RELIABLE 1Z0-1110-25 DUMPS BOOK, VALID 1Z0-1110-25 MOCK EXAM

Reliable 1z0-1110-25 Dumps Book, Valid 1z0-1110-25 Mock Exam

Reliable 1z0-1110-25 Dumps Book, Valid 1z0-1110-25 Mock Exam

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q38-Q43):

NEW QUESTION # 38
You are using a custom application with third-party APIs to manage application and data hosted in an Oracle Cloud Infrastructure (OCI) tenancy. Although your third-party APIs don't support OCI's signature-based authentication, you want them to communicate with OCI resources. Which authentication option must you use to ensure this?

  • A. API Signing Key
  • B. Auth Token
  • C. SSH Key Pair with 2048-bit algorithm
  • D. OCI username and password

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select an auth method for third-party APIs lacking OCI signature support.
* Understand OCI Auth: Typically uses API keys, but alternatives exist for non-standard APIs.
* Evaluate Options:
* A: Username/password-Not API-friendly, insecure.
* B: API Signing Key-Requires signature-based auth, unsupported here.
* C: SSH Key-For instance access, not APIs.
* D: Auth Token-Simple token for API calls-correct.
* Reasoning: Auth Token provides a bearer token for APIs without signature complexity.
* Conclusion: D is correct.
OCI documentation states: "For third-party APIs not supporting signature-based authentication, use an Auth Token (D), a secure, revocable token for accessing OCI resources via REST APIs." A, B, and C don't fit non- signature scenarios-only D ensures compatibility per OCI's IAM options.
Oracle Cloud Infrastructure IAM Documentation, "Auth Tokens for API Access".


NEW QUESTION # 39
You have a complex Python code project that could benefit from using Data Science Jobs as it is a repeatable machine learning model training task. The project contains many sub-folders and classes. What is the best way to run this project as a Job?

  • A. Rewrite your code so that it is a single executable Python or Bash/Shell script file
  • B. ZIP the entire code project folder and upload it as a Job artifact on job creation. Jobs identifies the main executable file automatically
  • C. ZIP the entire code project folder, upload it as a Job artifact on job creation, and set JOB_RUN_ENTRYPOINT to point to the main executable file
  • D. ZIP the entire code project folder and upload it as a Job artifact. Jobs automatically identifies the main top-level where the code is run

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Run a complex Python project as an OCI Job.
* Evaluate Options:
* A: Auto-identification-False; entrypoint must be set.
* B: Rewrite-Unnecessary, inefficient.
* C: Auto-executable-False; needs explicit entrypoint.
* D: ZIP with entrypoint-Correct, flexible approach.
* Reasoning: D preserves structure, specifies execution.
* Conclusion: D is correct.
OCI documentation states: "For complex projects, ZIP the folder and upload as a Job artifact, then set JOB_RUN_ENTRYPOINT (D) to the main executable (e.g., main.py)." Auto-detection (A, C) isn't supported, and B discards structure-D is best.
Oracle Cloud Infrastructure Data Science Documentation, "Job Artifacts".


NEW QUESTION # 40
Which of the following best describes the principal goal of data science?

  • A. To collect and archive exhaustive datasets from various source systems for corporate record-keeping uses.
  • B. To mine and analyze large amounts of data in order to uncover information that can be used for operational improvements and business gains.
  • C. To collect and prepare data for use as part of analytics applications.
  • D. Data science is focused on output of the analysis.

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Define data science's main goal.
* Evaluate Options:
* A: Archiving-Not the focus; too narrow.
* B: Analyze for insights/business value-Core purpose-correct.
* C: Prep for analytics-Means, not the end goal.
* D: Output-focused-Vague, incomplete.
* Reasoning: B captures the actionable insight generation central to data science.
* Conclusion: B is correct.
OCI documentation defines data science as "mining and analyzing large datasets to uncoveractionable insights for operational improvements and business value." A is storage-focused, C is preparatory, and D is unclear-only B reflects the principal goal per OCI's mission.
Oracle Cloud Infrastructure Data Science Documentation, "What is Data Science?".


NEW QUESTION # 41
As a data scientist, you are trying to automate a machine learning (ML) workflow and have decided to use Oracle Cloud Infrastructure (OCI) AutoML Pipeline. Which THREE are part of the AutoML Pipeline?

  • A. Adaptive Sampling
  • B. Algorithm Selection
  • C. Feature Selection
  • D. Model Deployment
  • E. Feature Extraction

Answer: A,B,C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three stages in OCI AutoML Pipeline.
* Understand Pipeline: Automates ML steps from data to model training.
* Evaluate Options:
* A: Feature Selection-Selects relevant features-correct.
* B: Adaptive Sampling-Reduces data size-correct.
* C: Model Deployment-Post-pipeline step-incorrect.
* D: Feature Extraction-Not explicit in OCI AutoML-incorrect.
* E: Algorithm Selection-Chooses best model-correct.
* Reasoning: A, B, E are core automated stages; C and D are separate.
* Conclusion: A, B, E are correct.
OCI documentation lists "AutoML Pipeline stages as adaptive sampling (B), feature selection (A), algorithm selection (E), and hyperparameter tuning." Deployment (C) is post-pipeline, and extraction (D) isn't highlighted-only A, B, E are included per OCI's design.
Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Components".


NEW QUESTION # 42
You are a data scientist trying to load data into your notebook session. You understand that Accelerated Data Science (ADS) SDK supports loading various data formats. Which of the following THREE are ADS- supported data formats?

  • A. JSON
  • B. Pandas DataFrame
  • C. XML
  • D. Raw Images
  • E. DOCX

Answer: A,B,C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three data formats supported by ADS SDK for loading data.
* Understand ADS SDK: Facilitates data loading into notebook sessions via DatasetFactory.
* Evaluate Options:
* A. DOCX: Not natively supported-requires conversion (e.g., to text).
* B. Pandas DataFrame: Supported-core format for data manipulation in ADS.
* C. JSON: Supported-common structured data format.
* D. Raw Images: Not directly supported-image data needs preprocessing (e.g., via Vision).
* E. XML: Supported-parseable structured format.
* Reasoning: ADS focuses on tabular/structured data-B, C, E align; A and D require external handling.
* Conclusion: B, C, E are correct.
OCI documentation states: "ADS SDK's DatasetFactory supports loading data from formats like Pandas DataFrames (B), JSON (C), and XML (E), enabling easy integration into notebook sessions." DOCX (A) isn't natively handled, and raw images (D) require preprocessing outside ADS-B, C, E match the supported list.
Oracle Cloud Infrastructure ADS SDK Documentation, "Supported Data Formats".


NEW QUESTION # 43
......

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