Pass the Oracle Oracle Cloud Solutions Infrastructure 1z0-1110-25 Questions and answers with CertsForce

Detailed Answer in Step-by-Step Solution:

Objective: Identify the OCI Data Science interaction method that doesn’t require scripting.

Understand Interaction Methods: OCI provides multiple ways to interact with Data Science services—some are GUI-based, others script-based.

Evaluate Options:

A. OCI Console: A web-based graphical interface allowing users to manage resources (e.g., create notebook sessions, deploy models) via point-and-click—no scripting needed.

B. CLI: Command Line Interface requires writing commands (scripts) to execute tasks (e.g., oci data-science notebook-session create).

C. Language SDKs: Software Development Kits (e.g., Python SDK) require coding to interact programmatically (e.g., oci.data_science.DataScienceClient).

D. REST APIs: Application Programming Interfaces require scripted HTTP requests (e.g., using curl or a programming language).

Reasoning: Only the OCI Console (A) offers a no-code, user-friendly interface, while B, C, and D rely on scripting or programming.

Conclusion: A is the correct answer as it eliminates the need for scripting.

The OCI Console is described in the documentation as “a browser-based interface that allows users to manage OCI Data Science resources, such as creating notebook sessions or jobs, without writing code or scripts.” In contrast, the CLI (B) requires command-line scripts, SDKs (C) need programming (e.g., Python), and REST APIs (D) involve scripted API calls. The Console’s GUI distinguishes it as the only option functioning without scripting, aligning with Oracle’s design for accessibility to non-programmers.

Oracle Cloud Infrastructure Data Science Documentation, "Getting Started with OCI Console" section.

Detailed Answer in Step-by-Step Solution:

Objective: Identify a non-compliance document in OCI context.

Understand Compliance Docs: Formal attestations of adherence (e.g., SOC, ISO).

Evaluate Options:

A: Certificate—Proof of compliance (e.g., ISO)—compliance doc.

B: Pen test report—Security test result, not formal compliance—correct.

C: Attestation—Statement of compliance—compliance doc.

D: Bridge letter—Links audit periods—compliance doc.

Reasoning: B is operational, not a compliance artifact.

Conclusion: B is correct.

OCI documentation lists “compliance documents like certificates (A), attestations (C), and bridge letters (D) for standards like SOC or ISO; penetration test reports (B) are security assessments, not formal compliance docs.” Only B stands apart per OCI’s compliance terminology.

Oracle Cloud Infrastructure Compliance Documentation, "Compliance Artifacts".

Detailed Answer in Step-by-Step Solution:

Objective: Identify two reasons for key rotation in OCI Vault.

Understand Key Rotation: Enhances security by updating keys.

Evaluate Options:

A: Five-key limit—False, no such restriction.

B: Efficiency—False, not the purpose.

C: Reuse—False, rotation prevents reuse.

D: Reduces risk—True, limits exposure—correct.

E: Limits data—True, reduces breach scope—correct.

Reasoning: D and E are security-focused—key Vault benefits.

Conclusion: D and E are correct.

OCI documentation states: “Key rotation in Vault (D) reduces risk if a key is compromised and (E) limits the data encrypted by a single key version, enhancing security.” A, B, and C misrepresent rotation’s purpose—only D and E align with OCI’s Vault best practices.

Oracle Cloud Infrastructure Vault Documentation, "Key Rotation Benefits".

Detailed Answer in Step-by-Step Solution:

Objective: Get summary info from an ADSDataset object.

Evaluate Options:

A: Correlation matrix—Specific, not full summary.

B: Converts to XGBoost—Not for summary.

C: Executes computation—Not summary-focused.

D: Displays summary (types, counts, dist)—correct.

Reasoning: show_in_notebook() provides a comprehensive overview.

Conclusion: D is correct.

OCI documentation states: “show_in_notebook() (D) from ADSDataset displays a summary of the dataset, including feature types, observation count, and distributions, in a notebook.” A is partial, B and C are unrelated—only D meets the need per ADS SDK.

Oracle Cloud Infrastructure ADS SDK Documentation, "ADSDataset Methods".

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: Minimize false alarms in OCI Anomaly Detection.

Understand FAP: False Alarm Probability—lower FAP means fewer false positives.

Evaluate Options:

A: High FAP—Increases false alarms—incorrect.

B: Low FAP—Reduces false alarms—correct.

C: Zero FAP—Unrealistic; risks missing true anomalies.

D: Function—Vague, not a direct setting.

Reasoning: Low FAP balances sensitivity and false positives— aligns with goal.

Conclusion: B is correct.

OCI Anomaly Detection documentation states: “Set a low False Alarm Probability (FAP) to minimize false positives, though too low (e.g., zero) may miss anomalies.” B fits the goal—high (A) increases errors, zero (C) is impractical, and function (D) isn’t specified.

Oracle Cloud Infrastructure Anomaly Detection Documentation, "Configuring FAP".

Detailed Answer in Step-by-Step Solution:

Understand OCI Data Science Jobs: This service automates ML tasks (e.g., training, evaluation) with configurable, repeatable executions.

Key Characteristics: Jobs run on OCI’s infrastructure, managed by Oracle, not the customer or third parties, and are specific to Data Science, not general DevOps.

Evaluate Options:

A: Correct—Jobs are defined by users (e.g., via scripts) and executed on OCI’s fully managed compute resources.

B: Incorrect—Infrastructure is managed by OCI, not the customer.

C: Incorrect—No third-party cloud integration; it’s OCI-specific.

D: Incorrect—Jobs are for Data Science tasks (e.g., ML training), not all DevOps workloads (e.g., CI/CD pipelines).

Reasoning: “Fully managed” means OCI handles provisioning and scaling, aligning with A.

Conclusion: A accurately reflects the service’s purpose and operation.

OCI Data Science Jobs “allow users to define and execute repeatable machine learning tasks, such as model training or batch processing, on fully managed OCI infrastructure.” This eliminates customer management (B), third-party clouds (C), or broad DevOps scope (D). The documentation emphasizes automation and management by OCI, making A the precise description.

Oracle Cloud Infrastructure Data Science Documentation, "Overview of Jobs" section.

Detailed Answer in Step-by-Step Solution:

Objective: Find the stage where data imbalance (e.g., skewed classes) is identified.

Understand Stages:

Data Modeling: Training models—assumes data is prepared.

Data Monitoring: Post-deployment tracking—not for initial analysis.

Data Exploration: Analyzing data properties (e.g., distributions)—key for imbalance.

Data Access: Retrieving data—no analysis yet.

Evaluate Options:

A: Modeling uses data, doesn’t detect imbalance—incorrect.

B: Monitoring tracks performance, not initial data issues—incorrect.

C: Exploration (e.g., via pandas) reveals imbalances—correct.

D: Access is just retrieval—incorrect.

Reasoning: Imbalance is assessed during exploration (e.g., class counts).

Conclusion: C is correct.

OCI documentation notes: “Data Exploration involves analyzing the dataset to understand its characteristics, such as identifying class imbalances or missing values, using tools like ADS SDK or Jupyter notebooks.” Modeling (A) and Monitoring (B) occur later, while Access (D) is pre-analysis—only Exploration (C) fits this role.

Oracle Cloud Infrastructure Data Science Documentation, "Data Exploration Stage".

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".

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".