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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Development and Deployment | 30% | - Model deployment
|
| Topic 2: Machine Learning Fundamentals | 20% | - Unsupervised learning
|
| Topic 3: MLOps and OCI Integration | 20% | - OCI ecosystem
|
| Topic 4: OCI Data Science Service | 30% | - Model catalog
|
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問題 #155
You are using Oracle Cloud Infrastructure (OCI) Anomaly Detection to train a model to detect anomalies in pump sensor dat a. What are you trying to determine? How does the required False Alarm Probability setting affect an anomaly detection model?
答案：D
解題說明：
Detailed Answer in Step-by-Step Solution:
Objective: Understand FAP’s effect in OCI Anomaly Detection.
Evaluate Options:
A: Disable reporting—Incorrect; FAP sets threshold.
B: Sensitivity—Correct; lower FAP reduces false positives.
C: Error message—Incorrect; not a count mechanism.
D: Score per signal—Incorrect; FAP is a global setting.
Reasoning: FAP adjusts detection threshold—key to sensitivity.
Conclusion: B is correct.
OCI documentation states: “False Alarm Probability (FAP) controls the model’s sensitivity—lower values reduce false positives, higher values increase detection.” B aligns—others misrepresent FAP’s role.
1: Oracle Cloud Infrastructure Anomaly Detection Documentation, "FAP Configuration".
問題 #156
Which of the following analytical and statistical techniques do data scientists commonly use?
答案：D
解題說明：
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".
問題 #157
You want to make API calls against other OCI services from your instance without configuring user credentials. How would you achieve this?
答案：C
解題說明：
Detailed Answer in Step-by-Step Solution:
Objective: Enable credential-less API calls from an instance.
Understand Resource Principal: Allows instances to authenticate via IAM without user creds.
Evaluate Options:
A: Dynamic group + policy—Correct; groups instance, grants access.
B: Dynamic group only—Incomplete; needs policy.
C: User group—Irrelevant for instances.
D: No config—False; setup required.
Reasoning: A sets up resource principal fully—group and perms.
Conclusion: A is correct.
OCI documentation states: “To make API calls without credentials, create a dynamic group including the instance and add a policy (A) granting access to OCI services—enables resource principal.” B lacks policy, C is user-based, D is false—only A completes the process per OCI’s IAM setup.
1: Oracle Cloud Infrastructure IAM Documentation, "Resource Principal Configuration".
問題 #158
Six months ago, you created and deployed a model that predicts customer churn for a call centre. Initially, it was yielding quality predictions. However, over the last two months, users are questioning the credibility of the predictions. Which TWO methods would you employ to verify the accuracy of the model?
答案：C,D
解題說明：
Detailed Answer in Step-by-Step Solution:
Objective: Address declining prediction accuracy and verify model performance.
Analyze Problem: Degradation over time suggests data drift or model staleness—common ML issues.
Evaluate Options:
A . Retrain the model: Uses new data to update the model—fixes accuracy—correct.
B . Validate with recent data: Tests performance but doesn’t fix—diagnostic only.
C . Drift monitoring: Detects data distribution shifts—verifies cause—correct.
D . Redeploy the model: Repeats deployment, doesn’t address root cause.
E . Operational monitoring: Tracks infra (e.g., latency), not prediction accuracy.
Reasoning: C identifies drift (why accuracy dropped), A corrects it—best pair for verification and improvement.
Conclusion: A and C are correct.
OCI documentation states: “Drift monitoring (C) detects changes in data distribution that impact accuracy, while retraining (A) with new data restores model performance.” Validation (B) checks but doesn’t fix, redeployment (D) is redundant, and operational monitoring (E) is infra-focused—only A and C align with OCI’s model maintenance strategy.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".
問題 #159
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?
答案：D
解題說明：
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.
問題 #160
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