Important Oracle 1Z0-1110-25 Exam Questions

Answer: D

Explanation:

Detailed Answer in Step-by-Step Solution:

Objective: Configure conda env for a pipeline step.

Evaluate Options:

A: Shape---Infra, not env config.

B: Volume---Storage, not env.

C: Command-line---Step args, not env.

D: Env variables---Sets conda path---correct.

Reasoning: D specifies runtime env (e.g., CONDA_ENV_SLUG).

Conclusion: D is correct.

OCI documentation states: ''Configure a pipeline step's conda environment using environment variables (D), such as CONDA_ENV_SLUG, in the step definition.'' A, B, and C address other aspects---only D fits env config.

: Oracle Cloud Infrastructure Data Science Documentation, 'Pipeline Step Configuration'.

Answer: D

Explanation:

Detailed Answer in Step-by-Step Solution:

Step 1: Install, configure, and authenticate Git: Git must be installed and configured (e.g., git config --global user.name) before any repository actions.

Step 2: Configure SSH keys: SSH keys are set up (e.g., ssh-keygen) and added to the remote service (e.g., GitHub, OCI Code Repository) for secure access.

Step 3: Create local and remote Git repository: Initialize a local repo (git init) and create/link a remote repo (e.g., git remote add origin).

Step 4: Commit files: Add files (git add .) and commit them locally (git commit -m 'message').

Step 5: Push to remote: Push local commits to the remote repo (git push origin main).

Evaluate Options: Only D (1, 2, 3, 4, 5) follows this logical sequence; others (e.g., A starts with SSH before Git installation) are illogical.

The standard Git workflow in OCI Data Science or general practice begins with installing Git (1), configuring SSH for secure access (2), creating repositories (3), committing locally (4), and pushing remotely (5). The OCI Code Repository documentation aligns with this: ''First, install Git and configure authentication (e.g., SSH), then set up repositories and manage code.'' D is the only option reflecting this industry-standard process.

: Oracle Cloud Infrastructure Code Repository Documentation, 'Git Workflow Basics'.

Answer: C

Explanation:

Detailed Answer in Step-by-Step Solution:

Objective: Identify a true statement about OCI Jobs logging.

Understand Logging: Jobs can log stdout/stderr to OCI Logging service.

Evaluate Options:

A: False---Each run has its own log, not a single job log.

B: False---Logging is optional, not mandatory.

C: True---When enabled, stdout/stderr are auto-captured.

D: False---Logs persist unless explicitly deleted.

Reasoning: C matches OCI's automatic logging feature.

Conclusion: C is correct.

OCI documentation states: ''When automatic log creation is enabled for Data Science Jobs, all stdout and stderr outputs are captured and stored in the OCI Logging service.'' A is incorrect (per-run logs), B is optional, and D contradicts log retention---only C is accurate.

: Oracle Cloud Infrastructure Data Science Documentation, 'Jobs Logging'.

Answer: D

Explanation:

Detailed Answer in Step-by-Step Solution:

Define Resource Principals: They allow OCI resources (e.g., notebook sessions) to authenticate to other OCI services without user credentials.

Evaluate Options:

A: False---Resource principals eliminate manual credential management.

B: False---They're secure, leveraging IAM policies, not less secure than API keys.

C: False---Data Science supports resource principals for accessing resources (e.g., Object Storage).

D: True---Resource principals are an IAM feature authorizing resources as actors.

Reasoning: D captures the essence of resource principals as an IAM mechanism.

Conclusion: D is correct.

OCI documentation states: ''A resource principal is an IAM feature that enables OCI resources, such as compute instances or notebook sessions, to act as principal actors and authenticate to other OCI services using policies.'' This refutes A (no credentials needed), B (secure method), and C (supported in Data Science), making D the accurate statement.

: Oracle Cloud Infrastructure IAM Documentation, 'Resource Principals'.

Answer: A

Explanation:

Detailed Answer in Step-by-Step Solution:

Objective: Identify a feature engineering technique in ML.

Understand Feature Engineering: Transforms raw data into model-ready features.

Evaluate Options:

A . PCA: Reduces dimensionality---feature engineering---correct.

B . K-means: Clustering model---not feature engineering.

C . SVM: Classification model---not feature engineering.

D . Gradient boosting: Model training---not feature engineering.

Reasoning: PCA creates new features via transformation---fits definition.

Conclusion: A is correct.

OCI documentation states: ''Feature engineering techniques like Principal Component Analysis (PCA) (A) transform data into new features to enhance model performance.'' B, C, and D are modeling techniques---only A aligns with OCI's feature engineering stage.

: Oracle Cloud Infrastructure Data Science Documentation, 'Feature Engineering Techniques'.