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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q44-Q49):
NEW QUESTION # 44
What is a conda environment?
- A. An open-source environment management system
- B. An environment deployment system on Oracle AI
- C. A system that manages package dependencies
- D. A collection of kernels
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Define Conda: Conda is a widely used tool for managing packages and environments in data science.
* Evaluate Options:
* A: Partially true-Conda manages dependencies, but it's broader (an environment system).
* B: Incorrect-Kernels (e.g., Jupyter) are separate; Conda manages environments.
* C: Correct-Conda is an open-source tool for creating isolated environments with specific packages.
* D: Incorrect-Not specific to Oracle AI; it's a general tool.
* Reasoning: C captures Conda's full scope as an open-source system, beyond just dependency management (A).
* Conclusion: C is the most accurate.
OCI documentation describes Conda as "an open-source package and environment management system that allows data scientists to create isolated environments with specific versions of Python and libraries." A is too narrow, B misaligns with kernel concepts, and D ties it incorrectly to Oracle AI. C aligns with Conda's official definition and OCI's usage.
Oracle Cloud Infrastructure Data Science Documentation, "Conda Environments Overview".
NEW QUESTION # 45
You have received machine learning model training code, without clear information about the optimal shape to run the training. 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 smaller 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 optimizations and performance tests in advance to identify the right compute shape before running the model training as a job
- C. 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
- D. 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
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Optimize compute shape for cost and time.
* Evaluate Options:
* A: Tuning params-Focuses on model, not shape.
* B: Strongest shape-Costly, unbalanced.
* C: Scale up when utilized-Balances cost/time-correct.
* D: Random start-Unsystematic.
* Reasoning: C iteratively optimizes based on utilization.
* Conclusion: C is correct.
OCI documentation advises: "Start with a small shape, monitor utilization and time (C); scale up if fully utilized until performance stabilizes-optimizes cost and speed." A misfocuses, B overspends, D lacks method-only C aligns.
Oracle Cloud Infrastructure Data Science Documentation, "Compute Shape Optimization".
NEW QUESTION # 46
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. ODSC CLI
- B. OCI Python SDK
- C. Console
- D. Oracle Cloud Infrastructure (OCI) Command Line Interface (CLI)
- E. Accelerated Data Science (ADS) Software Development Kit (SDK)
- F. Git CLI
Answer: B,C,E
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 # 47
Which statement about Oracle Cloud Infrastructure Anomaly Detection is true?
- A. It is an important tool for detecting fraud, network intrusions, and discrepancies in sensor time series analysis
- B. It is trained on a combination of customer and general industry datasets
- C. Data used for analysis can be text or numerical in nature
- D. Accepted file types are SQL and Python
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Find a true statement about OCI Anomaly Detection.
* Understand Service: Detects anomalies in multivariate data (e.g., time series).
* Evaluate Options:
* A: False-Accepted types are CSV/JSON, not SQL/Python.
* B: Partially true-Focuses on numerical data (e.g., sensors), not text broadly.
* C: True-Used for fraud, intrusions, and sensor anomalies (key use cases).
* D: False-Trained on customer data only, not general datasets.
* Reasoning: C aligns with documented applications; others misalign.
* Conclusion: C is correct.
OCI Anomaly Detection documentation states: "The service is designed to detect anomalies in time series data, making it valuable for fraud detection, network intrusion analysis, and sensor discrepancies." A is incorrect (file formats), B overgeneralizes (numerical focus), and D misstates training data-only C matches the service's purpose.
Oracle Cloud Infrastructure Anomaly Detection Documentation, "Use Cases".
NEW QUESTION # 48
When preparing your model artifact to save it to the Oracle Cloud Infrastructure (OCI) DataScience model catalog, you create a score.py file. What is the purpose of the score.py file?
- A. Define the compute scaling strategy
- B. Configure the deployment infrastructure
- C. Define the inference server dependencies
- D. Execute the inference logic code
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Define the role of score.py in OCI model artifacts.
* Understand Artifacts: score.py is key for deployment runtime.
* Evaluate Options:
* A: Infra config-Handled by OCI settings, not score.py.
* B: Inference logic-Correct; runs load_model(), predict().
* C: Scaling-Set in deployment, not score.py.
* D: Dependencies-In runtime.yaml, not score.py.
* Reasoning: B aligns with score.py's execution role.
* Conclusion: B is correct.
OCI documentation states: "score.py (B) contains the inference logic, including functions to load the model and predict outputs, executed by the deployment endpoint." A, C, and D are managed elsewhere-only B matches OCI's design.
Oracle Cloud Infrastructure Data Science Documentation, "Model Artifact - score.py".
NEW QUESTION # 49
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