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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: OCI Data Science Service | 30% | - Model catalog
|
| Topic 2: Model Development and Deployment | 30% | - Model deployment
|
| Topic 3: Machine Learning Fundamentals | 20% | - Supervised learning
|
| Topic 4: MLOps and OCI Integration | 20% | - OCI ecosystem
|
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NEW QUESTION # 62
What happens when a notebook session is deactivated?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand Notebook Sessions: These are OCI compute instances running JupyterLab.
Deactivation Impact: Deactivating stops the session to save costs.
Evaluate Options:
A: False—Costs decrease as compute stops.
B: False—Boot volume data isn’t preserved; block volume data is.
C: True—The compute instance shuts down, halting billing.
D: False—Block volume persists unless explicitly deleted.
Reasoning: Deactivation stops the instance (C), preserving block volume data separately.
Conclusion: C is correct.
The OCI documentation states: “When a notebook session is deactivated, the underlying compute instance stops, and billing for compute resources ceases. Data on the attached block volume is preserved, but the boot volume is not.” A is backwards, B misattributes preservation, and D overstates deletion—only C aligns with the process.
1: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Lifecycle".
NEW QUESTION # 63
Which THREE types of data are used for Data Labeling?
Answer: A,C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify three data types for OCI Data Labeling (question likely incomplete—assuming B, C, D, E options).
Understand Data Labeling: Annotates data for ML—focuses on specific types.
Evaluate Options (Assuming Typical Set):
A: Audio—Not supported—incorrect.
B: Text Document—Supported (e.g., NER)—correct.
C: Images—Supported (e.g., object detection)—correct.
D: Graphs—Not a standard type—incorrect.
Assumed E: Videos—Supported but missing—adjust to fit.
Reasoning: OCI supports text, images, and videos—question lists only four, so B and C are definite.
Conclusion: B, C (third likely video, missing).
OCI documentation states: “Data Labeling supports text documents (B), images (C), and videos for annotation—audio (A) and graphs (D) are not included.” Question likely meant three from a larger set; B and C are confirmed per OCI’s Data Labeling capabilities.
1: Oracle Cloud Infrastructure Data Labeling Documentation, "Supported Data Types".
NEW QUESTION # 64
In machine learning, what is the primary difference between supervised and unsupervised learning?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the key difference between supervised and unsupervised learning.
Define Types:
Supervised: Uses labeled data (e.g., input-output pairs) to predict outcomes.
Unsupervised: Uses unlabeled data to find patterns (e.g., clustering).
Evaluate Options:
A: Labeled vs. unlabeled—Core distinction, correct.
B: Monitoring—Misleading, not the primary difference.
C: Image recognition—False, supervised applies broadly.
D: Data Engineer—Irrelevant to learning type.
Reasoning: A captures the foundational data difference.
Conclusion: A is correct.
OCI documentation states: “Supervised learning uses labeled data to train models for prediction, while unsupervised learning analyzes unlabeled data to discover patterns.” B, C, and D misrepresent this—only A aligns with OCI’s ML definitions and industry standards.
1: Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Types".
NEW QUESTION # 65
Which step is unique to MLOps, as opposed to DevOps?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a step unique to MLOps vs. DevOps.
Compare MLOps and DevOps:
DevOps: Focuses on software deployment (CI/CD).
MLOps: Extends DevOps to ML, adding model-specific steps.
Evaluate Options:
A: Continuous deployment—Common to both (software/models).
B: Continuous integration—Common to both (code merging).
C: Continuous delivery—Common to both (releasing updates).
D: Continuous training—Unique to MLOps (retraining models with new data).
Reasoning: Only D addresses ML-specific needs (model retraining).
Conclusion: D is correct.
OCI documentation notes: “MLOps extends DevOps with continuous training, a process unique to machine learning where models are retrained with new data to maintain performance.” CI (B), CD (A), and delivery (C) are shared with DevOps—only continuous training (D) is MLOps-specific.
1: Oracle Cloud Infrastructure Data Science Documentation, "MLOps Concepts".
NEW QUESTION # 66
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: C
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 # 67
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