Free 1Z0-1110-25 Practice Test Questions and Answers (2026)

Detailed Answer in Step-by-Step Solution:

Objective: Sequence OCI AutoML pipeline stages. Stages:

Adaptive sampling: Reduces data size if large. Feature selection: Picks relevant features.

Algorithm selection: Chooses best model type. Hyperparameter tuning: Optimizes model params.

Evaluate: C (sampling, features, algorithms, tuning) matches logical flow—data first, then model.

Reasoning: Sampling precedes feature work—standard in OCI.

Conclusion: C is correct.

OCI documentation states: “AutoML pipeline runs 1) adaptive sampling, 2) feature selection, 3)

algorithm selection, 4) hyperparameter tuning (C).” Sampling reduces data first, then features and models

are optimized—other orders (A, B, D) misalign with OCI’s sequence.

: Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Sequence".

OCI Logging integrates with Data Science jobs and model deployments to capture logs, errors, and

runtime events, enabling observability and debugging of MLOps workflows.

Detailed Answer in Step-by-Step Solution:

Objective: Efficiently increase storage for an OCI Job.

Understand Jobs: Storage (block volume) is set at job creation, not dynamically adjustable. Evaluate

Options:

A: False—Jobs can’t edit storage post-creation; it’s fixed. B: False—No environment variable adjusts

storage size.

C: True—Create a new job with larger storage (e.g., 200 GB) and run it. D: False—Refactoring code is

inefficient compared to increasing storage. Reasoning: C is the standard OCI process for adjusting

resources.

Conclusion: C is correct.

OCI documentation states: “Storage size for a Data Science Job is specified during job creation (e.g.,

block volume size). To increase it, create a new job with a larger storage configuration and initiate a new

run.” Editing (A) isn’t supported, variables (B) don’t apply, and refactoring (D) avoids the issue— only C

is efficient.

: Oracle Cloud Infrastructure Data Science Documentation, "Jobs - Storage Configuration".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the interactive coding environment in OCI Data Science. Evaluate Options:

A: Model Catalog stores models—not for coding. B: Jobs run predefined tasks—not interactive.

C: Notebook Sessions provide JupyterLab for coding and training—interactive. D: Projects organize

work—not a coding environment.

Reasoning: Notebook Sessions are OCI’s Jupyter-based tool for interactive ML development.

Conclusion: C is correct.

OCI Data Science Notebook Sessions “provide an interactive JupyterLab environment where data

scientists can write code, explore data, and train machine learning models.” Model Catalog (A) is for

storage, Jobs (B) for automation, and Projects (D) for organization—only C offers interactivity.

: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Sessions Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Address declining model performance and improve churn prediction.

Analyze Issue: Poor predictions suggest data drift or model staleness—common ML challenges.

Evaluate Options:

A . Drift monitoring: Tracks changes in data distribution—identifies root cause of accuracy drop—

correct.

B . Redeploy the model: Repeats deployment without fixing the issue—ineffective alone.

C . Operational monitoring: Tracks system health (e.g., latency), not prediction quality.

D . Retrain the model: Updates model with new data—directly improves accuracy—correct.

E . Validate with recent data: Checks performance but doesn’t fix—diagnostic, not corrective. Reasoning:

A diagnoses drift (cause), D retrains to adapt (solution)—best combo to verify and lower churn.

Conclusion: A and D are correct.

OCI documentation advises: “Drift monitoring (A) detects shifts in data distribution that degrade model

performance, while retraining (D) with fresh data restores accuracy.” Redeployment (B) doesn’t address

drift, operational monitoring (C) focuses on infra, and validation (E) is a check—not a fix. A and D align

with OCI’s model maintenance strategy.

: Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".

Detailed Answer in Step-by-Step Solution:

Define Feature Engineering: It’s the process of creating or modifying features to improve model

performance.

Evaluate Options:

A: Parameter tuning adjusts model hyperparameters (e.g., learning rate), not features. B: Model

interpretation (e.g., SHAP values) explains predictions, not feature creation.

C: Transforming features (e.g., normalizing, encoding) is the core of feature engineering—correct. D:

Understanding features occurs during exploration, not engineering.

Reasoning: Feature engineering directly manipulates data inputs (e.g., converting timestamps to day- ofweek), distinct from tuning or interpretation.

OCI Data Science documentation defines feature engineering as “the process of transforming raw data

into features that better represent the underlying problem to the predictive models, resulting in improved

model accuracy.” Examples include scaling or creating interaction terms, aligning with C. Other options

(A, B, D) relate to different ML stages.

: Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the initial data science step.

Define Process: Starts with problem definition, then data and modeling. Evaluate Options:

A: Data collection—Second step after problem definition. B: Modeling—Later stage.

C: Hypothesis—Sets the goal, first step—correct. D: Data owners—Collaboration, not the start.

Reasoning: Hypothesis drives the process (e.g., “Can we predict churn?”). Conclusion: C is correct.

