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

NEW QUESTION # 38
You have configured the Management Agent on an Oracle Cloud Infrastructure (OCI) Linux instance for log ingestion purposes. Which is a required configuration for OCI Logging Analytics service to collect data from multiple logs of this instance?

A. Source - Entity Association
B. Log Group - Source Association
C. Entity - Log Association
D. Log - Log Group Association

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the required configuration for OCI Logging Analytics to collect logs from an instance.
* Understand Logging Analytics: Collects and analyzes logs from OCI resources via Management Agents.
* Key Concepts:
* Entity: Represents the instance (e.g., Linux VM).
* Source: Defines log locations (e.g., file paths).
* Log Group: Organizes logs for analysis.
* Evaluate Options:
* A: Log-Log Group-Groups logs, not collection setup.
* B: Entity-Log-Links instance to logs, but not source-specific.
* C: Source-Entity-Maps log sources to the instance-correct.
* D: Log Group-Source-Post-collection organization, not ingestion.
* Reasoning: C establishes the link between the instance and its log sources-key for ingestion.
* Conclusion: C is correct.
OCI documentation states: "To collect logs using Logging Analytics, configure a Source-Entity Association (C) to link the Management Agent on the instance (entity) to specific log sources (e.g., file paths)." A and D organize logs post-collection, B is less specific-only C is required for ingestion per OCI's Logging Analytics setup.
Oracle Cloud Infrastructure Logging Analytics Documentation, "Configuring Log Collection".

NEW QUESTION # 39
True or false? Data scientists typically need a combination of technical skills, nontechnical ones, and suitable personality traits to be successful.

A. True
B. False

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Assess required skills for data scientists.
* Analyze Skills:
* Technical: Coding, stats, ML.
* Nontechnical: Communication, business acumen.
* Traits: Curiosity, problem-solving.
* Reasoning: Success requires this mix-e.g., explaining models to stakeholders.
* Conclusion: A (True) is correct.
OCI documentation states: "Effective data scientists combine technical skills (e.g., Python), nontechnical skills (e.g., storytelling), and traits like analytical thinking." This holistic requirement is true (A), not false (B).
Oracle Cloud Infrastructure Data Science Documentation, "Data Scientist Skills".

NEW QUESTION # 40
You have received machine learning model training code, without clear information about the optimal shape to run the training on. How would you proceed to identify the optimal compute shape for your model training that provides a balanced cost and processing time?

A. Start with a small shape and monitor the utilization metrics and time required to complete the model training. If the compute shape is fully utilized, change to compute that has more resources and rerun the job. Repeat the process until the processing time does not improve.
B. Start with a random compute shape and monitor the utilization metrics and time required to finish the model training. Perform model training optimization and performance tests in advance to identify the right compute shape before running the model training as a job.
C. Start with the strongest compute shape Jobs support and monitor the job run metrics and time required to complete the model training. Tune the model so that it utilizes as much compute resources as possible, even at an increased cost.
D. Start with a smaller shape and monitor the job run metrics and time required to complete the model training. If the compute shape is not fully utilized, tune the model parameters, and rerun the job. Repeat the process until the shape resources are fully utilized.

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Find optimal compute shape balancing cost and time.
* Approach: Iterative testing with metrics (e.g., CPU/memory usage, runtime).
* Evaluate Options:
* A: Tuning parameters when underutilized-focuses on model, not shape optimization.
* B: Strongest shape-Costly, ignores balance; overkill likely.
* C: Scale up from small shape when fully utilized-Balances cost/time effectively.
* D: Random start with pre-tests-Unsystematic and inefficient.
* Reasoning: C incrementally increases resources based on utilization, optimizing both factors.
* Conclusion: C is correct.
OCI documentation advises: "To optimize compute shape for Jobs, start with a small shape, monitor utilization (e.g., CPU, memory) and runtime via OCI Monitoring. If fully utilized, scale up until performance plateaus-balancing cost and speed." A misfocuses on model tuning, B wastes cost, and D lacks structure- only C aligns with this method.
Oracle Cloud Infrastructure Data Science Documentation, "Optimizing ComputeShapes for Jobs".

NEW QUESTION # 41
You are asked to prepare data for a custom-built model that requires transcribing Spanish video recordings into a readable text format with profane words identified. Which Oracle Cloud Service would you use?

A. OCI Language
B. OCI Translation
C. OCI Speech
D. OCI Anomaly Detection

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Transcribe Spanish video audio and identify profanity.
* Evaluate Options:
* A: Anomaly Detection-Not for transcription or text analysis.
* B: Speech-Converts audio to text (e.g., Spanish), base for further analysis-correct.
* C: Translation-Translates text, not transcription.
* D: Language-Analyzes text (e.g., profanity), but needs transcribed input.
* Reasoning: Speech (B) transcribes video audio; Language could follow for profanity.
* Conclusion: B is correct for transcription.
OCI Speech "transcribes audio from video or audio files into text, supporting languages like Spanish." Post- transcription, OCI Language could detect profanity, but B is the starting point-Anomaly (A) and Translation (C) don't fit.
Oracle Cloud Infrastructure Speech Documentation, "Transcription Features".

NEW QUESTION # 42
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 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
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 a new job every time you need to run your code and pass the parameters as environment variables

Answer: A

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 # 43
......

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1z0-1110-25 Testking Learning Materials - Official 1z0-1110-25 Practice Test

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

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