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| Section | Weight | Objectives |
|---|---|---|
| MLOps and OCI Integration | 20% | - OCI ecosystem
|
| Machine Learning Fundamentals | 20% | - Unsupervised learning
|
| Model Development and Deployment | 30% | - Model deployment
|
| OCI Data Science Service | 30% | - Projects and notebooks
|
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NEW QUESTION # 159
How are datasets exported in the OCI Data Labeling service?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand OCI Data Labeling Export: After annotation, datasets are exported for ML use.
Check Supported Formats: OCI Data Labeling exports annotations in a structured, machine-readable format.
Evaluate Options:
A: Binary isn’t a standard export format for annotations.
B: XML isn’t used; JSON is preferred for flexibility.
C: Line-delimited JSON is the correct format, aligning with ML workflows.
D: CSV is common but not the default for OCI Data Labeling.
Conclusion: C matches the official export format.
OCI Data Labeling exports annotated datasets as line-delimited JSON files, which store each annotation as a separate JSON object per line, suitable for ML pipelines. This is explicitly stated in the documentation. (Oracle Cloud Infrastructure Data Labeling Service Documentation, "Exporting Datasets").
NEW QUESTION # 160
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?
Answer: A
Explanation:
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.
NEW QUESTION # 161
You have trained three different models on your dataset using Oracle AutoML. You want to visualize the behavior of each of the models, including the baseline model, on the test set. Which class should be used from the Accelerated Data Science (ADS) SDK to visually compare the models?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the ADS SDK class for visualizing model performance comparison.
Understand ADS Classes: Each serves a specific ML purpose—visualization requires evaluation tools.
Evaluate Options:
A . EvaluationMetrics: Likely a typo—meant EvaluationsMetrics? Not a standalone class for visualization.
B . ADSEvaluator: Designed to evaluate and visualize model performance (e.g., ROC curves)—correct.
C . ADSExplainer: Explains model predictions (e.g., SHAP), not comparative visualization.
D . ADSTuner: Tunes hyperparameters, not for visualization.
Reasoning: ADSEvaluator provides comparative plots (e.g., precision-recall) for multiple models, including baselines.
Conclusion: B is correct.
OCI documentation states: “The ADSEvaluator class in ADS SDK (B) enables visualization of model performance metrics, such as ROC curves and confusion matrices, for multiple models on a test set, including baselines.” EvaluationMetrics (A) isn’t a class, ADSExplainer (C) focuses on interpretability, and ADSTuner (D) is for tuning—only B fits the visualization need per OCI’s ADS toolkit.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "ADSEvaluator Class".
NEW QUESTION # 162
Which of the following TWO non-open source JupyterLab extensions has Oracle Cloud Infrastructure (OCI) Data Science developed and added to the notebook session experience?
Answer: C,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify two OCI-developed, non-open-source JupyterLab extensions.
Understand Extensions: OCI enhances JupyterLab with proprietary tools.
Evaluate Options:
A: Environment Explorer—OCI-specific, non-open—correct.
B: Table of Contents—Open-source Jupyter—incorrect.
C: Command Palette—Open-source Jupyter—incorrect.
D: Notebook Examples—OCI-specific, non-open—correct.
E: Terminal—Open-source Jupyter—incorrect.
Reasoning: A and D are OCI proprietary; others are standard JupyterLab.
Conclusion: A and D are correct.
OCI documentation states: “OCI Data Science adds non-open-source extensions like Environment Explorer (A) for conda management and Notebook Examples (D) for sample code—both proprietary enhancements.” B, C, and E are open-source JupyterLab defaults—only A and D are OCI-specific per the notebook session design.
1: Oracle Cloud Infrastructure Data Science Documentation, "JupyterLab Extensions".
NEW QUESTION # 163
How can you convert a fixed load balancer to a flexible load balancer?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Convert fixed to flexible load balancer in OCI.
Understand Load Balancers: Fixed (e.g., 10 Mbps) vs. flexible (dynamic shapes).
Evaluate Options:
A: False—Conversion possible via recreation.
B: Update Shape—For flexible only, not conversion.
C: Delete and recreate—Standard method—correct.
D: Edit Listener—Configures rules, not type.
Reasoning: OCI requires new creation for type change.
Conclusion: C is correct.
OCI documentation states: “To change from a fixed to a flexible load balancer, delete the existing fixed load balancer and create a new flexible one (C)—direct conversion isn’t supported.” A is too absolute, B and D don’t apply—only C matches OCI’s process.
1: Oracle Cloud Infrastructure Load Balancing Documentation, "Changing Load Balancer Type".
NEW QUESTION # 164
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