OCI documentation states: “The data science process begins with defining an analytical hypothesis to

address a business problem, followed by data collection and analysis.” C precedes A, B, and D— aligning

with OCI’s structured approach.

: Oracle Cloud Infrastructure Data Science Documentation, "Data Science Process".

Detailed Answer in Step-by-Step Solution:

Objective: Find a true statement about OCI Anomaly Detection.

Understand Service: Detects anomalies in multivariate data (e.g., time series). Evaluate Options:

A: False—Accepted types are CSV/JSON, not SQL/Python.

B: Partially true—Focuses on numerical data (e.g., sensors), not text broadly. C: True—Used for fraud,

intrusions, and sensor anomalies (key use cases).

D: False—Trained on customer data only, not general datasets. Reasoning: C aligns with documented

applications; others misalign. Conclusion: C is correct.

OCI Anomaly Detection documentation states: “The service is designed to detect anomalies in time series

data, making it valuable for fraud detection, network intrusion analysis, and sensor discrepancies.” A is

incorrect (file formats), B overgeneralizes (numerical focus), and D misstates training data—only C

matches the service’s purpose.

: Oracle Cloud Infrastructure Anomaly Detection Documentation, "Use Cases".

Detailed Answer in Step-by-Step Solution:

Objective: Determine where to specify the conda environment for an OCI model deployment. Understand

Model Deployment: Requires artifacts like score.py and runtime.yaml to define runtime settings.

Evaluate Options:

A . score.py: Contains inference logic (e.g., load_model(), predict())—not for environment specs.

B . runtime.yaml: Defines deployment runtime, including conda environment path—correct.

C . requirements.txt: Lists pip dependencies—not used in OCI for conda environments.

D . model_artifact_validate.py: Not a standard artifact; doesn’t exist in OCI deployment.

Reasoning: runtime.yaml specifies the conda env (e.g., slug: pyspark30_p37_cpu_v2)—failure to set

this causes deployment errors. Conclusion: B is correct.

OCI documentation states: “The runtime.yaml file in a model artifact specifies the runtime environment,

including the conda environment path (e.g., ENVIRONMENT_SLUG: pyspark30_p37_cpu_v2),

ensuring the deployed model uses the correct dependencies.” score.py (A) handles inference,

requirements.txt (C) is for pip (not conda in OCI), and D isn’t valid—only B addresses the conda issue

per OCI’s deployment process.

: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".

OCI Vault is used to securely store and manage sensitive data such as passwords, API tokens, and

encryption keys. It enables controlled access and integrates with other OCI services like Data Science to

ensure credentials are not hardcoded into notebooks.

Detailed Answer in Step-by-Step Solution:

Objective: Identify correct statements about installing Python libraries in OCI Data Science. Understand

Notebook Sessions: Run in a managed environment with specific permissions. Evaluate Options:

A: False—No root privileges; users operate as datascience with limited sudo. B: True—pip install from

PyPI works with internet access (e.g., NAT Gateway). C: False—Yum isn’t available; pip is the primary

tool as a normal user.

D: False—Misstated; youcaninstall non-preinstalled libraries—likely a typo (intended opposite). E:

True—Custom repos are supported with proper network config.

Correct Interpretation: Assuming D’s intent was “Youcaninstall…” (common exam error), B, D

(corrected), E are true.

Conclusion: B, D (corrected), E are correct.

OCI documentation states: “In notebook sessions, you can install Python libraries from PyPI (B) or

private repositories (E) using pip, but root privileges (A) are not granted—users operate asdatascience.”

Yum (C) isn’t supported, and D’s phrasing contradicts capability—corrected, it’s true you can install

beyond preinstalled. B, D (adjusted), E align with OCI’s flexibility.

: Oracle Cloud Infrastructure Data Science Documentation, "Installing Libraries in Notebook Sessions".

Detailed Answer in Step-by-Step Solution:

Understand Published Conda Environments: In OCI Data Science, these are custom conda environments

shared across users via Object Storage.

Evaluate Options:

A: Vague—All conda environments can address use cases; not unique to “published.” B:

Incorrect—Availability on reactivation applies to session persistence, not publishing. C:

Correct—Publishing saves the environment to Object Storage for sharing/reuse.

D: Incorrect—Block volumes store session data, not published environments.

Reasoning: The unique aspect of “published” environments is their storage in Object Storage (via odsc

conda publish), enabling team access.

Conclusion: C is the distinctive feature.

The OCI Data Science documentation highlights that “published conda environments are saved to an OCI

Object Storage Bucket, allowing them to be shared across notebook sessions and users.” This

distinguishes C from A (generic), B (session-related), and D (block volume is for session state, not

publishing). Publishing to Object Storage is the defining trait per Oracle’s design.

: Oracle Cloud Infrastructure Data Science Documentation, "Managing Conda Environments Publishing" section.

What is a conda environment?

A. A system that manages package dependencies

B. A collection of kernels

C. An open-source environment management system

D. An environment deployment system on Oracle AI

Answer: C

Explanation:

Detailed Answer in Step-by-Step Solution:

Define Conda: Conda is a widely used tool for managing packages and environments in data science.

