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| Section | Weight | Objectives |
|---|---|---|
| OCI Data Science Service | 30% | - Projects and notebooks
|
| MLOps and OCI Integration | 20% | - Automation and pipelines
|
| Model Development and Deployment | 30% | - Model training
|
| Machine Learning Fundamentals | 20% | - Supervised learning
|
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NEW QUESTION # 89
Which OCI service provides a scalable environment for developers and data scientists to run Apache Spark applications at scale?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the OCI service for scalable Spark applications.
Evaluate Options:
A: Data Science—ML platform, not Spark-focused.
B: Anomaly Detection—Specific ML service, not general Spark.
C: Data Labeling—Annotation tool, not Spark-related.
D: Data Flow—Managed Spark service for big data.
Reasoning: Data Flow is OCI’s Spark execution engine.
Conclusion: D is correct.
OCI Data Flow “provides a fully managed environment to run Apache Spark applications at scale, ideal for data processing and ML tasks.” Data Science (A) supports Spark in notebooks, but Data Flow (D) is the dedicated, scalable solution—B and C are unrelated.
1: Oracle Cloud Infrastructure Data Flow Documentation, "Overview".
NEW QUESTION # 90
You have been given a collection of digital files required for a business audit. They consist of several different formats that you would like to annotate using Oracle Cloud Infrastructure (OCI) Data Labeling. Which THREE types of files could this tool annotate?
Answer: A,C,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand OCI Data Labeling Capabilities: OCI Data Labeling is designed to annotate data for machine learning, supporting specific file types like images, text documents, and videos.
Evaluate Options:
A . Video footage: Supported for tasks like object detection or action recognition.
B . Images: Supported for image classification, object detection, etc.
C . Typewritten document: Supported as text data for tasks like entity extraction or classification.
D . Purchase orders: While potentially text-based, this is ambiguous without format clarification (e.g., PDF, image). OCI supports text annotation, but “purchase orders” isn’t a specific file type—it’s assumed as text here.
E . Audio recording: Not supported, as OCI Data Labeling focuses on visual and textual data, not audio.
Select Three: A (video), B (images), and C (text documents) are explicitly supported file types.
OCI Data Labeling supports annotating datasets of images, text, and videos, as per the official documentation. Video footage (A) can be annotated for tasks like object tracking, images (B) for classification or detection, and typewritten documents (C) for text-based annotations (e.g., named entity recognition). Audio files (E) are not supported, and while purchase orders (D) could be text, the question specifies “typewritten document” as a clearer match. (Oracle Cloud Infrastructure Data Labeling Service Documentation, "Supported Data Types").
NEW QUESTION # 91
You want to make your model more frugal to reduce the cost of collecting and processing dat a. You plan to do this by removing features that are highly correlated. You would like to create a heatmap that displays the correlation so that you can identify candidate features to remove. Which Accelerated Data Science (ADS) SDK method is appropriate to display the comparability between Continuous and Categorical features?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Visualize correlation between continuous and categorical features.
Evaluate Options:
A: Pearson—Continuous vs. continuous—incorrect.
B: Cramer’s V—Categorical vs. categorical—incorrect.
C: Correlation ratio—Continuous vs. categorical—correct.
D: General correlation—Not specific to mixed types.
Reasoning: Correlation ratio handles mixed feature types for heatmaps.
Conclusion: C is correct.
OCI documentation states: “correlation_ratio_plot() (C) in ADS SDK visualizes correlations between continuous and categorical features, ideal for mixed-type heatmaps.” Pearson (A) and Cramer’s (B) are type-specific, corr() (D) is broad—only C fits per ADS capabilities.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization".
NEW QUESTION # 92
You are a data scientist; you use the Oracle Cloud Infrastructure (OCI) Language service to train custom models. Which types of custom models can be trained?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify custom model types for OCI Language.
Understand OCI Language: Focuses on text analysis.
Evaluate Options:
A: Image classification—Not text-based, incorrect.
B: Text classification, NER—Both text tasks—correct.
C: Sentiment, NER—Sentiment is pretrained, not custom.
D: Object detection—Image-based, incorrect.
Reasoning: B aligns with OCI Language’s text custom models.
Conclusion: B is correct.
OCI Language documentation states: “Custom models can be trained for text classification and Named Entity Recognition (NER) using your data.” Image tasks (A, D) are for Vision, and sentiment (C) is pretrained—only B fits OCI Language’s scope.
1: Oracle Cloud Infrastructure Language Documentation, "Custom Model Training".
NEW QUESTION # 93
Which statement about Oracle Cloud Infrastructure Data Science Jobs is true?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a true statement about OCI Data Science Jobs.
Understand OCI Jobs: Jobs automate ML tasks (e.g., training) on managed infrastructure.
Evaluate Options:
A: True—Jobs provision OCI compute resources on-demand for task execution.
B: False—Users define custom tasks (e.g., Python scripts), not limited to standard ones.
C: False—Infrastructure is fully managed by OCI, not user-managed.
D: False—Multiple artifacts (e.g., ZIP with dependencies) can be used, not just one file.
Reasoning: A reflects OCI’s managed, on-demand provisioning model for Jobs.
Conclusion: A is correct.
The OCI Data Science documentation states: “Jobs provision compute infrastructure on-demand to execute user-defined tasks, such as model training or data processing, on fully managed OCI resources.” B is incorrect (customization is allowed), C contradicts the managed nature, and D misstates artifact flexibility—only A accurately describes Jobs.
1: Oracle Cloud Infrastructure Data Science Documentation, "Jobs Overview".
NEW QUESTION # 94
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