Question 1

You’re going to create an Oracle Cloud Infrastructure Anomaly Detection model for multivariate data. Where do you need to store the training data?

Accepted Answer

D

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. : Oracle Cloud Infrastructure Anomaly Detection Documentation, "Preparing Training Data" section.

Question 2

What must be configured in OCI for a user to create Data Science resources via the SDK?

Accepted Answer

B

Explanation: 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.

Question 3

You want to create a reproducible environment for a notebook session. What should you do?

Accepted Answer

C

Explanation: 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.

Question 4

Which formats can be used to deploy LangChain applications on OCI Data Science? (Choose two)

Accepted Answer

A, C

Explanation: 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.

Question 5

What are key functions offered by the ADS CLI (Command Line Interface)? (Choose two)

Accepted Answer

B, C

Explanation: 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.

Question 6

You notice that several features have over 80% missing values. What should you consider doing?

Accepted Answer

B

Explanation: Features with a high proportion of missing values may contribute little to model performance and can be dropped or imputed to maintain data integrity.

Question 7

Which ADS feature allows for automated summarization of dataset metrics?

Accepted Answer

C

Explanation: DataFrameProfile in ADS generates a comprehensive summary including types, missing values, distributions, and cardinality, providing insight into dataset structure and quality.

Question 8

How does the ADS Explainer class assist data scientists?

Accepted Answer

C

Explanation: 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.

Question 9

What action must you take to push changes from a notebook session to a Git repository?

Accepted Answer

C

Explanation: 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.

Question 10

After storing a GitHub token in OCI Vault, what is the proper way to use it in a notebook session to clone a repo?

Accepted Answer

A

Explanation: 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.

Question 11

What is a common input format for AI Quick Actions in OCI?

Accepted Answer

C

Explanation: AI Quick Actions generally accept plain text or JSON inputs, depending on the type of task. For example, summarization expects textual input, while entity extraction may require structured formats.

Question 12

Which step is typically performed first in the machine learning workflow?

Accepted Answer

D

Explanation: Data exploration is the first critical step in the ML workflow to understand the structure, quality, and distribution of data, guiding downstream processing and modeling.

Question 13

Which two types of resources are associated with creating an OCI Data Science Job? (Choose two)

Accepted Answer

A, C

Explanation: A Job refers to the reusable definition (script, environment, etc.), while a Job Run is a specific execution of the job. Job artifacts hold the code or model to execute.

Question 14

Which of the following are required when creating a notebook session? (Choose two)

Accepted Answer

A, B

Explanation: To create a notebook session, you must specify the compartment and the shape (compute configuration) for the session. Load balancers and NAT gateways are not required components.

Question 15

Which two explanation types are supported in ADS? (Choose two)

Accepted Answer

A, C

Explanation: ADS supports both global explanations, which describe the overall model behavior, and local explanations, which focus on individual prediction cases. These help in trust-building and regulatory compliance.

Free Oracle 1Z0-1110-25 Actual Exam Questions