  A: True&#x2014;Dynamic group must include notebook sessions (e.g., resource.type = &apos;datasciencenotebooksession&apos;) to authenticate.
                                  B: False&#x2014;Block volume stores artifacts locally, but saving to the catalog is a permission issue, not storage.
                                  C: True&#x2014;Policy must grant manage data-science-models to the dynamic group for catalog access.
                                  D: False&#x2014;Service gateway ensures network access, but 404 is auth-related, not connectivity.
                                  E: False&#x2014;Resource principal uses dynamic group policies, not user group policies.
                                  Reasoning: A (group inclusion) and C (policy permission) are critical for resource principal auth&#x2014;others are tangential.
                                  Conclusion: A and C are correct.
                                  OCI documentation states: &#x201C;To use resource principal with ADS SDK for model catalog operations, ensure (1) a dynamic group includes the notebook session with a matching rule (e.g., all {resource.type = &apos;datasciencenotebooksession&apos;}) and (2) a policy grants the dynamic group manage data-science-models permissions in the compartment.&#x201D; B is unrelated (storage location), D is network-focused, and E applies to user auth&#x2014;not resource principal. A 404 error flags missing auth, fixed by A and C.
                                  1: Oracle Cloud Infrastructure Data Science Documentation, &quot;Using Resource Principals with ADS SDK&quot;.


                                  NEW QUESTION # 60
                                  How are datasets exported in the OCI Data Labeling service?

                                  Answer: C

                                  Explanation:
                                  Detailed Answer in Step-by-Step Solution:
                                  Understand OCI Data Labeling Export: After annotation, datasets are exported for ML use.
                                  Check Supported Formats: OCI Data Labeling exports annotations in a structured, machine-readable format.
                                  Evaluate Options:
                                  A: Binary isn&#x2019;t a standard export format for annotations.
                                  B: XML isn&#x2019;t used; JSON is preferred for flexibility.
                                  C: Line-delimited JSON is the correct format, aligning with ML workflows.
                                  D: CSV is common but not the default for OCI Data Labeling.
                                  Conclusion: C matches the official export format.
                                  OCI Data Labeling exports annotated datasets as line-delimited JSON files, which store each annotation as a separate JSON object per line, suitable for ML pipelines. This is explicitly stated in the documentation. (Oracle Cloud Infrastructure Data Labeling Service Documentation, &quot;Exporting Datasets&quot;).


                                  NEW QUESTION # 61
                                  ......

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