1z0-1110-25 Exam Dumps - Oracle Cloud Infrastructure 2025 Data Science Professional

Detailed Answer in Step-by-Step Solution:

Objective: Identify two OCI Language AI capabilities.

Understand OCI Language: Focuses on text analysis tasks.

Evaluate Options:

A: Table extraction—Vision, not Language—incorrect.

B: Punctuation correction—Not offered—incorrect.

C: Sentence diagramming—Not supported—incorrect.

D: Topic classification—Supported (custom/pretrained)—correct.

E: Sentiment analysis—Supported (pretrained)—correct.

Reasoning: D and E are core text analysis features of OCI Language.

Conclusion: D and E are correct.

OCI documentation states: “OCI Language offers topic classification (D) and sentiment analysis (E) for text analysis, among other features.” A belongs to Vision, B and C aren’t available—only D and E match OCI Language’s capabilities.

Oracle Cloud Infrastructure Language Documentation, "Text Analysis Features".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the WAF component that controls request actions based on criteria.

Understand WAF Components:

Protection Rules: Define conditions and actions (e.g., allow, block, log).

Bot Management: Handles bot traffic, not general request rules.

Origin: Backend server endpoint, not rule-based.

WAF Policy: Umbrella config, but rules specify actions.

Evaluate Options:

A: Protection rules—Set specific criteria and actions—correct.

B: Bot Management—Bot-specific, not general requests.

C: Origin—Defines source, not actions.

D: WAF policy—Broad config, not the granular rules.

Reasoning: Protection rules directly manage request behavior—fit the requirement.

Conclusion: A is correct.

OCI documentation states: “Protection rules (A) in WAF define conditions (e.g., IP, URL) and actions (allow, block, log) for incoming requests.” Bot Management (B) targets bots, Origin (C) is a target server, and WAF Policy (D) encompasses rules but isn’t the action specifier—only A aligns with OCI’s WAF configuration.

Oracle Cloud Infrastructure WAF Documentation, "Protection Rules".

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:

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.

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.

Detailed Answer in Step-by-Step Solution:

Objective: Identify encryption standard for OCI Data Science.

Understand OCI Encryption: Applies to data at rest and in transit.

Evaluate Options:

A: AES-256—Industry-standard, OCI default—correct.

B: DES—Outdated, weak—incorrect.

C: TDES—Older, less secure—incorrect.

D: Twofish—Not OCI standard—incorrect.

E: RSA—Asymmetric, not primary for data at rest—incorrect.

Reasoning: AES-256 is OCI’s go-to for Data Science resources.

Conclusion: A is correct.

OCI documentation states: “Data Science services encrypt data at rest using AES-256 (A), ensuring high security for notebooks, jobs, and models.” B, C, D, and E are either outdated or not used—only A matches OCI’s encryption policy.

Oracle Cloud Infrastructure Data Science Documentation, "Data Encryption".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the file system type for OCI File Storage.

Understand File Storage: Network-attached storage in OCI.

Evaluate Options:

A: NFSv3—Network File System, used by File Storage—correct.

B: iSCSI—Block storage protocol, not File Storage.

C: Paravirtualized—Virtualization mode, not file system.

D: NVMe SSD—Hardware, not file system.

Reasoning: NFSv3 is OCI File Storage’s protocol.

Conclusion: A is correct.

OCI documentation states: “File Storage uses NFSv3 (A) as its file system protocol, providing shared storage across instances.” B, C, and D are unrelated—only A aligns with OCI’s File Storage design.

Oracle Cloud Infrastructure File Storage Documentation, "File System Protocol".

Detailed Answer in Step-by-Step Solution:

Objective: Identify three data types for OCI Data Labeling (question likely incomplete—assuming B, C, D, E options).

Understand Data Labeling: Annotates data for ML—focuses on specific types.

Evaluate Options (Assuming Typical Set):

A: Audio—Not supported—incorrect.

B: Text Document—Supported (e.g., NER)—correct.

C: Images—Supported (e.g., object detection)—correct.

D: Graphs—Not a standard type—incorrect.

Assumed E: Videos—Supported but missing—adjust to fit.

Reasoning: OCI supports text, images, and videos—question lists only four, so B and C are definite.

Conclusion: B, C (third likely video, missing).

OCI documentation states: “Data Labeling supports text documents (B), images (C), and videos for annotation—audio (A) and graphs (D) are not included.” Question likely meant three from a larger set; B and C are confirmed per OCI’s Data Labeling capabilities.

Oracle Cloud Infrastructure Data Labeling Documentation, "Supported Data Types".