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Oracle 1Z0-1110-26 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Model Development and Deployment30%- Model training
  • 1. Hyperparameter optimization
    • 2. Experiments
      - Model deployment
      • 1. Prediction endpoints
        • 2. Deployment creation
          Topic 2: OCI Data Science Service30%- Model catalog
          • 1. Model versioning
            • 2. Model metadata
              - Projects and notebooks
              • 1. Conda environments
                • 2. Notebook sessions
                  Topic 3: MLOps and OCI Integration20%- Automation and pipelines
                  • 1. Model lifecycle management
                    • 2. CI/CD integration
                      - OCI ecosystem
                      • 1. IAM and security
                        • 2. Object Storage
                          Topic 4: Machine Learning Fundamentals20%- Supervised learning
                          • 1. Classification
                            • 2. Regression
                              - Unsupervised learning
                              • 1. Dimensionality reduction
                                • 2. Clustering

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                                  Oracle Cloud Infrastructure Data Science Professional Sample Questions (Q118-Q123):

                                  NEW QUESTION # 118
                                  You are using a custom application with third-party APIs to manage application and data hosted in an Oracle Cloud Infrastructure (OCI) tenancy. Although your third-party APIs don&#x2019;t support OCI&#x2019;s signature-based authentication, you want them to communicate with OCI resources. Which authentication option must you use to ensure this?

                                  Answer: B

                                  Explanation:
                                  Detailed Answer in Step-by-Step Solution:
                                  Objective: Select an auth method for third-party APIs lacking OCI signature support.
                                  Understand OCI Auth: Typically uses API keys, but alternatives exist for non-standard APIs.
                                  Evaluate Options:
                                  A: Username/password&#x2014;Not API-friendly, insecure.
                                  B: API Signing Key&#x2014;Requires signature-based auth, unsupported here.
                                  C: SSH Key&#x2014;For instance access, not APIs.
                                  D: Auth Token&#x2014;Simple token for API calls&#x2014;correct.
                                  Reasoning: Auth Token provides a bearer token for APIs without signature complexity.
                                  Conclusion: D is correct.
                                  OCI documentation states: &#x201C;For third-party APIs not supporting signature-based authentication, use an Auth Token (D), a secure, revocable token for accessing OCI resources via REST APIs.&#x201D; A, B, and C don&#x2019;t fit non-signature scenarios&#x2014;only D ensures compatibility per OCI&#x2019;s IAM options.
                                  1: Oracle Cloud Infrastructure IAM Documentation, &quot;Auth Tokens for API Access&quot;.


                                  NEW QUESTION # 119
                                  You want to create an anomaly detection model using the OCI Anomaly Detection service that avoids as many false alarms as possible. False Alarm Probability (FAP) indicates model performance. How would you set the value of the False Alarm Probability?

                                  Answer: A

                                  Explanation:
                                  Detailed Answer in Step-by-Step Solution:
                                  Objective: Minimize false alarms in OCI Anomaly Detection.
                                  Understand FAP: False Alarm Probability&#x2014;lower FAP means fewer false positives.
                                  Evaluate Options:
                                  A: High FAP&#x2014;Increases false alarms&#x2014;incorrect.
                                  B: Low FAP&#x2014;Reduces false alarms&#x2014;correct.
                                  C: Zero FAP&#x2014;Unrealistic; risks missing true anomalies.
                                  D: Function&#x2014;Vague, not a direct setting.
                                  Reasoning: Low FAP balances sensitivity and false positives&#x2014; aligns with goal.
                                  Conclusion: B is correct.
                                  OCI Anomaly Detection documentation states: &#x201C;Set a low False Alarm Probability (FAP) to minimize false positives, though too low (e.g., zero) may miss anomalies.&#x201D; B fits the goal&#x2014;high (A) increases errors, zero (C) is impractical, and function (D) isn&#x2019;t specified.
                                  1: Oracle Cloud Infrastructure Anomaly Detection Documentation, &quot;Configuring FAP&quot;.


                                  NEW QUESTION # 120
                                  You have trained a binary classifier for a loan application and saved this model into the model catalog. A colleague wants to examine the model, and you need to share the model with your colleague. From the model catalog, which model artifacts can be shared?