Evaluate Options:

A: Partially true—Conda manages dependencies, but it’s broader (an environment system). B:

Incorrect—Kernels (e.g., Jupyter) are separate; Conda manages environments.

C: Correct—Conda is an open-source tool for creating isolated environments with specific packages. D:

Incorrect—Not specific to Oracle AI; it’s a general tool.

Reasoning: C captures Conda’s full scope as an open-source system, beyond just dependency

management (A).

Conclusion: C is the most accurate.

OCI documentation describes Conda as “an open-source package and environment management system

that allows data scientists to create isolated environments with specific versions of Python and libraries.”

A is too narrow, B misaligns with kernel concepts, and D ties it incorrectly to Oracle AI. C aligns with

Conda’s official definition and OCI’s usage.

: Oracle Cloud Infrastructure Data Science Documentation, "Conda Environments Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Securely authenticate a recurring OCI Job. Evaluate Options:

A: PAR—Limited duration, insecure for recurring jobs.

B: Resource principal—Secure, managed auth for Jobs—correct. C: Personal config—Unscalable,

security risk.

D: Vault with personal keys—Complex, still uses user creds.

Reasoning: B uses OCI’s native, secure resource principal mechanism. Conclusion: B is correct.

OCI documentation states: “For Jobs accessing sensitive data, use resource principals with a dynamic

group (e.g., resource.type = 'datasciencejobrun') and policies granting access to Object Storage and Model

Catalog—secure and scalable.” A is temporary, C and D risk credential exposure—B is best practice.

: Oracle Cloud Infrastructure Data Science Documentation, "Job Authentication".

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.

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".

OCI compartments enforce isolation through IAM policies. Without explicit permissions, users in other

compartments cannot access a notebook session.

Detailed Answer in Step-by-Step Solution:

Objective: Differentiate data scientist vs. data engineer roles. Define Roles:

Data Engineer: Builds pipelines, prepares data. Data Scientist: Analyzes data, builds models.

Evaluate Options:

A: Engineer preps, scientist analyzes—Correct division. B: Reverses roles—Incorrect.

C: Overlaps roles—Scientist doesn’t typically build pipelines. D: Misaligns—Analyst isn’t the focus.

Reasoning: A reflects standard role separation. Conclusion: A is correct.

OCI documentation notes: “Data engineers focus on collecting and preparing data through pipelines,

while data scientists analyze it to derive insights and build models.” A aligns, B inverts, C

overcomplicates, and D shifts focus—only A is accurate.

: Oracle Cloud Infrastructure Data Science Documentation, "Roles in Data Science".

ROC-AUC is a common metric used in binary classification to measure the model’s ability to distinguish

between positive and negative classes across various thresholds.

Detailed Answer in Step-by-Step Solution:

Objective: Scale up a notebook session without losing work.

Understand Persistence: Block volume stores session data (e.g., /home/datascience). Evaluate Options:

A: Recreating work—inefficient, risks loss.

B: Local download/upload—cumbersome, unnecessary.

C: Use block volume persistence, scale up—efficient, preserves work—correct. D: Object Storage—extra

steps, not needed with block volume.

Reasoning: C leverages OCI’s built-in persistence for seamless scaling. Conclusion: C is correct.

OCI documentation states: “Files in /home/datascience are stored on the block volume. To scale up,

deactivate the session, provision a new one with a larger shape, and the block volume persists your

work.” A loses data, B and D add complexity—only C is optimal.

: Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".

Detailed Answer in Step-by-Step Solution:

Objective: Assess required skills for data scientists. Analyze Skills:

Technical: Coding, stats, ML.

Nontechnical: Communication, business acumen. Traits: Curiosity, problem-solving.

Reasoning: Success requires this mix—e.g., explaining models to stakeholders. Conclusion: A (True) is

correct.

OCI documentation states: “Effective data scientists combine technical skills (e.g., Python), nontechnical

skills (e.g., storytelling), and traits like analytical thinking.” This holistic requirement is true (A), not false

(B).

: Oracle Cloud Infrastructure Data Science Documentation, "Data Scientist Skills".

Detailed Answer in Step-by-Step Solution:

Objective: Assess if bias is a common issue in data science.

Define Bias: Systematic errors in data/models (e.g., skewed training data). Evaluate Statement:

Bias arises from unrepresentative data, poor feature selection, or algorithmic flaws—widely

recognized in ML.

Examples: Gender bias in hiring models, racial bias in facial recognition. Reasoning: Literature and

practice (e.g., fairness in AI) confirm bias as prevalent. Conclusion: A (True) is correct.

OCI documentation notes: “Bias is a common challenge in data science, stemming from imbalanced

datasets or flawed assumptions, requiring techniques like re-weighting or fairness checks.” This aligns

with industry standards—bias is a well-documented issue, making A true.

: Oracle Cloud Infrastructure Data Science Documentation, "Addressing Bias in Models".