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| Section | Weight | Objectives |
|---|---|---|
| Implement End-to-End Machine Learning Lifecycle | 45% | - Deploy models and manage endpoints - Model saving, cataloging, and versioning - Data preparation, exploration, and transformation - Model development, training, and evaluation - Use AutoML and built-in algorithms |
| OCI Data Science - Introduction & Configuration | 10% | - Overview and core concepts of OCI Data Science - Capabilities of the Accelerated Data Science (ADS) SDK - Tenancy and environment configuration for Data Science |
| Integrate Related OCI Services | 10% | - Use OCI AI and data services with Data Science - Integration with OCI Object Storage, Vault, and Networking |
| Apply MLOps Practices | 20% | - ML pipelines, automation, and reproducibility - Model monitoring, drift detection, and performance tracking - Governance, auditing, and compliance |
| 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 |
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NEW QUESTION # 88
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?
Answer: A,C,E
Explanation:
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—Scales horizontally—correct.
B: Delete—Stops service, not a fix—incorrect.
C: Fewer instances—Worsens issue—incorrect.
D: Larger VM—Scales vertically—correct.
E: Reduce bandwidth—Controls load—correct.
Reasoning: A and D increase capacity, E manages demand—effective trio.
Conclusion: A, D, E are correct.
OCI documentation states: “To avoid utilization limits, increase instances (A), use a larger compute shape (D), or reduce load balancer bandwidth (E) to manage request rates.” B stops service, C reduces capacity—only A, D, E align with OCI’s deployment scaling options.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Scaling".
NEW QUESTION # 89
Which OCI cloud service lets you centrally manage the encryption keys that protect your data and the secret credentials that you use to securely access resources?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the OCI service for key and secret management.
Evaluate Options:
A: Data Safe—Database security, not key management.
B: Cloud Guard—Threat detection, not keys.
C: Data Guard—DB replication, not keys.
D: Vault—Key and secret management—correct.
Reasoning: Vault is OCI’s dedicated service for crypto keys and secrets.
Conclusion: D is correct.
OCI documentation states: “OCI Vault (D) centrally manages encryption keys and secrets, securing data and resource access.” A, B, and C serve other purposes—only D matches per OCI’s securityservices.
1: Oracle Cloud Infrastructure Vault Documentation, "Overview".
NEW QUESTION # 90
You have an embarrassingly parallel or distributed batch job on a large amount of data that you consider running using Data Science Jobs. What would be the best approach to run the workload?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Optimize embarrassingly parallel workload in OCI Jobs.
Evaluate Options:
A: Sequential runs—Inefficient for parallel tasks.
B: Simultaneous runs—Maximizes parallelism—correct.
C: False—Jobs support parallelism.
D: One job per run—Misstates capability, wasteful.
Reasoning: B leverages OCI’s parallel run support.
Conclusion: B is correct.
OCI documentation states: “For embarrassingly parallel tasks, create one Job and launch multiple simultaneous Job Runs (B) to process data efficiently.” A is slow, C is incorrect, and Dovercomplicates—B is the best approach.
1: Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Execution".
NEW QUESTION # 91
You are given the task of writing a program that sorts document images by language. Which Oracle service would you use?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the Oracle service to sort document images by language.
Task Breakdown: Requires extracting text from images (OCR) and detecting language—two potential services involved.
Evaluate Options:
A . Oracle Digital Assistant: Builds chatbots—irrelevant to image or language processing.
B . OCI Language: Detects and classifies languages in text—ideal for sorting after text extraction.
C . OCI Speech: Transcribes audio to text—not applicable to images.
D . OCI Vision: Performs OCR to extract text from images—necessary but not sufficient for language sorting.
Reasoning: The task emphasizes “sorting by language.” OCI Vision extracts text, but OCI Language identifies the language (e.g., English, Spanish). Since the question asks for one service and focuses on sorting, OCI Language (B) is the best fit, assuming text extraction is a precursor step.
Conclusion: B is correct.
OCI Language “provides language detection and classification capabilities, enabling identification of languages in text extracted from documents,” per the documentation. OCI Vision handles OCR, but the sorting task aligns with OCI Language (B). Digital Assistant (A) and Speech (C) don’t apply, and while Vision (D) is a prerequisite, B is the primary service for language sorting as per OCI’s AI service design.
1: Oracle Cloud Infrastructure Language Documentation, "Language Detection Features".
NEW QUESTION # 92
You have built a machine model to predict whether a bank customer is going to default on a loan. You want to use Local Interpretable Model-Agnostic Explanations (LIME) to understand a specific prediction. What is the key idea behind LIME?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Define LIME’s core concept.
Understand LIME: Explains individual predictions with local surrogate models.
Evaluate Options:
A: Complex global, simple local—Correct LIME principle.
B: Agnosticism—True but not the key idea.
C: Global/local similarity—False.
D: Local vs. global agnosticism—Incorrect distinction.
Reasoning: A captures LIME’s local approximation focus.
Conclusion: A is correct.
OCI documentation notes: “LIME (A) explains predictions by approximating complex global models with simpler local surrogate models around specific instances.” B, C, and D misalign—only A reflects LIME’s foundational idea per OCI’s interpretability tools.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Interpretability - LIME".
NEW QUESTION # 93
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