                                  Answer: C

                                  Explanation:
                                  Detailed Answer in Step-by-Step Solution:
                                  Understand Model Catalog: The OCI Model Catalog stores trained models and associated details.
                                  Identify Shareable Artifacts: When sharing, all components&#x2014;model file, metadata (e.g., name, description), hyperparameters (e.g., learning rate), and metrics (e.g., accuracy)&#x2014;are accessible.
                                  Evaluate Options:
                                  A: Excludes the model itself&#x2014;incorrect.
                                  B: Excludes metrics&#x2014;incorrect.
                                  C: Excludes metadata and hyperparameters&#x2014;incorrect.
                                  D: Includes all components&#x2014;correct.
                                  Conclusion: D is comprehensive and accurate.
                                  The OCI Model Catalog allows sharing of the model artifact (the trained model), metadata, hyperparameters, and performance metrics, enabling full examination by colleagues. This isdetailed in the official documentation. (Oracle Cloud Infrastructure Data Science Documentation, &quot;Model Catalog Overview&quot;).


                                  NEW QUESTION # 121
                                  While working with Git on Oracle Cloud Infrastructure (OCI) Data Science, you notice that two of the operations are taking more time than the others due to your slow internet speed. Which TWO operations would experience the delay?

                                  Answer: A,E

                                  Explanation:
                                  Detailed Answer in Step-by-Step Solution:
                                  Analyze Git Operations: Identify which depend on internet speed.
                                  Evaluate Options:
                                  A . Staging (git add): Local operation&#x2014;adds files to the index; no network involved.
                                  B . Updating local repo (git pull): Downloads remote changes&#x2014;requires internet, slowed by poor connectivity.
                                  C . Pushing changes (git push): Uploads local commits to remote&#x2014;network-dependent, delayed by slow speed.
                                  D . Committing (git commit): Local snapshot&#x2014;no network needed.
                                  E . Converting to Git repo (git init): Local initialization&#x2014;no internet required.
                                  Reasoning: Only B and C involve network transfers, directly impacted by slow internet.
                                  Conclusion: B and C are the correct choices.
                                  Git operations like git pull (B) and git push (C) rely on network communication with a remote repository, such as OCI Code Repository, and are documented as &#x201C;bandwidth-sensitive&#x201D; in OCI&#x2019;s guides. Local actions like staging (A), committing (D), and initializing (E) occur on the user&#x2019;s machine, unaffected by internet speed. This matches standard Git behavior and OCI&#x2019;s implementation.
                                  1: Oracle Cloud Infrastructure Data Science Documentation, &quot;Using Git in Notebook Sessions&quot;.


                                  NEW QUESTION # 122
                                  Which stage in the machine learning life cycle helps in identifying the imbalance present in the data?

                                  Answer: B

                                  Explanation:
                                  Detailed Answer in Step-by-Step Solution:
                                  Objective: Find the stage where data imbalance (e.g., skewed classes) is identified.
                                  Understand Stages:
                                  Data Modeling: Training models&#x2014;assumes data is prepared.
                                  Data Monitoring: Post-deployment tracking&#x2014;not for initial analysis.
                                  Data Exploration: Analyzing data properties (e.g., distributions)&#x2014;key for imbalance.
                                  Data Access: Retrieving data&#x2014;no analysis yet.
                                  Evaluate Options:
                                  A: Modeling uses data, doesn&#x2019;t detect imbalance&#x2014;incorrect.
                                  B: Monitoring tracks performance, not initial data issues&#x2014;incorrect.
                                  C: Exploration (e.g., via pandas) reveals imbalances&#x2014;correct.
                                  D: Access is just retrieval&#x2014;incorrect.
                                  Reasoning: Imbalance is assessed during exploration (e.g., class counts).
                                  Conclusion: C is correct.
                                  OCI documentation notes: &#x201C;Data Exploration involves analyzing the dataset to understand its characteristics, such as identifying class imbalances or missing values, using tools like ADS SDK or Jupyter notebooks.&#x201D; Modeling (A) and Monitoring (B) occur later, while Access (D) is pre-analysis&#x2014;only Exploration (C) fits this role.
                                  1: Oracle Cloud Infrastructure Data Science Documentation, &quot;Data Exploration Stage&quot;.


                                  NEW QUESTION # 123
                                  ......

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