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| Section | Objectives |
|---|---|
| Apply MLOps Practices | - Monitor and maintain machine learning models - Use best practices for operationalizing ML solutions - Implement model lifecycle management |
| Implement End-to-End Machine Learning Lifecycle | - Prepare and manage datasets - Build, train, and evaluate machine learning models - Automate machine learning workflows and pipelines - Save and manage models using Model Catalog - Deploy models and consume model endpoints |
| Design and Set Up Data Science Workspace | - Create and configure Data Science projects - Use Accelerated Data Science SDK and open source tools - Manage notebook sessions and compute resources |
| Use Related OCI Services | - Integrate OCI Data and AI services - Design machine learning solutions for business use cases - Apply OCI services for data ingestion, storage, and processing |
| OCI Data Science - Introduction and Configuration | - Understand OCI Data Science service concepts and architecture - Use OCI Data Science notebooks and sessions - Configure and manage Data Science resources |
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NEW QUESTION # 109
Which type of firewalls are designed to protect against web application attacks, such as SQL injection and cross-site scripting?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the firewall type protecting against web app attacks like SQL injection and XSS.
Understand Firewall Types:
Stateful Inspection: Tracks connection states, not app-specific.
Web Application Firewall (WAF): Targets web app vulnerabilities.
Incident Firewall: Not a recognized term.
Packet Filtering: Basic packet rules, not app-aware.
Evaluate Options:
A: Stateful—General network, not web-specific—incorrect.
B: WAF—Designed for SQLi, XSS—correct.
C: Incident—Non-existent—incorrect.
D: Packet—Low-level, not app-focused—incorrect.
Reasoning: WAF specializes in web app security—matches requirement.
Conclusion: B is correct.
OCI documentation states: “Web Application Firewall (WAF) (B) protects against web application attacks like SQL injection and cross-site scripting by inspecting HTTP traffic.” A and D handle network-level threats, C isn’t real—only B aligns with OCI’s WAF purpose.
1: Oracle Cloud Infrastructure WAF Documentation, "Overview".
NEW QUESTION # 110
You have a complex Python code project that could benefit from using Data Science Jobs as it is a repeatable machine learning model training task. The project contains many sub-folders and classes. What is the best way to run this project as a Job?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Run a complex Python project as an OCI Job.
Evaluate Options:
A: Auto-identification—False; entrypoint must be set.
B: Rewrite—Unnecessary, inefficient.
C: Auto-executable—False; needs explicit entrypoint.
D: ZIP with entrypoint—Correct, flexible approach.
Reasoning: D preserves structure, specifies execution.
Conclusion: D is correct.
OCI documentation states: “For complex projects, ZIP the folder and upload as a Job artifact, then set JOB_RUN_ENTRYPOINT (D) to the main executable (e.g., main.py).” Auto-detection (A, C) isn’t supported, and B discards structure—D is best.
1: Oracle Cloud Infrastructure Data Science Documentation, "Job Artifacts".
NEW QUESTION # 111
You are a data scientist working for a manufacturing company. You have developed a forecasting model to predict the sales demand in the upcoming months. You created a model artifact that contained custom logic requiring third-party libraries. When you deployed the model, it failed to run because you did not include all the third-party dependencies in the model artifact. What file should be modified to include the missing libraries?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Specify third-party libraries for model deployment.
Understand Artifacts: runtime.yaml defines runtime; score.py handles logic.
Evaluate Options:
A: Not a standard file—incorrect.
B: Inference code—not for dependencies.
C: Defines conda env with dependencies—correct.
D: Pip list—not used in OCI conda deployments.
Reasoning: runtime.yaml points to a conda env with all libraries.
Conclusion: C is correct.
OCI documentation states: “In runtime.yaml, specify the conda environment slug (e.g., ENVIRONMENT_SLUG: custom_env) containing all third-party libraries required by the model.” score.py (B) is for logic, requirements.txt (D) isn’t OCI-standard, and A doesn’t exist—C fixes the issue.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".
NEW QUESTION # 112
Which step is unique to MLOps, as opposed to DevOps?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a step unique to MLOps vs. DevOps.
Compare MLOps and DevOps:
DevOps: Focuses on software deployment (CI/CD).
MLOps: Extends DevOps to ML, adding model-specific steps.
Evaluate Options:
A: Continuous deployment—Common to both (software/models).
B: Continuous integration—Common to both (code merging).
C: Continuous delivery—Common to both (releasing updates).
D: Continuous training—Unique to MLOps (retraining models with new data).
Reasoning: Only D addresses ML-specific needs (model retraining).
Conclusion: D is correct.
OCI documentation notes: “MLOps extends DevOps with continuous training, a process unique to machine learning where models are retrained with new data to maintain performance.” CI (B), CD (A), and delivery (C) are shared with DevOps—only continuous training (D) is MLOps-specific.
1: Oracle Cloud Infrastructure Data Science Documentation, "MLOps Concepts".
NEW QUESTION # 113
You want to write a program that performs document analysis tasks such as extracting text and tables from a document. Which Oracle AI service would you use?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Select an OCI AI service for text and table extraction from documents.
Evaluate Options:
A: Language—Text analysis, not extraction—incorrect.
B: Digital Assistant—Chatbots, not document tasks—incorrect.
C: Speech—Audio transcription, not documents—incorrect.
D: Vision—OCR for text/tables—correct.
Reasoning: Vision’s OCR extracts text and tables from document images.
Conclusion: D is correct.
OCI documentation states: “OCI Vision (D) uses OCR to extract text and tables from documents, supporting document analysis tasks.” A analyzes text post-extraction, B and C are unrelated—only D fits per OCI’s AI services.
1: Oracle Cloud Infrastructure Vision Documentation, "Document Analysis Features".
NEW QUESTION # 114
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