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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: MLOps and OCI Integration | 20% | - Automation and pipelines
|
| Topic 2: Machine Learning Fundamentals | 20% | - Supervised learning
|
| Topic 3: OCI Data Science Service | 30% | - Projects and notebooks
|
| Topic 4: Model Development and Deployment | 30% | - Model training
|
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NEW QUESTION # 145
Which is NOT a part of Observability and Management Services?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the non-Observability and Management (O&M) service in OCI.
Understand O&M: Includes monitoring, logging, events tools.
Evaluate Options:
A: Event Services—Triggers actions, part of O&M—correct.
B: OCI Management Service—Not a defined O&M service—incorrect.
C: Logging Analytics—Log analysis, O&M component—correct.
D: Logging—Log collection, O&M component—correct.
Reasoning: B isn’t listed in OCI’s O&M suite—others are.
Conclusion: B is correct (not part of O&M).
OCI documentation lists “Observability and Management Services as including Event Services (A), Logging Analytics (C), and Logging (D)—‘OCI Management Service’ (B) is not a recognized component.” B appears to be a misnomer—only A, C, D are O&M per OCI’s service catalog.
1: Oracle Cloud Infrastructure Observability and Management Documentation, "Service Overview".
NEW QUESTION # 146
You have created a conda environment in your notebook session. This is the first time you are working with published conda environments. You have also created an Object Storage bucket with permission to manage the bucket. Which TWO commands are required to publish the conda environment?
Answer: C,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Publish a conda env to Object Storage.
Process: Initialize bucket config, then publish env.
Evaluate Options:
A: Publishes env with slug—correct final step.
B: Lists envs—unrelated to publishing.
C: Sets bucket details—required setup—correct.
D: Creates env—not publishing.
E: Activates env—not for sharing.
Reasoning: C sets up, A executes—standard workflow.
Conclusion: A and C are correct.
OCI documentation states: “To publish a conda environment, first run odsc conda init (C) with bucket namespace and name, then odsc conda publish (A) with a slug to upload to Object Storage.” B, D, and E serve other purposes—only A and C are required per OCI’s process.
1: Oracle Cloud Infrastructure Data Science CLI Reference, "Publishing Conda Environments".
NEW QUESTION # 147
You loaded data into Oracle Cloud Infrastructure (OCI) Data Science. To transform the data, you want to use the Accelerated Data Science (ADS) SDK. When you applied the get_recommendations() tool to the ADSDataset object, it showed you user-detected issues with all the recommended changes to apply to the dataset. Which option should you use to apply all the recommended transformations at once?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Apply all recommended transformations from get_recommendations() in ADS.
Understand ADS Tools: get_recommendations() suggests fixes (e.g., missing values).
Evaluate Options:
A: Returns transformed data—Not for applying—incorrect.
B: Sklearn-style, not ADS-specific—incorrect.
C: auto_transform()—Applies all recommendations—correct.
D: Visualizes, doesn’t apply—incorrect.
Reasoning: auto_transform() executes the fixes suggested by get_recommendations().
Conclusion: C is correct.
OCI documentation states: “After get_recommendations() identifies issues, use auto_transform() (C) on the ADSDataset to apply all recommended transformations at once.” A retrieves, B is external, D visualizes—only C aligns with OCI’s ADS transformation workflow.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Data Transformation Methods".
NEW QUESTION # 148
True or false? Bias is a common problem in data science applications.
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Assess if bias is a common issue in data science.
Define Bias: Systematic errors in data/models (e.g., skewed training data).
Evaluate Statement:
Bias arises from unrepresentative data, poor feature selection, or algorithmic flaws—widely recognized in ML.
Examples: Gender bias in hiring models, racial bias in facial recognition.
Reasoning: Literature and practice (e.g., fairness in AI) confirm bias as prevalent.
Conclusion: A (True) is correct.
OCI documentation notes: “Bias is a common challenge in data science, stemming from imbalanced datasets or flawed assumptions, requiring techniques like re-weighting or fairness checks.” This aligns with industry standards—bias is a well-documented issue, making A true.
1: Oracle Cloud Infrastructure Data Science Documentation, "Addressing Bias in Models".
NEW QUESTION # 149
You are a data scientist working for a utilities company. You have developed an algorithm that detects anomalies from a utility reader in the grid. The size of the model artifact is about 2 GB, and you are trying to store it in the model catalog. Which THREE interfaces could you use to save the model artifact into the model catalog?
Answer: A,B,C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify interfaces to save a 2 GB model to the Model Catalog.
Evaluate Options:
A: OCI CLI—Supports Data Science tasks—possible but not primary.
B: ADS SDK—Designed for model catalog ops—correct.
C: ODSC CLI—Not standard; likely typo for OCI CLI.
D: Console—GUI for catalog uploads—correct.
E: OCI Python SDK—Programmatic catalog access—correct.
F: Git CLI—Version control, not catalog-related.
Reasoning: B, D, E are OCI’s primary interfaces; A is valid but less emphasized.
Conclusion: B, D, E are correct (A plausible but not top-tier).
OCI documentation lists “ADS SDK (B), OCI Console (D), and OCI Python SDK (E) as primary methods to save models to the Model Catalog.” OCI CLI (A) works but isn’t highlighted, C isn’t real, and F is unrelated—B, D, E are the standard trio.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Interfaces".
NEW QUESTION # 150
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