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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q66-Q71):

NEW QUESTION # 66
You are given the task of writing a program that sorts document images by language. Which Oracle service would you use?

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

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the Oracle service to sort document images by language.
* Task Breakdown: Requires extracting text from images (OCR) and detecting language-two potential services involved.
* Evaluate Options:
* A. Oracle Digital Assistant: Builds chatbots-irrelevant to image or language processing.
* B. OCI Language: Detects and classifies languages in text-ideal for sorting after text extraction.
* C. OCI Speech: Transcribes audio to text-not applicable to images.
* D. OCI Vision: Performs OCR to extract text from images-necessary but not sufficient for language sorting.
* Reasoning: The task emphasizes "sorting by language." OCI Vision extracts text, but OCI Language identifies the language (e.g., English, Spanish). Since the question asks for one service and focuses on sorting, OCI Language (B) is the best fit, assuming text extraction is a precursor step.
* Conclusion: B is correct.
OCI Language "provides language detection and classification capabilities, enabling identification of languages in text extracted from documents," per the documentation. OCI Vision handles OCR, but the sorting task aligns with OCI Language (B). Digital Assistant (A) and Speech (C) don't apply, and while Vision (D) is a prerequisite, B is the primary service for language sorting as per OCI's AI service design.
Oracle Cloud Infrastructure Language Documentation, "Language Detection Features".

NEW QUESTION # 67
You have just started as a data scientist at a healthcare company. You have been asked to analyze and improve a deep neural network model, which was built based on the electrocardiogram records of patients.
There are no details about the model framework that was built. What would be the best way to find more details about the machine learning models inside the model catalog?

A. Check for metadata tags
B. Check for provenance details
C. Check for model taxonomy details
D. Refer to the code inside the model

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Context Analysis: You need to investigate an existing deep neural network model in the OCI Model Catalog with no prior information.
* Understand Model Catalog: The Model Catalog stores trained models along with metadata, hyperparameters, and provenance (origin and history) details.
* Evaluate Options:
* A. Refer to the code inside the model: The model artifact (e.g., a serialized file like .pkl) doesn't typically include readable source code; it's a trained object, not the training script.
* B. Check for model taxonomy details: Taxonomy (e.g., classification vs. regression) provides high-level categorization but lacks specifics like framework or architecture.
* C. Check for metadata tags: Metadata includes name, description, and tags, offering some context but not detailed framework info (e.g., TensorFlow vs. PyTorch).
* D. Check for provenance details: Provenance tracks the model's creation process, including the framework, training environment, and data sources, providing the most comprehensive insight.
* Reasoning: Provenance details are designed to document the "how" and "what" of model creation, making them ideal for uncovering the framework (e.g., Keras, PyTorch) and other specifics absent from initial handover.
* Conclusion: D is the best approach for detailed investigation.
In OCI Data Science, the Model Catalog stores provenance information, which includes "details about the model's origin, such as the framework used (e.g., TensorFlow, PyTorch), the training environment, and dataset references." This is more informative than metadata tags (C), which are user-defined and less structured, or taxonomy (B), which is broad. The model artifact (A) is a binary file (e.g., pickle), not a readable codebase. Provenance (D) offers a detailed audit trail, critical for analyzing an undocumented deep neural network model like this one.
Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog - Provenance Details" section.

NEW QUESTION # 68
You are a data scientist working for a utilities company. You have developed an algorithm that detects anomalies from a utility reader in the grid. The size of the model artifact is about 2 GB, and you are trying to store it in the model catalog. Which THREE interfaces could you use to save the model artifact into the model catalog?

A. Accelerated Data Science (ADS) Software Development Kit (SDK)
B. Console
C. Git CLI
D. Oracle Cloud Infrastructure (OCI) Command Line Interface (CLI)
E. ODSC CLI
F. OCI Python SDK

Answer: A,B,F

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify interfaces to save a 2 GB model to the Model Catalog.
* Evaluate Options:
* A: OCI CLI-Supports Data Science tasks-possible but not primary.
* B: ADS SDK-Designed for model catalog ops-correct.
* C: ODSC CLI-Not standard; likely typo for OCI CLI.
* D: Console-GUI for catalog uploads-correct.
* E: OCI Python SDK-Programmatic catalog access-correct.
* F: Git CLI-Version control, not catalog-related.
* Reasoning: B, D, E are OCI's primary interfaces; A is valid but less emphasized.
* Conclusion: B, D, E are correct (A plausible but not top-tier).
OCI documentation lists "ADS SDK (B), OCI Console (D), and OCI Python SDK (E) as primary methods to save models to the Model Catalog." OCI CLI (A) works but isn't highlighted, C isn't real, and F is unrelated- B, D, E are the standard trio.
Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Interfaces".

NEW QUESTION # 69
How are datasets exported in the OCI Data Labeling service?

A. As a line-delimited JSON file
B. As an XML file
C. As a binary file
D. As a CSV file

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand OCI Data Labeling Export: After annotation, datasets are exported for ML use.
* Check Supported Formats: OCI Data Labeling exports annotations in a structured, machine-readable format.
* Evaluate Options:
* A: Binary isn't a standard export format for annotations.
* B: XML isn't used; JSON is preferred for flexibility.
* C: Line-delimited JSON is the correct format, aligning with ML workflows.
* D: CSV is common but not the default for OCI Data Labeling.
* Conclusion: C matches the official export format.
OCI Data Labeling exports annotated datasets as line-delimited JSON files, which store each annotation as a separate JSON object per line, suitable for ML pipelines. This is explicitly stated in the documentation.
(Reference: Oracle Cloud Infrastructure Data Labeling Service Documentation, "Exporting Datasets").

NEW QUESTION # 70
You have an embarrassingly parallel or distributed batch job with a large amount of data running using Data Science Jobs. What would be the best approach to run the workload?

A. Create a new job for every job run that you have to run in parallel, because the Data Science Job service can have only one job per job
B. Reconfigure the job run because Data Science Jobs does not support embarrassingly parallel
C. Create the job in Data Science Jobs and start a job run. When it is done, start a new job run until you achieve the number of runs required
D. Create a job in Data Science Jobs and then start the number of simultaneous job runs required for your workload

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Optimize an embarrassingly parallel job in OCI Data Science Jobs.
* Define Embarrassingly Parallel: Tasks are independent, ideal for simultaneous runs.
* Evaluate Options:
* A: Multiple simultaneous runs-Leverages parallelism-correct.
* B: One job per run-Misstates capability; unnecessary complexity.
* C: Sequential runs-Inefficient, ignores parallelism.
* D: False-Jobs support parallelism.
* Reasoning: A maximizes efficiency for parallel tasks.
* Conclusion: A is correct.
OCI documentation states: "For embarrassingly parallel workloads, create a single Job and launch multiple simultaneous Job Runs to process data in parallel." B misinterprets limits, C wastes time, and D denies capability-only A fits OCI's design.
Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Runs".

NEW QUESTION # 71
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