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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement End-to-End Machine Learning Lifecycle | 45% | - Model saving, cataloging, and versioning - Data preparation, exploration, and transformation - Model development, training, and evaluation - Use AutoML and built-in algorithms - Deploy models and manage endpoints |
| Topic 2: Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| Topic 3: 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 4: 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 |
| Topic 5: Apply MLOps Practices | 20% | - Model monitoring, drift detection, and performance tracking - ML pipelines, automation, and reproducibility - Governance, auditing, and compliance |
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NEW QUESTION # 130
Which of the following best describes the principal goal of data science?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Define data science’s main goal.
Evaluate Options:
A: Archiving—Not the focus; too narrow.
B: Analyze for insights/business value—Core purpose—correct.
C: Prep for analytics—Means, not the end goal.
D: Output-focused—Vague, incomplete.
Reasoning: B captures the actionable insight generation central to data science.
Conclusion: B is correct.
OCI documentation defines data science as “mining and analyzing large datasets to uncoveractionable insights for operational improvements and business value.” A is storage-focused, C is preparatory, and D is unclear—only B reflects the principal goal per OCI’s mission.
1: Oracle Cloud Infrastructure Data Science Documentation, "What is Data Science?".
NEW QUESTION # 131
Arrange the following in the correct Git Repository workflow order:
Install, configure, and authenticate Git.
Configure SSH keys for the Git repository.
Create a local and remote Git repository.
Commit files to the local Git repository.
Push the commit to the remote Git repository.
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Step 1: Install, configure, and authenticate Git: Git must be installed and configured (e.g., git config --global user.name) before any repository actions.
Step 2: Configure SSH keys: SSH keys are set up (e.g., ssh-keygen) and added to the remote service (e.g., GitHub, OCI Code Repository) for secure access.
Step 3: Create local and remote Git repository: Initialize a local repo (git init) and create/link a remote repo (e.g., git remote add origin).
Step 4: Commit files: Add files (git add .) and commit them locally (git commit -m "message").
Step 5: Push to remote: Push local commits to the remote repo (git push origin main).
Evaluate Options: Only D (1, 2, 3, 4, 5) follows this logical sequence; others (e.g., A starts with SSH before Git installation) are illogical.
The standard Git workflow in OCI Data Science or general practice begins with installing Git (1), configuring SSH for secure access (2), creating repositories (3), committing locally (4), and pushing remotely (5). The OCI Code Repository documentation aligns with this: “First, install Git and configure authentication (e.g., SSH), then set up repositories and manage code.” D is the only option reflecting this industry-standard process.
1: Oracle Cloud Infrastructure Code Repository Documentation, "Git Workflow Basics".
NEW QUESTION # 132
Where do calls to stdout and stderr from score.py go in a model deployment?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Locate score.py output in OCI model deployment.
Understand Deployment: Logs are centralized in OCI Logging.
Evaluate Options:
A: VM file—Not default; requires custom config—incorrect.
B: Predict log in OCI Logging—Standard destination—correct.
C: Cloud Shell—Separate tool, not logs—incorrect.
D: Console—UI, not raw logs—incorrect.
Reasoning: B aligns with OCI’s logging integration.
Conclusion: B is correct.
OCI documentation states: “score.py stdout and stderr are captured in the predict log within OCI Logging service (B), configured during deployment.” A isn’t standard, C and D don’t receive logs—only B fits OCI’s logging setup.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Logging".
NEW QUESTION # 133
You are creating an Oracle Cloud Infrastructure (OCI) Data Science job that will run on a recurring basis in a production environment. This job will pick up sensitive data from an Object Storage Bucket, train a model, and save it to the model catalog. How would you design the authentication mechanism for the job?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Securely authenticate a recurring OCI Job.
Evaluate Options:
A: PAR—Limited duration, insecure for recurring jobs.
B: Resource principal—Secure, managed auth for Jobs—correct.
C: Personal config—Unscalable, security risk.
D: Vault with personal keys—Complex, still uses user creds.
Reasoning: B uses OCI’s native, secure resource principal mechanism.
Conclusion: B is correct.
OCI documentation states: “For Jobs accessing sensitive data, use resource principals with a dynamic group (e.g., resource.type = 'datasciencejobrun') and policies granting access to Object Storage and Model Catalog—secure and scalable.” A is temporary, C and D risk credential exposure—B is best practice.
1: Oracle Cloud Infrastructure Data Science Documentation, "Job Authentication".
NEW QUESTION # 134
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which TWO of the following prevailing metrics would you use for evaluating a multiclass classification model?
Answer: C,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand Multiclass Classification: Metrics evaluate how well the model predicts multiple classes.
Evaluate Metrics:
A . Mean squared error: Used for regression, not classification.
B . Explained variance score: Regression metric, not suitable.
C . Recall: Measures true positive rate per class—key for classification.
D . F1-score: Balances precision and recall—widely used in multiclass.
E . R-squared: Regression metric, not applicable.
Select Two: Recall (C) and F1-score (D) are standard for multiclass classification.
Oracle AutoML supports metrics like recall and F1-score for multiclass classification, as they assess per-class performance and overall precision-recall balance, respectively. Regression metrics (A, B,E) are irrelevant here. (Oracle Cloud Infrastructure Data Science Documentation, "AutoML Metrics").
NEW QUESTION # 135
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