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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement End-to-End Machine Learning Lifecycle | 45% | - Model saving, cataloging, and versioning - Use AutoML and built-in algorithms - Model development, training, and evaluation - Data preparation, exploration, and transformation - Deploy models and manage endpoints |
| Topic 2: Design and Set Up Data Science Workspace | 15% | - Configure compute shapes, storage, and networking - Create and manage projects and notebook sessions - Manage access control, security, and IAM integration |
| Topic 3: Apply MLOps Practices | 20% | - Model monitoring, drift detection, and performance tracking - ML pipelines, automation, and reproducibility - Governance, auditing, and compliance |
| Topic 4: OCI Data Science - Introduction & Configuration | 10% | - Tenancy and environment configuration for Data Science - Overview and core concepts of OCI Data Science - Capabilities of the Accelerated Data Science (ADS) SDK |
| Topic 5: Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
誰もが良い仕事とまともな収入を望んでいます。 しかし、彼らが優れた能力と優れた主要な知識を持っていない場合、彼らはまともな仕事を見つけるのは難しいです。 Oracleテスト1Z0-1110-26認定に合格すると、夢を実現し、満足のいく仕事を見つけることができます。 1Z0-1110-26学習教材は、1Z0-1110-26のOracle Cloud Infrastructure Data Science Professional試験に簡単に合格するのに役立つ優れたツールです。 時間をかけて学習する必要はありません。 1Z0-1110-26試験ガイドは高品質であり、当社Oracleの製品を使用する場合、1Z0-1110-26試験に合格する可能性は99％〜100％と非常に高くなっています。
質問 # 111
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? Pick THREE.
正解：A、B、C
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Prevent deployment failure due to high utilization.
Evaluate Options:
A: More instances—Scales capacity—correct.
B: Delete—Stops service, not a solution.
C: Fewer instances—Worsens utilization.
D: Larger VM—Increases resource capacity—correct.
E: Reduce bandwidth—Limits load—correct.
Reasoning: A and D boost capacity, E controls demand—proactive fixes.
Conclusion: A, D, E are correct.
OCI documentation advises: “To handle high utilization, increase instances (A), use a larger compute shape (D), or adjust load balancer bandwidth (E) to manage request volume.” B stops service, C reduces capacity—only A, D, E prevent failure per OCI’s scaling options.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Scaling".
質問 # 112
You have an image classification model in the model catalog which is deployed as an HTTP endpoint using model deployments. Your tenancy administrator is seeing increased demands and has asked you to increase the load balancing bandwidth from the default of 10Mbps. You are provided with the following information:
Payload size in KB = 1024
Estimated requests per second = 120 requests/second (Monday through Friday, in every month, in every year) Buffer percentage = 20%What is the optimal load balancing bandwidth to redeploy your model?
正解：B
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Calculate optimal bandwidth for model deployment.
Given Data:
Payload size = 1024 KB = 1024 * 8 = 8192 Kb (kilobits).
Requests/sec = 120.
Buffer = 20% = 0.2.
Calculate Base Bandwidth:
Bits/sec = Payload * Requests = 8192 Kb * 120 = 983,040 Kb/s = 983.04 Mbps.
Add Buffer:
Total = Base * (1 + Buffer) = 983.04 * 1.2 = 1179.648 Mbps.
Evaluate Options: Closest to 1179.648 Mbps is 1152 Mbps (D)—realistic rounding.
Conclusion: D is correct.
OCI documentation advises: “Calculate bandwidth as payload size (in bits) * requests/sec, then add a buffer (e.g., 20%) for peak loads.” Here, 1024 KB = 8192 Kb, * 120 = 983.04 Mbps, * 1.2 = 1179.648 Mbps. D (1152 Mbps) is the closest practical option—452 (A) and 52 (B) are too low, 7052 (C) excessive.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - Load Balancing".
質問 # 113
Which step is a part of the AutoML pipeline?
正解：B
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Identify a step in OCI’s AutoML pipeline.
Understand AutoML: Automates model building—includes preprocessing, selection, and tuning.
Evaluate Options:
A: Feature Extraction (e.g., PCA) isn’t explicitly part of OCI AutoML—too specific.
B: Saving to Model Catalog is post-AutoML, not a pipeline step.
C: Deployment is a separate action after AutoML—incorrect.
D: Feature Selection (e.g., choosing relevant features) is a core AutoML step—correct.
Reasoning: OCI AutoML automates feature selection, algorithm choice, and tuning—D fits.
Conclusion: D is correct.
OCI AutoML’s pipeline includes “feature selection, algorithm selection, adaptive sampling, and hyperparameter tuning,” per the documentation. Extraction (A) isn’t highlighted, while saving (B) and deployment (C) are post-process actions—only Feature Selection (D) is an integral automated step.
1: Oracle Cloud Infrastructure Data Science Documentation, "AutoML Pipeline".
質問 # 114
Which Security Zone policy is NOT valid?
正解：A
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Spot the invalid Security Zone policy.
Understand Security Zones: Enforce strict security—resources can’t leave easily.
Evaluate Options:
A: Boot volume move—Not allowed; violates zone rules—invalid.
B: Compute no-move—True, enforces security.
C: No public access—True, standard policy.
D: Auto-backup—True, required for compliance.
Reasoning: A contradicts Security Zone immutability.
Conclusion: A is incorrect.
OCI documentation states: “Resources like boot volumes (A) cannot be moved from a Security Zone to a standard compartment—B, C, and D are enforced policies.” A violates OCI’s Security Zone restrictions—only A is not valid.
1: Oracle Cloud Infrastructure Security Zones Documentation, "Policy Restrictions".
質問 # 115
Which Oracle Accelerated Data Science (ADS) classes can be used for easy access to datasets fromreference libraries and index websites, such as scikit-learn?
正解：B
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Identify ADS class for accessing datasets (e.g., scikit-learn).
Evaluate Options:
A: DatasetBrowser—Not an ADS class.
B: DatasetFactory—Loads datasets from sources like scikit-learn—correct.
C: ADSTuner—Hyperparameter tuning, not data access.
D: SecretKeeper—Manages credentials, not datasets.
Reasoning: DatasetFactory simplifies dataset loading (e.g., DatasetFactory.open()).
Conclusion: B is correct.
OCI documentation states: “DatasetFactory in ADS SDK provides methods to easily load datasets from libraries like scikit-learn or other sources (e.g., DatasetFactory.open('sklearn.datasets:load_iris')).” A isn’t real, C tunes models, and D handles secrets—only B fits.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "DatasetFactory".
質問 # 116
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