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| Section | Weight | Objectives |
|---|---|---|
| Model Development and Deployment | 30% | - Model deployment
|
| MLOps and OCI Integration | 20% | - OCI ecosystem
|
| Machine Learning Fundamentals | 20% | - Supervised learning
|
| OCI Data Science Service | 30% | - Projects and notebooks
|
>> 1Z0-1110-26 Zertifizierungsantworten <<
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70. Frage
What do you use the score.py file for?
Antwort: B
Begründung:
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".
71. Frage
Why is data sampling useful for data scientists?
Antwort: A
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Determine the primary benefit of data sampling.
Define Sampling: Selecting a subset of data to represent the whole—used in ML/statistics.
Evaluate Options:
A: Small batches reduce resources—True but not the main purpose.
B: Reduces storage—Incidental, not the goal.
C: Representative subset for faster, accurate models—Core purpose of sampling.
Reasoning: Sampling speeds up analysis while maintaining accuracy (e.g., training on 10% of data).
Conclusion: C is correct.
OCI documentation states: “Data sampling allows data scientists to use a representative subset of a large dataset to build accurate models more quickly, especially when processing full datasets is impractical.” A focuses on resources (secondary), B on storage (not primary)—only C captures the analytical intent per OCI’s AutoML sampling approach.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Sampling Techniques".
72. Frage
You want to write a Python script to create a collection of different projects for your data science team. Which Oracle Cloud Infrastructure (OCI) Data Science interface would you use?
Antwort: A
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Choose an interface for a Python script to manage projects.
Evaluate Options:
A: OCI SDK—Python-based, scriptable—correct.
B: Console—GUI, not scriptable.
C: CLI—Command-based, not Python-native.
D: Mobile App—Not for scripting.
Reasoning: A enables programmatic project creation.
Conclusion: A is correct.
OCI documentation states: “Use the OCI Python SDK (A) to programmatically manage Data Science resources, like creating projects, via Python scripts.” B, C, and D don’t support Python scripting—only A fits.
1: Oracle Cloud Infrastructure SDK Documentation, "Data Science API".
73. Frage
Which of the following analytical and statistical techniques do data scientists commonly use?
Antwort: B
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Identify common data science techniques.
Define Techniques:
Classification: Predicts categories (e.g., spam vs. not).
Regression: Predicts continuous values (e.g., sales).
Clustering: Groups data (e.g., customer segments).
Evaluate Options:
A, B, C: All are standard ML/statistical methods.
D: Encompasses all—correct as they’re widely used.
Reasoning: These are foundational in data science workflows.
Conclusion: D is correct.
OCI documentation lists “classification, regression, and clustering as core techniques in data science, supported by tools like ADS SDK and AutoML.” All (D) are common per OCI’s ML framework, not just subsets (A, B, C).
1: Oracle Cloud Infrastructure Data Science Documentation, "Analytical Techniques".
74. Frage
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?
Antwort: B
Begründung:
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.
75. Frage
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