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

Detailed Answer in Step-by-Step Solution:

Objective: Choose an interface for a Python script to manage projects.

Evaluate Options:

A: OCI SDK—Python-based, scriptable—correct.

B: Console—GUI, not scriptable.

C: CLI—Command-based, not Python-native.

D: Mobile App—Not for scripting.

Reasoning: A enables programmatic project creation.

Conclusion: A is correct.

OCI documentation states: “Use the OCI Python SDK (A) to programmatically manage Data Science resources, like creating projects, via Python scripts.” B, C, and D don’t support Python scripting—only A fits.

Oracle Cloud Infrastructure SDK Documentation, "Data Science API".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the non-Observability and Management (O&M) service in OCI.

Understand O&M: Includes monitoring, logging, events tools.

Evaluate Options:

A: Event Services—Triggers actions, part of O&M—correct.

B: OCI Management Service—Not a defined O&M service—incorrect.

C: Logging Analytics—Log analysis, O&M component—correct.

D: Logging—Log collection, O&M component—correct.

Reasoning: B isn’t listed in OCI’s O&M suite—others are.

Conclusion: B is correct (not part of O&M).

OCI documentation lists “Observability and Management Services as including Event Services (A), Logging Analytics (C), and Logging (D)—‘OCI Management Service’ (B) is not a recognized component.” B appears to be a misnomer—only A, C, D are O&M per OCI’s service catalog.

Oracle Cloud Infrastructure Observability and Management Documentation, "Service Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Identify two OCI-developed, non-open-source JupyterLab extensions.

Understand Extensions: OCI enhances JupyterLab with proprietary tools.

Evaluate Options:

A: Environment Explorer—OCI-specific, non-open—correct.

B: Table of Contents—Open-source Jupyter—incorrect.

C: Command Palette—Open-source Jupyter—incorrect.

D: Notebook Examples—OCI-specific, non-open—correct.

E: Terminal—Open-source Jupyter—incorrect.

Reasoning: A and D are OCI proprietary; others are standard JupyterLab.

Conclusion: A and D are correct.

OCI documentation states: “OCI Data Science adds non-open-source extensions like Environment Explorer (A) for conda management and Notebook Examples (D) for sample code—both proprietary enhancements.” B, C, and E are open-source JupyterLab defaults—only A and D are OCI-specific per the notebook session design.

Oracle Cloud Infrastructure Data Science Documentation, "JupyterLab Extensions".

Detailed Answer in Step-by-Step Solution:

Objective: Identify ADS class for accessing datasets (e.g., scikit-learn).

Evaluate Options:

A: DatasetBrowser—Not an ADS class.

B: DatasetFactory—Loads datasets from sources like scikit-learn—correct.

C: ADSTuner—Hyperparameter tuning, not data access.

D: SecretKeeper—Manages credentials, not datasets.

Reasoning: DatasetFactory simplifies dataset loading (e.g., DatasetFactory.open()).

Conclusion: B is correct.

OCI documentation states: “DatasetFactory in ADS SDK provides methods to easily load datasets from libraries like scikit-learn or other sources (e.g., DatasetFactory.open('sklearn.datasets:load_iris')).” A isn’t real, C tunes models, and D handles secrets—only B fits.

Oracle Cloud Infrastructure ADS SDK Documentation, "DatasetFactory".

Detailed Answer in Step-by-Step Solution:

Objective: Identify three required params for an OCI Data Flow app config.

Understand Data Flow: Runs Spark apps; needs compartment, storage, and identity.

Evaluate Options:

A: Archive path—Optional if script is in Object Storage—incorrect.

B: Local script path—Not needed; script is uploaded—incorrect.

C: Compartment—Required for resource scope—correct.

D: Bucket—Required for script storage/access—correct.

E: Display name—Required for app identification—correct.

Reasoning: C, D, E are mandatory metadata for Data Flow creation—script location is specified via bucket.

Conclusion: C, D, E are correct.

OCI documentation states: “To create a Data Flow application, configure the compartment OCID (C), Object Storage bucket for the PySpark script (D), and a display name (E) in the application object.” Local paths (B) or archives (A) are optional or handled separately—only C, D, E are required per OCI’s Data Flow API spec.

Oracle Cloud Infrastructure Data Flow Documentation, "Creating Applications".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the OCI service for ML model lifecycle.

Evaluate Options:

A: Data Catalog—Metadata management, not ML.

B: Data Integration—ETL, not ML.

C: Data Science—Full ML lifecycle—correct.

D: Data Flow—Spark processing, not full ML.

Reasoning: C supports building, training, deploying models.

Conclusion: C is correct.

OCI documentation states: “OCI Data Science (C) provides tools to build, train, and deploy machine learning models in the cloud, including notebooks and model catalog.” A, B, and D serve other purposes—only C fits the ML lifecycle per OCI’s offerings.

Oracle Cloud Infrastructure Data Science Documentation, "Service Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Troubleshoot a 404 auth error when saving a model with resource principal.

Understand Resource Principal: Allows notebook sessions to act as principals via dynamic groups and policies—no user credentials.

Analyze 404 Error: Indicates permission failure—likely IAM misconfiguration.

Evaluate Options:

A: Block volume storage—Irrelevant to auth; it’s about saving locally—incorrect.

B: Dynamic group matching—Ensures notebook is recognized—correct.

C: User group policy—Not used with resource principal—incorrect.

D: Dynamic group policy—Grants catalog access—correct.

E: Service Gateway—Network-related, not auth-specific—incorrect.

Reasoning: Resource principal needs B (group inclusion) and D (policy perms)—404 points to these.

Conclusion: B and D are correct.

OCI documentation states: “For ADS SDK to save to the Model Catalog using resource principal, ensure (1) a dynamic group includes notebook sessions with matching rules (e.g., resource.type ='datasciencenotebooksession') (B), and (2) a policy grants manage data-science-models to that dynamic group (D).” A is storage, C is user-based, E is network—only B and D fix the auth issue per OCI’s IAM setup.

Oracle Cloud Infrastructure Data Science Documentation, "Resource Principal with Model Catalog".

Detailed Answer in Step-by-Step Solution:

Objective: Spot the invalid OCI IAM policy.

Understand Policies: Use verbs (e.g., use, manage) and resource types.

Evaluate Options:

A: Valid—virtual-network-family is a resource type.

B: Invalid—No data-science-model-sessions resource type; likely meant data-science-models.

C: Valid—data-science-projects is correct.

D: Valid—data-science-family aggregates Data Science resources.

Reasoning: B uses a nonexistent resource type—error in syntax.

Conclusion: B is incorrect.

OCI IAM documentation lists valid resource types: “data-science-models, data-science-projects, data-science-family—but not data-science-model-sessions (B).” A, C, D are correct—B is invalid due to this typo/misunderstanding.

Oracle Cloud Infrastructure IAM Documentation, "Data Science Resource Types".

Detailed Answer in Step-by-Step Solution:

Objective: Efficiently vary hyperparameters in OCI Jobs.

Evaluate Options:

A: New job per run—Wastes setup time.

B: Code changes per job—Inefficient, error-prone.

C: Flexible params per run—Efficient, reusable—correct.

D: New job per run—Redundant effort.

Reasoning: C minimizes job creation, maximizes flexibility.

Conclusion: C is correct.

OCI documentation states: “For varying hyperparameters, configure a single Job with code accepting environment variables or command-line arguments (C), set per run—most efficient.” A and D over-create jobs, B ties params to code—only C optimizes.

Oracle Cloud Infrastructure Data Science Documentation, "Job Parameterization".

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".