Which of these protects customer data at rest and in transit in a way that allows customers to meet their security and compliance requirements for cryptographic algorithms and key management?
Correct Answer: C
Detailed Answer in Step-by-Step Solution: * Objective: Identify protection for data at rest/transit with customer control. * Evaluate Options: * A: Controls-Broad, not specific to encryption. * B: Isolation-Separates tenants, not crypto-focused. * C: Encryption-Secures data, allows key management-correct. * D: Federation-Auth sharing, not data protection. * Reasoning: C provides crypto control (e.g., Vault keys). * Conclusion: C is correct. OCI documentation states: "Data encryption (C) protects data at rest and in transit, with customer-managed keys in OCI Vault meeting compliance needs." A and B are broader, D is unrelated-only C fits per OCI's security model. Oracle Cloud Infrastructure Security Documentation, "Data Encryption".
Question 32
You have created a model and want to use Accelerated Data Science (ADS) SDK to deploy the model. Where are the artifacts to deploy this model with ADS?
Correct Answer: C
Detailed Answer in Step-by-Step Solution: * Objective: Locate artifacts for ADS model deployment. * Understand ADS Deployment: Requires model artifacts (e.g., score.py) stored in OCI. * Evaluate Options: * A: Vault-Stores secrets, not models. * B: Depository-Not an OCI term. * C: Model Catalog-Stores models/artifacts for deployment-correct. * D: Artifactory-Not an OCI service. * Reasoning: Model Catalog is OCI's model repository for ADS. * Conclusion: C is correct. OCI documentation states: "ADS SDK deploys models from the Model Catalog, where trainedmodels and artifacts (e.g., score.py) are stored." Vault (A) is for secrets, B and D aren't real-only C supports ADS deployment. Oracle Cloud Infrastructure Data Science Documentation, "ADS Model Deployment".
Question 33
When preparing your model artifact to save it to the Oracle Cloud Infrastructure (OCI) DataScience model catalog, you create a score.py file. What is the purpose of the score.py file?
Correct Answer: B
Detailed Answer in Step-by-Step Solution: * Objective: Define the role of score.py in OCI model artifacts. * Understand Artifacts: score.py is key for deployment runtime. * Evaluate Options: * A: Infra config-Handled by OCI settings, not score.py. * B: Inference logic-Correct; runs load_model(), predict(). * C: Scaling-Set in deployment, not score.py. * D: Dependencies-In runtime.yaml, not score.py. * Reasoning: B aligns with score.py's execution role. * Conclusion: B is correct. OCI documentation states: "score.py (B) contains the inference logic, including functions to load the model and predict outputs, executed by the deployment endpoint." A, C, and D are managed elsewhere-only B matches OCI's design. Oracle Cloud Infrastructure Data Science Documentation, "Model Artifact - score.py".
Question 34
As a data scientist, you are tasked with creating a model training job that is expected to take different hyperparameter values on every run. What is the most efficient way to set those parameters with Oracle Data Science Jobs?
Correct Answer: D
Detailed Answer in Step-by-Step Solution: * Objective: Efficiently manage varying hyperparameters in OCI Data Science Jobs. * Understand OCI Jobs: Jobs execute predefined tasks with configurable inputs (e.g., env vars, args). * Evaluate Options: * A: New job per run with env vars-Redundant job creation, inefficient. * B: New job per run with args-Similarly inefficient due to repeated setup. * C: Hardcode params, new job per change-Highly inefficient, requires code edits. * D: Single job, flexible params via env vars or args-Efficient, reusable-correct. * Reasoning: D minimizes job creation, allows runtime flexibility via configuration-best practice. * Conclusion: D is correct. OCI documentation states: "For Jobs with varying hyperparameters, write code to accept environment variables or command-line arguments (D), then configure these per Job Run using the OCI Console or SDK- most efficient approach." Options A, B, and C involve unnecessary job proliferation or code changes-only D aligns with OCI's design for parameterized runs. Oracle Cloud Infrastructure Data Science Documentation, "Configuring Job Runs with Parameters".
Question 35
You are building a model and need input that represents data as morning, afternoon, or evening. However, the data contains a timestamp. What part of the Data Science lifecycle would you be in when creating the new variable?
Correct Answer: D
Detailed Answer in Step-by-Step Solution: * Objective: Locate the lifecycle stage for transforming timestamps into categories. * Understand Lifecycle: * Model Type Selection: Choosing algorithms-post-data prep. * Model Validation: Evaluating performance-post-training. * Data Access: Retrieving raw data-pre-transformation. * Feature Engineering: Creating new features-correct. * Evaluate Options: * A: Too late-incorrect. * B: Post-model-incorrect. * C: Pre-transformation-incorrect. * D: Feature creation-correct. * Reasoning: Converting timestamps to time periods is feature engineering. * Conclusion: D is correct. OCI documentation states: "Feature engineering (D) transforms raw data, such as timestamps into categorical variables (e.g., morning/afternoon), to enhance model inputs." A, B, and C occur at different stages-only D fits OCI's lifecycle for this task. Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Stage".
