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| Section | Weight | Objectives |
|---|---|---|
| OCI Data Science Service | 30% | - Model catalog
|
| MLOps and OCI Integration | 20% | - OCI ecosystem
|
| Model Development and Deployment | 30% | - Model deployment
|
| Machine Learning Fundamentals | 20% | - Unsupervised learning
|
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問題 #48
Which statement about logs for Oracle Cloud Infrastructure Jobs is true?
答案：B
解題說明：
Detailed Answer in Step-by-Step Solution:
Objective: Identify a true statement about OCI Jobs logging.
Understand Logging: Jobs can log stdout/stderr to OCI Logging service.
Evaluate Options:
A: False—Each run has its own log, not a single job log.
B: False—Logging is optional, not mandatory.
C: True—When enabled, stdout/stderr are auto-captured.
D: False—Logs persist unless explicitly deleted.
Reasoning: C matches OCI’s automatic logging feature.
Conclusion: C is correct.
OCI documentation states: “When automatic log creation is enabled for Data Science Jobs, all stdout and stderr outputs are captured and stored in the OCI Logging service.” A is incorrect (per-run logs), B is optional, and D contradicts log retention—only C is accurate.
1: Oracle Cloud Infrastructure Data Science Documentation, "Jobs Logging".
問題 #49
You are using Oracle Cloud Infrastructure (OCI) Anomaly Detection to train a model to detect anomalies in pump sensor dat a. 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 the effect of False Alarm Probability (FAP) in OCI Anomaly Detection.
Understand FAP: Controls false positive rate—threshold for anomaly flagging.
Evaluate Options:
A: Disable reporting—Incorrect; FAP sets sensitivity, not on/off.
B: Changes sensitivity—Correct; lower FAP = fewer false positives—correct.
C: Count-based error—Incorrect; not a counter.
D: Score per signal—Incorrect; FAP is a global setting.
Reasoning: FAP adjusts detection threshold—direct impact on sensitivity.
Conclusion: B is correct.
OCI documentation states: “False Alarm Probability (FAP) (B) adjusts the model’s sensitivity in Anomaly Detection—lower values increase specificity, reducing false positives.” A, C, and D misinterpret FAP’s role—only B aligns with OCI’s anomaly detection tuning.
1: Oracle Cloud Infrastructure Anomaly Detection Documentation, "FAP Settings".
問題 #50
While working with Git on Oracle Cloud Infrastructure (OCI) Data Science, you notice that two of the operations are taking more time than the others due to your slow internet speed. Which TWO operations would experience the delay?
答案：A,E
解題說明：
Detailed Answer in Step-by-Step Solution:
Analyze Git Operations: Identify which depend on internet speed.
Evaluate Options:
A . Staging (git add): Local operation—adds files to the index; no network involved.
B . Updating local repo (git pull): Downloads remote changes—requires internet, slowed by poor connectivity.
C . Pushing changes (git push): Uploads local commits to remote—network-dependent, delayed by slow speed.
D . Committing (git commit): Local snapshot—no network needed.
E . Converting to Git repo (git init): Local initialization—no internet required.
Reasoning: Only B and C involve network transfers, directly impacted by slow internet.
Conclusion: B and C are the correct choices.
Git operations like git pull (B) and git push (C) rely on network communication with a remote repository, such as OCI Code Repository, and are documented as “bandwidth-sensitive” in OCI’s guides. Local actions like staging (A), committing (D), and initializing (E) occur on the user’s machine, unaffected by internet speed. This matches standard Git behavior and OCI’s implementation.
1: Oracle Cloud Infrastructure Data Science Documentation, "Using Git in Notebook Sessions".
問題 #51
You want to build a multistep machine learning workflow by using the Oracle Cloud Infrastructure (OCI) Data Science Pipeline feature. How would you configure the conda environment to run a pipeline step?
答案：C
解題說明：
Detailed Answer in Step-by-Step Solution:
Objective: Configure conda env for a pipeline step.
Evaluate Options:
A: Shape—Infra, not env config.
B: Volume—Storage, not env.
C: Command-line—Step args, not env.
D: Env variables—Sets conda path—correct.
Reasoning: D specifies runtime env (e.g., CONDA_ENV_SLUG).
Conclusion: D is correct.
OCI documentation states: “Configure a pipeline step’s conda environment using environment variables (D), such as CONDA_ENV_SLUG, in the step definition.” A, B, and C address other aspects—only D fits env config.
1: Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Step Configuration".
問題 #52
You are a data scientist leveraging the Oracle Cloud Infrastructure (OCI) Language AI service for various types of text analyses. Which TWO capabilities can you utilize with this tool?
答案：A,B
解題說明：
Detailed Answer in Step-by-Step Solution:
Objective: Identify two OCI Language AI capabilities.
Understand OCI Language: Focuses on text analysis tasks.
Evaluate Options:
A: Table extraction—Vision, not Language—incorrect.
B: Punctuation correction—Not offered—incorrect.
C: Sentence diagramming—Not supported—incorrect.
D: Topic classification—Supported (custom/pretrained)—correct.
E: Sentiment analysis—Supported (pretrained)—correct.
Reasoning: D and E are core text analysis features of OCI Language.
Conclusion: D and E are correct.
OCI documentation states: “OCI Language offers topic classification (D) and sentiment analysis (E) for text analysis, among other features.” A belongs to Vision, B and C aren’t available—only D and E match OCI Language’s capabilities.
1: Oracle Cloud Infrastructure Language Documentation, "Text Analysis Features".
問題 #53
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