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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?
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'.
Which model has an open-source, open model format that allows you to run machine learning models on different platforms?
Detailed Answer in Step-by-Step Solution:
Objective: Identify an open model format for cross-platform ML model execution.
Evaluate Options:
A . PySpark: A big data framework, not a model format.
B . PyTorch: An ML framework with its own format, not inherently cross-platform without conversion.
C . TensorFlow: An ML framework with its SavedModel format, not universally open across platforms.
D . ONNX: Open Neural Network Exchange, an open-source format for model interoperability across frameworks.
Reasoning: ONNX is designed for portability (e.g., convert PyTorch to ONNX, run in TensorFlow), unlike framework-specific options.
Conclusion: D is the correct choice.
ONNX (D) is ''an open-source model format that enables interoperability between ML frameworks like PyTorch and TensorFlow,'' per OCI documentation. PySpark (A) is a processing tool, while PyTorch (B) and TensorFlow (C) are frameworks with native formats---only ONNX ensures cross-platform compatibility.
: Oracle Cloud Infrastructure Data Science Documentation, 'Supported Model Formats'.
Select two reasons why it is important to rotate encryption keys when using Oracle Cloud Infrastructure (OCI) Vault to store credentials or other secrets.
Detailed Answer in Step-by-Step Solution:
Objective: Identify two reasons for key rotation in OCI Vault.
Understand Key Rotation: Enhances security by updating keys.
Evaluate Options:
A: Five-key limit---False, no such restriction.
B: Efficiency---False, not the purpose.
C: Reuse---False, rotation prevents reuse.
D: Reduces risk---True, limits exposure---correct.
E: Limits data---True, reduces breach scope---correct.
Reasoning: D and E are security-focused---key Vault benefits.
Conclusion: D and E are correct.
OCI documentation states: ''Key rotation in Vault (D) reduces risk if a key is compromised and (E) limits the data encrypted by a single key version, enhancing security.'' A, B, and C misrepresent rotation's purpose---only D and E align with OCI's Vault best practices.
: Oracle Cloud Infrastructure Vault Documentation, 'Key Rotation Benefits'.
Which stage in the machine learning life cycle helps in identifying the imbalance present in the data?
Detailed Answer in Step-by-Step Solution:
Objective: Find the stage where data imbalance (e.g., skewed classes) is identified.
Understand Stages:
Data Modeling: Training models---assumes data is prepared.
Data Monitoring: Post-deployment tracking---not for initial analysis.
Data Exploration: Analyzing data properties (e.g., distributions)---key for imbalance.
Data Access: Retrieving data---no analysis yet.
Evaluate Options:
A: Modeling uses data, doesn't detect imbalance---incorrect.
B: Monitoring tracks performance, not initial data issues---incorrect.
C: Exploration (e.g., via pandas) reveals imbalances---correct.
D: Access is just retrieval---incorrect.
Reasoning: Imbalance is assessed during exploration (e.g., class counts).
Conclusion: C is correct.
OCI documentation notes: ''Data Exploration involves analyzing the dataset to understand its characteristics, such as identifying class imbalances or missing values, using tools like ADS SDK or Jupyter notebooks.'' Modeling (A) and Monitoring (B) occur later, while Access (D) is pre-analysis---only Exploration (C) fits this role.
: Oracle Cloud Infrastructure Data Science Documentation, 'Data Exploration Stage'.
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?
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.
: Oracle Cloud Infrastructure Anomaly Detection Documentation, 'Preparing Training Data' section.
