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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?
See the explanation below.
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.
: Oracle Cloud Infrastructure Data Science Documentation, 'JupyterLab Extensions'.
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?
See the explanation below.
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.
: Oracle Cloud Infrastructure ADS SDK Documentation, 'Data Transformation Methods'.
You are a data scientist leveraging the Oracle Cloud Infrastructure (OCI) Language AI service for various types of text analyses. Which TWO capabilities can you utilize with this tool?
See the explanation below.
Detailed Answer in Step-by-Step Solution:
Objective: Identify two OCI Language AI capabilities.
Understand OCI Language: Focuses on text analysis tasks.
Evaluate Options:
A: Table extraction---Vision, not Language---incorrect.
B: Punctuation correction---Not offered---incorrect.
C: Sentence diagramming---Not supported---incorrect.
D: Topic classification---Supported (custom/pretrained)---correct.
E: Sentiment analysis---Supported (pretrained)---correct.
Reasoning: D and E are core text analysis features of OCI Language.
Conclusion: D and E are correct.
OCI documentation states: ''OCI Language offers topic classification (D) and sentiment analysis (E) for text analysis, among other features.'' A belongs to Vision, B and C aren't available---only D and E match OCI Language's capabilities.
: Oracle Cloud Infrastructure Language Documentation, 'Text Analysis Features'.
Which THREE types of data are used for Data Labeling?
See the explanation below.
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.
: Oracle Cloud Infrastructure Data Labeling Documentation, 'Supported Data Types'.
You realize that your model deployment is about to reach its utilization limit. What would you do to avoid the issue before requests start to fail? Which THREE steps would you perform?
See the explanation below.
Detailed Answer in Step-by-Step Solution:
Objective: Prevent deployment failure due to utilization limits.
Understand Utilization: High load requires capacity or throttling.
Evaluate Options:
A: More instances---Scales horizontally---correct.
B: Delete---Stops service, not a fix---incorrect.
C: Fewer instances---Worsens issue---incorrect.
D: Larger VM---Scales vertically---correct.
E: Reduce bandwidth---Controls load---correct.
Reasoning: A and D increase capacity, E manages demand---effective trio.
Conclusion: A, D, E are correct.
OCI documentation states: ''To avoid utilization limits, increase instances (A), use a larger compute shape (D), or reduce load balancer bandwidth (E) to manage request rates.'' B stops service, C reduces capacity---only A, D, E align with OCI's deployment scaling options.
: Oracle Cloud Infrastructure Data Science Documentation, 'Model Deployment Scaling'.
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