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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q103-Q108):

NEW QUESTION # 103
As a data scientist, you are tasked with creating a model training job that is expected to take different hyperparameter values on every run. What is the most efficient way to set those parameters with Oracle Data Science Jobs?

A. Create a new job every time you need to run your code and pass the parameters as environment variables
B. Create your code to expect different parameters as command-line arguments and create a new job every time you run the code
C. Create a new job by setting the required parameters in your code and create a new job for every code change
D. Create your code to expect different parameters either as environment variables or as command-line arguments, which are set on every job run with different values

Answer: D

Explanation:
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".

NEW QUESTION # 104
You are a computer vision engineer building an image recognition model. You decide to use Oracle Data Labeling to annotate your image data. Which of the following THREE are possible ways to annotate an image in Data Labeling?

A. Adding multiple labels to an image
B. Adding labels to an image using object detection, by drawing bounding boxes to an image
C. Adding labels to an image using semantic segmentation, by drawing multiple bounding boxes to an image
D. Adding labels to an image by drawing a bounding box to an image is not supported by Data Labeling
E. Adding a single label to an image

Answer: A,B,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three annotation methods in OCI Data Labeling for images.
* Understand Data Labeling: Supports image annotations for ML.
* Evaluate Options:
* A: Semantic segmentation with boxes-Incorrect; segmentation is pixel-based, not boxes.
* B: Single label (classification)-Supported-correct.
* C: No bounding boxes-False; boxes are supported.
* D: Object detection with boxes-Supported-correct.
* E: Multiple labels (multi-label)-Supported-correct.
* Reasoning: B (classification), D (detection), E (multi-label) match OCI capabilities.
* Conclusion: B, D, E are correct.
OCI documentation states: "Data Labeling supports image annotations via single-label classification (B), object detection with bounding boxes (D), and multi-label classification (E)." A misdefines segmentation, C contradicts support-only B, D, E are valid per OCI's Data Labeling features.
Oracle Cloud Infrastructure Data Labeling Documentation, "Image Annotation Types".

NEW QUESTION # 105
You have a dataset with fewer than 1000 observations, and you are using Oracle AutoML to build a classifier.
While visualizing the results of each stage of the Oracle AutoML pipeline, you notice that no visualization has been generated for one of the stages. Which stage is not visualized?

A. Algorithm selection
B. Feature selection
C. Adaptive sampling
D. Hyperparameter tuning

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the non-visualized AutoML stage with small data.
* Understand AutoML Pipeline: Includes sampling, feature/algorithm selection, tuning.
* Evaluate Options:
* A: Feature selection-Visualized (e.g., feature importance).
* B: Algorithm selection-Visualized (e.g., algorithm scores).
* C: Adaptive sampling-Skipped/visualization absent for <1000 rows.
* D: Hyperparameter tuning-Visualized (e.g., trial plots).
* Reasoning: Adaptive sampling optimizes large datasets; small data skips it, omitting visuals.
* Conclusion: C is correct.
OCI AutoML documentation notes: "Adaptive sampling is applied to large datasets (>1000 rows) to reduce size; for smaller datasets, it's skipped, and no visualization is generated." Other stages (A, B,D) produce visuals-only C is absent here.
Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Stages".

NEW QUESTION # 106
You are given a task of writing a program that sorts document images by language. Which Oracle AI Service would you use?

A. Oracle Digital Assistant
B. OCI Vision
C. OCI Speech
D. OCI Language

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select an OCI AI service to sort images by language.
* Evaluate Options:
* A: Digital Assistant-Chatbots, not image/language processing.
* B: Vision-Image analysis (e.g., object detection), not language sorting.
* C: Speech-Audio-to-text, not image-based.
* D: Language-Text analysis (e.g., language detection) after OCR-correct.
* Reasoning: Images need OCR (Vision) then language detection (Language)-D fits the sorting task.
* Conclusion: D is correct.
OCI Language "detects and classifies languages in text," often paired with OCI Vision's OCR to process document images. Vision (B) extracts text, but Language (D) sorts by language-Digital Assistant (A) and Speech (C) don't apply. Documentation supports this workflow.
Oracle Cloud Infrastructure Language Documentation, "Language Detection".

NEW QUESTION # 107
Which statement about Oracle Cloud Infrastructure Multi-Factor Authentication (MFA) is NOT valid?

A. A user can register only one device to use for MFA
B. Users must install a supported authenticator app on the mobile device they intend to register for MFA
C. Users cannot disable MFA for themselves
D. An administrator can disable MFA for another user

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the invalid MFA statement.
* Evaluate Options:
* A: True-Users can't disable MFA; admin-controlled.
* B: False-Multiple devices can be registered-invalid.
* C: True-Authenticator app is required.
* D: True-Admins can disable MFA.
* Reasoning: B contradicts OCI's multi-device support.
* Conclusion: B is incorrect.
OCI documentation states: "Users can register multiple devices for MFA (B is false), must use an authenticator app (C), and cannot disable MFA themselves (A)-admins can (D)." Only B is not valid per OCI's IAM MFA policy.
Oracle Cloud Infrastructure IAM Documentation, "Multi-Factor Authentication".

NEW QUESTION # 108
......

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100% PASS 2025 RELIABLE ORACLE 1Z0-1110-25: ORACLE CLOUD INFRASTRUCTURE 2025 DATA SCIENCE PROFESSIONAL BOOK FREE

100% Pass 2025 Reliable Oracle 1z0-1110-25: Oracle Cloud Infrastructure 2025 Data Science Professional Book Free