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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: MLOps and OCI Integration | 20% | - Automation and pipelines
|
| Topic 2: Model Development and Deployment | 30% | - Model training
|
| Topic 3: OCI Data Science Service | 30% | - Model catalog
|
| Topic 4: Machine Learning Fundamentals | 20% | - Supervised learning
|
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NEW QUESTION # 155
You want to create an anomaly detection model using the OCI Anomaly Detection service that avoids as many false alarms as possible. False Alarm Probability (FAP) indicates model performance. How would you set the value of the False Alarm Probability?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Minimize false alarms in OCI Anomaly Detection.
Understand FAP: False Alarm Probability—lower FAP means fewer false positives.
Evaluate Options:
A: High FAP—Increases false alarms—incorrect.
B: Low FAP—Reduces false alarms—correct.
C: Zero FAP—Unrealistic; risks missing true anomalies.
D: Function—Vague, not a direct setting.
Reasoning: Low FAP balances sensitivity and false positives— aligns with goal.
Conclusion: B is correct.
OCI Anomaly Detection documentation states: “Set a low False Alarm Probability (FAP) to minimize false positives, though too low (e.g., zero) may miss anomalies.” B fits the goal—high (A) increases errors, zero (C) is impractical, and function (D) isn’t specified.
1: Oracle Cloud Infrastructure Anomaly Detection Documentation, "Configuring FAP".
NEW QUESTION # 156
During a job run, you receive an error message that no space is left on your disk device. To solve the problem, you must increase the size of the job storage. What would be the most efficient way to do this with Data Science Jobs?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Efficiently increase storage for an OCI Job.
Understand Jobs: Storage (block volume) is set at job creation, not dynamically adjustable.
Evaluate Options:
A: False—Jobs can’t edit storage post-creation; it’s fixed.
B: False—No environment variable adjusts storage size.
C: True—Create a new job with larger storage (e.g., 200 GB) and run it.
D: False—Refactoring code is inefficient compared to increasing storage.
Reasoning: C is the standard OCI process for adjusting resources.
Conclusion: C is correct.
OCI documentation states: “Storage size for a Data Science Job is specified during job creation (e.g., block volume size). To increase it, create a new job with a larger storage configuration and initiate a new run.” Editing (A) isn’t supported, variables (B) don’t apply, and refactoring (D) avoids the issue—only C is efficient.
1: Oracle Cloud Infrastructure Data Science Documentation, "Jobs - Storage Configuration".
NEW QUESTION # 157
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: B
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".
NEW QUESTION # 158
Where do calls to stdout and stderr from score.py go in a model deployment?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Locate score.py output in OCI model deployment.
Understand Deployment: Logs are centralized in OCI Logging.
Evaluate Options:
A: VM file—Not default; requires custom config—incorrect.
B: Predict log in OCI Logging—Standard destination—correct.
C: Cloud Shell—Separate tool, not logs—incorrect.
D: Console—UI, not raw logs—incorrect.
Reasoning: B aligns with OCI’s logging integration.
Conclusion: B is correct.
OCI documentation states: “score.py stdout and stderr are captured in the predict log within OCI Logging service (B), configured during deployment.” A isn’t standard, C and D don’t receive logs—only B fits OCI’s logging setup.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Logging".
NEW QUESTION # 159
You are a researcher who requires access to large datasets. Which OCI service would you use?
Answer: D
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 # 160
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