Detailed Answer in Step-by-Step Solution:
* Objective: Select an OCI AI service to sort images by language.
* Evaluate Options:
* A: Digital Assistant-Chatbots, not image/language processing.
* B: Vision-Image analysis (e.g., object detection), not language sorting.
* C: Speech-Audio-to-text, not image-based.
* D: Language-Text analysis (e.g., language detection) after OCR-correct.
* Reasoning: Images need OCR (Vision) then language detection (Language)-D fits the sorting task.
* Conclusion: D is correct.
OCI Language "detects and classifies languages in text," often paired with OCI Vision's OCR to process document images. Vision (B) extracts text, but Language (D) sorts by language-Digital Assistant (A) and Speech (C) don't apply. Documentation supports this workflow.
Oracle Cloud Infrastructure Language Documentation, "Language Detection".

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.

Detailed Answer in Step-by-Step Solution:
* Objective: Select an explanation technique for feature-target relationships with correlated features.
* Evaluate Options:
* A: Permutation-Breaks with high correlation.
* B: LIME-Local, not global relationships.
* C: Dependence-Not a standard term; vague.
* D: ALE-Handles correlation, shows feature effects-correct.
* Reasoning: ALE is robust to correlated features, ideal here.
* Conclusion: D is correct.
OCI documentation states: "Accumulated Local Effects (ALE) (D) evaluates feature-target relationships, accounting for correlations, unlike permutation importance (A) which falters with high correlation." B is local, C isn't defined-only D fits per OCI's explanation tools.
Oracle Cloud Infrastructure Data Science Documentation, "Model Explanation Techniques".

Detailed Answer in Step-by-Step Solution:
* Objective: Identify the Oracle service to sort document images by language.
* Task Breakdown: Requires extracting text from images (OCR) and detecting language-two potential services involved.
* Evaluate Options:
* A. Oracle Digital Assistant: Builds chatbots-irrelevant to image or language processing.
* B. OCI Language: Detects and classifies languages in text-ideal for sorting after text extraction.
* C. OCI Speech: Transcribes audio to text-not applicable to images.
* D. OCI Vision: Performs OCR to extract text from images-necessary but not sufficient for language sorting.
* Reasoning: The task emphasizes "sorting by language." OCI Vision extracts text, but OCI Language identifies the language (e.g., English, Spanish). Since the question asks for one service and focuses on sorting, OCI Language (B) is the best fit, assuming text extraction is a precursor step.
* Conclusion: B is correct.
OCI Language "provides language detection and classification capabilities, enabling identification of languages in text extracted from documents," per the documentation. OCI Vision handles OCR, but the sorting task aligns with OCI Language (B). Digital Assistant (A) and Speech (C) don't apply, and while Vision (D) is a prerequisite, B is the primary service for language sorting as per OCI's AI service design.
Oracle Cloud Infrastructure Language Documentation, "Language Detection Features".

Detailed Answer in Step-by-Step Solution:
* Objective: Identify a step in OCI's AutoML pipeline.
* Understand AutoML: Automates model building-includes preprocessing, selection, and tuning.
* Evaluate Options:
* A: Feature Extraction (e.g., PCA) isn't explicitly part of OCI AutoML-too specific.
* B: Saving to Model Catalog is post-AutoML, not a pipeline step.
* C: Deployment is a separate action after AutoML-incorrect.
* D: Feature Selection (e.g., choosing relevant features) is a core AutoML step-correct.
* Reasoning: OCI AutoML automates feature selection, algorithm choice, and tuning-D fits.
* Conclusion: D is correct.
OCI AutoML's pipeline includes "feature selection, algorithm selection, adaptive sampling, and hyperparameter tuning," per the documentation. Extraction (A) isn't highlighted, while saving (B) and deployment (C) are post-process actions-only Feature Selection (D) is an integral automated step.
Oracle Cloud Infrastructure Data Science Documentation, "AutoML Pipeline".