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| Section | Objectives |
|---|---|
| OCI Data Science - Introduction and Configuration | - Configure and manage Data Science resources - Understand OCI Data Science service concepts and architecture - Use OCI Data Science notebooks and sessions |
| Implement End-to-End Machine Learning Lifecycle | - Prepare and manage datasets - Automate machine learning workflows and pipelines - Save and manage models using Model Catalog - Build, train, and evaluate machine learning models - Deploy models and consume model endpoints |
| Apply MLOps Practices | - Monitor and maintain machine learning models - Use best practices for operationalizing ML solutions - Implement model lifecycle management |
| Use Related OCI Services | - Design machine learning solutions for business use cases - Apply OCI services for data ingestion, storage, and processing - Integrate OCI Data and AI services |
| Design and Set Up Data Science Workspace | - Create and configure Data Science projects - Manage notebook sessions and compute resources - Use Accelerated Data Science SDK and open source tools |
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NEW QUESTION # 96
You are a computer vision engineer building an image recognition model. You decide to use Oracle Data Labeling to annotate your image dat a. Which of the following THREE are possible ways to annotate an image in Data Labeling?
Answer: A,C,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify three annotation methods in OCI Data Labeling for images.
Understand Data Labeling: Supports image annotations for ML.
Evaluate Options:
A: Semantic segmentation with boxes—Incorrect; segmentation is pixel-based, not boxes.
B: Single label (classification)—Supported—correct.
C: No bounding boxes—False; boxes are supported.
D: Object detection with boxes—Supported—correct.
E: Multiple labels (multi-label)—Supported—correct.
Reasoning: B (classification), D (detection), E (multi-label) match OCI capabilities.
Conclusion: B, D, E are correct.
OCI documentation states: “Data Labeling supports image annotations via single-label classification (B), object detection with bounding boxes (D), and multi-label classification (E).” A misdefines segmentation, C contradicts support—only B, D, E are valid per OCI’s Data Labeling features.
1: Oracle Cloud Infrastructure Data Labeling Documentation, "Image Annotation Types".
NEW QUESTION # 97
On which option do you set Oracle Cloud Infrastructure Budget?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine where OCI budgets are set.
Understand Budgets: Track spending across OCI resources.
Evaluate Options:
A: Compartments—Scoped within tenancy, not budget root.
B: Instances—Specific resources, not budget scope.
C: Tags—Filter costs, not budget setting.
D: Tenancy—Top-level scope for budgets—correct.
Reasoning: Budgets apply at tenancy, optionally filtered (e.g., by compartment).
Conclusion: D is correct.
OCI documentation states: “Budgets are set at the tenancy level (D), with optional filters like compartments or tags to monitor spending.” A, B, and C are sub-elements—only D is the primary scope per OCI’s cost management.
1: Oracle Cloud Infrastructure Cost Management Documentation, "Setting Budgets".
NEW QUESTION # 98
Which statement about Oracle Cloud Infrastructure Multi-Factor Authentication (MFA) is NOT valid?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the invalid MFA statement.
Evaluate Options:
A: True—Users can’t disable MFA; admin-controlled.
B: False—Multiple devices can be registered—invalid.
C: True—Authenticator app is required.
D: True—Admins can disable MFA.
Reasoning: B contradicts OCI’s multi-device support.
Conclusion: B is incorrect.
OCI documentation states: “Users can register multiple devices for MFA (B is false), must use an authenticator app (C), and cannot disable MFA themselves (A)—admins can (D).” Only B is not valid per OCI’s IAM MFA policy.
1: Oracle Cloud Infrastructure IAM Documentation, "Multi-Factor Authentication".
NEW QUESTION # 99
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?
Answer: D
Explanation:
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".
NEW QUESTION # 100
You have been given a collection of digital files required for a business audit. They consist of several different formats that you would like to annotate using Oracle Cloud Infrastructure (OCI) Data Labeling. Which THREE types of files could this tool annotate?
Answer: C,D,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand OCI Data Labeling Capabilities: OCI Data Labeling is designed to annotate data for machine learning, supporting specific file types like images, text documents, and videos.
Evaluate Options:
A . Video footage: Supported for tasks like object detection or action recognition.
B . Images: Supported for image classification, object detection, etc.
C . Typewritten document: Supported as text data for tasks like entity extraction or classification.
D . Purchase orders: While potentially text-based, this is ambiguous without format clarification (e.g., PDF, image). OCI supports text annotation, but “purchase orders” isn’t a specific file type—it’s assumed as text here.
E . Audio recording: Not supported, as OCI Data Labeling focuses on visual and textual data, not audio.
Select Three: A (video), B (images), and C (text documents) are explicitly supported file types.
OCI Data Labeling supports annotating datasets of images, text, and videos, as per the official documentation. Video footage (A) can be annotated for tasks like object tracking, images (B) for classification or detection, and typewritten documents (C) for text-based annotations (e.g., named entity recognition). Audio files (E) are not supported, and while purchase orders (D) could be text, the question specifies “typewritten document” as a clearer match. (Oracle Cloud Infrastructure Data Labeling Service Documentation, "Supported Data Types").
NEW QUESTION # 101
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