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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: MLOps and OCI Integration | 20% | - OCI ecosystem
|
| Topic 2: Model Development and Deployment | 30% | - Model training
|
| Topic 3: Machine Learning Fundamentals | 20% | - Supervised learning
|
| Topic 4: OCI Data Science Service | 30% | - Projects and notebooks
|
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NEW QUESTION # 72
You are a researcher who requires access to large datasets. Which OCI service would you use?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Find the OCI service for accessing large public datasets.
Evaluate Options:
A: Oracle Databases—General-purpose, not dataset-focused.
B: ADW—Analytics warehouse, not a dataset repository.
C: OCI Data Science—ML platform, not a dataset provider.
D: Oracle Open Data—Free, public datasets (e.g., geospatial).
Reasoning: Open Data provides pre-existing large datasets for research.
Conclusion: D is correct.
OCI documentation states: “Oracle Open Data provides free access to large, curated datasets, such as geospatial data, ideal for researchers.” Databases (A) and ADW (B) are for storage/analytics, Data Science (C) is for ML—not datasets—only Open Data (D) fits.
1: Oracle Cloud Infrastructure Open Data Documentation, "Overview".
NEW QUESTION # 73
What do you use the score.py file for?
Answer: B
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - score.py".
NEW QUESTION # 74
Which feature of Oracle Cloud Infrastructure Data Science provides an interactive coding environment for building and training machine learning models?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the interactive coding environment in OCI Data Science.
Evaluate Options:
A: Model Catalog stores models—not for coding.
B: Jobs run predefined tasks—not interactive.
C: Notebook Sessions provide JupyterLab for coding and training—interactive.
D: Projects organize work—not a coding environment.
Reasoning: Notebook Sessions are OCI’s Jupyter-based tool for interactive ML development.
Conclusion: C is correct.
OCI Data Science Notebook Sessions “provide an interactive JupyterLab environment where data scientists can write code, explore data, and train machine learning models.” Model Catalog (A) is for storage, Jobs (B) for automation, and Projects (D) for organization—only C offers interactivity.
1: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Sessions Overview".
NEW QUESTION # 75
You’re going to create an Oracle Cloud Infrastructure Anomaly Detection model for multivariate dat a. Where do you need to store the training data?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand OCI Anomaly Detection: This service trains models to detect anomalies in multivariate data (e.g., sensor readings), requiring data to be accessible within OCI’s ecosystem.
Assess Storage Requirements: The training data must be in a scalable, OCI-compatible location that the Anomaly Detection service can access programmatically.
Evaluate Options:
A . Your local machine: Data on a local machine isn’t directly accessible to OCI services without upload, making it impractical for cloud-based training.
B . MySQL database: While OCI supports MySQL, Anomaly Detection doesn’t natively integrate with it for training data; it prefers file-based input.
C . Autonomous Data Warehouse: This is a database for analytics, not the default storage for Anomaly Detection training data, which expects CSV/JSON files.
D . Object Storage Bucket: OCI Object Storage is a scalable, durable storage service that Anomaly Detection uses to ingest training data (e.g., CSV files).
Reasoning: Object Storage is the standard for large-scale data in OCI services, offering seamless integration with Anomaly Detection via APIs or SDKs.
Conclusion: D is the correct choice as it aligns with the service’s architecture.
The OCI Anomaly Detection service requires training data to be uploaded to an Object Storage Bucket in formats like CSV or JSON. This is explicitly outlined in the official documentation, which states that users must “upload the training dataset to an OCI Object Storage bucket” before creating a data asset for model training. Local storage (A) isn’t viable for cloud processing, and databases like MySQL (B) or Autonomous Data Warehouse (C) aren’t supported as primary inputs. Object Storage (D) provides the scalability and accessibility needed for multivariate anomaly detection workflows.
1: Oracle Cloud Infrastructure Anomaly Detection Documentation, "Preparing Training Data" section.
NEW QUESTION # 76
You are a data scientist designing an air traffic control model, and you choose to leverage Oracle AutoML. You understand that the Oracle AutoML pipeline consists of multiple stages and automatically operates in a certain sequence. What is the correct sequence for the Oracle AutoML pipeline?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Sequence OCI AutoML pipeline stages.
Stages:
Adaptive sampling: Reduces data size if large.
Feature selection: Picks relevant features.
Algorithm selection: Chooses best model type.
Hyperparameter tuning: Optimizes model params.
Evaluate: C (sampling, features, algorithms, tuning) matches logical flow—data first, then model.
Reasoning: Sampling precedes feature work—standard in OCI.
Conclusion: C is correct.
OCI documentation states: “AutoML pipeline runs 1) adaptive sampling, 2) feature selection, 3) algorithm selection, 4) hyperparameter tuning (C).” Sampling reduces data first, then features and models are optimized—other orders (A, B, D) misalign with OCI’s sequence.
1: Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Sequence".
NEW QUESTION # 77
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