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NEW QUESTION # 132
A bike sharing platform has collected user commute data for the past 3 years. For increasing profitability and making useful inferences, a machine learning model needs to be built from the accumulated data. Which of the following options has the correct order of the required machine learning tasks for building a model?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Data Access: The first step in any machine learning workflow is accessing the raw data. This involves retrieving the user commute data collected over the past 3 years from the bike-sharing platform's storage system.
* Data Exploration: Once data is accessed, it's explored to understand its structure, quality, and patterns (e.g., missing values, distributions). This step helps identify what preprocessing is needed.
* Feature Engineering: After understanding the data, features are created or transformed (e.g., commute duration, time of day) to improve model performance. This step precedes feature exploration because you need engineered features to analyze further.
* Feature Exploration: This involves analyzing the engineered features (e.g., correlation analysis, importance ranking) to refine them or select the most relevant ones for modeling.
* Modeling: Finally, the prepared data and features are used to train and evaluate a machine learning model.
Option C (Data Access, Data Exploration, Feature Engineering, Feature Exploration, Modeling) follows this logical sequence, aligning with standard ML workflows.
The correct order reflects the machine learning lifecycle as outlined in Oracle's OCI Data Science documentation. Data Access is the initial step to retrieve data, followed by Data Exploration to assess it (e.g., using OCI Data Science Notebook Sessions with tools like pandas). Feature Engineering transforms raw data into meaningful inputs, followed by Feature Exploration to analyze feature importance (e.g., using ADS SDK' s correlation tools). Modeling is the final step where the model is built and trained. This sequence is consistent with Oracle's recommended practices for building ML models in OCI Data Science (Reference: Oracle Cloud Infrastructure Data Science Service Documentation, "Machine Learning Lifecycle").
NEW QUESTION # 133
What do you use the score.py file for?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine the purpose of score.py in OCI Data Science model deployment.
* Understand Model Deployment: When deploying a model in OCI, artifacts include score.py, runtime.
yaml, etc.
* Evaluate Options:
* A: Infrastructure configuration (e.g., compute shape) is handled by deployment settings, not score.
py.
* B: score.py contains the inference logic (e.g., load_model(), predict())-correct.
* C: Conda environment is defined in runtime.yaml or a requirements file-not score.py.
* D: Scaling (e.g., instance count) is set in deployment configuration-not score.py.
* Reasoning: score.py is the script executed by the deployment endpoint to load the model and make predictions.
* Conclusion: B is the correct purpose.
The OCI Data Science documentation states: "The score.py file is a required artifact for model deployment, containing the inference logic-functions like load_model() to load the model and predict() to generate predictions from input data." Infrastructure (A) and scaling (D) are managed via the OCI Console or SDK, while the environment (C) is specified in runtime.yaml. B is the precise role of score.py in OCI's deployment workflow.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - score.py".
NEW QUESTION # 134
Which OCI cloud service lets you centrally manage the encryption keys that protect your data and the secret credentials that you use to securely access resources?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the OCI service for key and secret management.
* Evaluate Options:
* A: Data Safe-Database security, not key management.
* B: Cloud Guard-Threat detection, not keys.
* C: Data Guard-DB replication, not keys.
* D: Vault-Key and secret management-correct.
* Reasoning: Vault is OCI's dedicated service for crypto keys and secrets.
* Conclusion: D is correct.
OCI documentation states: "OCI Vault (D) centrally manages encryption keys and secrets, securing data and resource access." A, B, and C serve other purposes-only D matches per OCI's securityservices.
Oracle Cloud Infrastructure Vault Documentation, "Overview".
NEW QUESTION # 135
You are working in your notebook session and find that your notebook session does not have enough compute CPU and memory for your workload. How would you scale up your notebook session without losing your work?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Scale up a notebook session without losing work.
* Understand Persistence: Block volume stores session data (e.g., /home/datascience).
* Evaluate Options:
* A: Recreating work-inefficient, risks loss.
* B: Local download/upload-cumbersome, unnecessary.
* C: Use block volume persistence, scale up-efficient, preserves work-correct.
* D: Object Storage-extra steps, not needed with block volume.
* Reasoning: C leverages OCI's built-in persistence for seamless scaling.
* Conclusion: C is correct.
OCI documentation states: "Files in /home/datascience are stored on the block volume. To scale up, deactivate the session, provision a new one with a larger shape, and the block volume persists your work." A loses data, B and D add complexity-only C is optimal.
Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".
NEW QUESTION # 136
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?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Efficiently manage varying hyperparameters in OCI Data Science Jobs.
* Understand OCI Jobs: Jobs execute predefined tasks with configurable inputs (e.g., env vars, args).
* Evaluate Options:
* A: New job per run with env vars-Redundant job creation, inefficient.
* B: New job per run with args-Similarly inefficient due to repeated setup.
* C: Hardcode params, new job per change-Highly inefficient, requires code edits.
* D: Single job, flexible params via env vars or args-Efficient, reusable-correct.
* Reasoning: D minimizes job creation, allows runtime flexibility via configuration-best practice.
* Conclusion: D is correct.
OCI documentation states: "For Jobs with varying hyperparameters, write code to accept environment variables or command-line arguments (D), then configure these per Job Run using the OCI Console or SDK- most efficient approach." Options A, B, and C involve unnecessary job proliferation or code changes-only D aligns with OCI's design for parameterized runs.
Oracle Cloud Infrastructure Data Science Documentation, "Configuring Job Runs with Parameters".
NEW QUESTION # 137
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