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| Section | Weight | Objectives |
|---|---|---|
| Apply MLOps Practices | 20% | - Model monitoring, drift detection, and performance tracking - ML pipelines, automation, and reproducibility - Governance, auditing, and compliance |
| Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| Implement End-to-End Machine Learning Lifecycle | 45% | - Model development, training, and evaluation - Use AutoML and built-in algorithms - Model saving, cataloging, and versioning - Data preparation, exploration, and transformation - Deploy models and manage endpoints |
| OCI Data Science - Introduction & Configuration | 10% | - Tenancy and environment configuration for Data Science - Capabilities of the Accelerated Data Science (ADS) SDK - Overview and core concepts of OCI Data Science |
| Design and Set Up Data Science Workspace | 15% | - Create and manage projects and notebook sessions - Configure compute shapes, storage, and networking - Manage access control, security, and IAM integration |
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NEW QUESTION # 121
After you have created and opened a notebook session, you want to use the Accelerated Data Science (ADS) SDK to access your data and get started with exploratory data analysis. From which TWO places can you access the ADS SDK?
Answer: A,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Locate sources for ADS SDK in OCI.
Understand ADS SDK: A Python library for Data Science tasks (e.g., EDA).
Evaluate Options:
A: Big Data Service—Spark-focused, not ADS source.
B: Machine Learning—Separate service, not ADS-related.
C: Conda in OCI Data Science—Preinstalled ADS in notebook sessions.
D: PyPI—Public source to install ADS (pip install oracle-ads).
E: ADW—Database, not an SDK source.
Reasoning: C (preinstalled) and D (installable) are practical access points.
Conclusion: C and D are correct.
OCI documentation states: “The ADS SDK is available in OCI Data Science notebook sessions via preinstalled conda environments (C) and can be installed from PyPI (D) using pip install oracle-ads.” Big Data (A), Machine Learning (B), and ADW (E) don’t host ADS—only C and D apply.
1: Oracle Cloud Infrastructure Data Science Documentation, "ADS SDK Installation".
NEW QUESTION # 122
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: D
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 # 123
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which of the following TWO prevailing metrics would you use for evaluating a multiclass classification model?
Answer: A,B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Select two metrics for multiclass classification in AutoML.
Understand Multiclass Metrics: Focus on class-specific performance—classification, not regression.
Evaluate Options:
A . Recall: Measures true positives per class—key for multiclass—correct.
B . Mean squared error: Regression metric—incorrect.
C . F1 Score: Balances precision and recall—standard for multiclass—correct.
D . R-Squared: Regression fit—incorrect.
E . Explained variance: Regression metric—incorrect.
Reasoning: A and C assess classification accuracy across multiple classes—fit AutoML’s evaluation.
Conclusion: A and C are correct.
OCI AutoML documentation states: “For multiclass classification, common evaluation metrics include recall (A) for per-class sensitivity and F1 Score (C) for balanced performance.” B, D, and E are regression-focused—only A and C are supported and relevant per OCI’s AutoML metrics suite.
1: Oracle Cloud Infrastructure AutoML Documentation, "Evaluation Metrics for Classification".
NEW QUESTION # 124
You’re going to create an Oracle Cloud Infrastructure Anomaly Detection model for multivariate dat a. Where do you need to store the training data?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand OCI Anomaly Detection: This service trains models to detect anomalies in multivariate data (e.g., sensor readings), requiring data to be accessible within OCI’s ecosystem.
Assess Storage Requirements: The training data must be in a scalable, OCI-compatible location that the Anomaly Detection service can access programmatically.
Evaluate Options:
A . Your local machine: Data on a local machine isn’t directly accessible to OCI services without upload, making it impractical for cloud-based training.
B . MySQL database: While OCI supports MySQL, Anomaly Detection doesn’t natively integrate with it for training data; it prefers file-based input.
C . Autonomous Data Warehouse: This is a database for analytics, not the default storage for Anomaly Detection training data, which expects CSV/JSON files.
D . Object Storage Bucket: OCI Object Storage is a scalable, durable storage service that Anomaly Detection uses to ingest training data (e.g., CSV files).
Reasoning: Object Storage is the standard for large-scale data in OCI services, offering seamless integration with Anomaly Detection via APIs or SDKs.
Conclusion: D is the correct choice as it aligns with the service’s architecture.
The OCI Anomaly Detection service requires training data to be uploaded to an Object Storage Bucket in formats like CSV or JSON. This is explicitly outlined in the official documentation, which states that users must “upload the training dataset to an OCI Object Storage bucket” before creating a data asset for model training. Local storage (A) isn’t viable for cloud processing, and databases like MySQL (B) or Autonomous Data Warehouse (C) aren’t supported as primary inputs. Object Storage (D) provides the scalability and accessibility needed for multivariate anomaly detection workflows.
1: Oracle Cloud Infrastructure Anomaly Detection Documentation, "Preparing Training Data" section.
NEW QUESTION # 125
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 # 126
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