1Z0-1110-25 Practice Questions

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.

To use the SDK, the user must be in a dynamic group with policies that allow interacting with Data

Science resources. This enables programmatic resource creation and management.

Exporting a Conda environment to a YAML file allows reproducibility by defining all dependencies and

versions. This file can be reused to replicate the environment.

LangChain apps can be containerized or built using Python web frameworks like Flask or FastAPI. These

are deployed to OCI Data Science as custom scripts or container-based deployments.

The ADS CLI lets you interact with OCI Data Science resources like notebook sessions and pipelines

through terminal commands. It provides a flexible alternative to the GUI.

Features with a high proportion of missing values may contribute little to model performance and can be

dropped or imputed to maintain data integrity.

DataFrameProfile in ADS generates a comprehensive summary including types, missing values,

distributions, and cardinality, providing insight into dataset structure and quality.

The Explainer class in ADS SDK enables generation of visual and tabular explanations for model outputs.

It supports SHAP-based techniques and helps both local and global interpretability.

You can use Git commands in the terminal within notebook sessions to commit and push changes. This

requires the session to be authenticated using HTTPS token or SSH key and the Git environment

configured properly.

Once retrieved and decoded from OCI Vault, the token can be passed to Git using an HTTPS format like

https://@github.com/. This avoids manual credential entry and maintains security.