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| Section | Objectives |
|---|---|
| Apply MLOps Practices | - Monitor and maintain machine learning models - Use best practices for operationalizing ML solutions - Implement model lifecycle management |
| Implement End-to-End Machine Learning Lifecycle | - Prepare and manage datasets - Save and manage models using Model Catalog - Deploy models and consume model endpoints - Build, train, and evaluate machine learning models - Automate machine learning workflows and pipelines |
| Design and Set Up Data Science Workspace | - Create and configure Data Science projects - Use Accelerated Data Science SDK and open source tools - Manage notebook sessions and compute resources |
| Use Related OCI Services | - Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases - Integrate OCI Data and AI services |
| OCI Data Science - Introduction and Configuration | - Configure and manage Data Science resources - Use OCI Data Science notebooks and sessions - Understand OCI Data Science service concepts and architecture |
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NEW QUESTION # 155
The feature type TechJob has the following registered validators:
TechJob.validator.register(name=’is_tech_job’, handler=is_tech_job_default_handler) TechJob.validator.register(name=’is_tech_job’, handler=is_tech_job_open_handler, condition=('job_family',)) TechJob.validator.register(name=’is_tech_job’, handler=is_tech_job_closed_handler, condition=('job_family': 'IT'))When you run is_tech_job(job_family='Engineering'), what does the feature type validator system do?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine which validator handler runs for is_tech_job(job_family='Engineering').
Understand Validator System: Likely ADS SDK—executes handlers based on conditions.
Analyze Validators:
Default: is_tech_job_default_handler—No condition, fallback.
Open: is_tech_job_open_handler—Condition ('job_family',)—requires job_family arg.
Closed: is_tech_job_closed_handler—Condition ('job_family': 'IT')—requires job_family='IT'.
Evaluate Call: job_family='Engineering'—Matches job_family presence, not IT.
Reasoning:
Open handler applies (tuple condition means arg exists).
Closed fails (Engineering ≠ IT).
Default is overridden by specific matches.
Conclusion: D is correct.
OCI ADS documentation states: “Validators execute the most specific handler matching the condition; for is_tech_job(job_family='Engineering'), is_tech_job_open_handler (D) runs as it matches job_family presence, while is_tech_job_closed_handler (C) requires IT—default (A) is bypassed, no error (B).” Only D fits per ADS validator logic.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Feature Type Validators".
NEW QUESTION # 156
You are working as a data scientist for a healthcare company. They decided to analyze the data to find patterns in a large volume of electronic medical records. You are asked to build a PySpark solution to analyze these records in a JupyterLab notebook. What is the order of recommended steps to develop a PySpark application in OCI Data Science?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Sequence steps for a PySpark app in OCI Data Science.
Evaluate Steps:
Launch notebook: First—provides the environment.
Install PySpark conda: Second—sets up Spark libraries.
Configure core-site.xml: Third—connects to data (e.g., Object Storage).
Develop app: Fourth—writes the PySpark code.
Data Flow: Fifth—optional scaling, post-development.
Check Options: D (1, 2, 3, 4, 5) matches this logical flow.
Reasoning: Notebook first, then setup, coding, and scaling.
Conclusion: D is correct.
OCI documentation recommends: “1) Launch a notebook session, 2) install a PySpark conda environment, 3) configure core-site.xml for data access, 4) develop your PySpark application, and 5) optionally use Data Flow for scale.” D follows this—others (A, B, C) misorder critical steps like launching the notebook.
1: Oracle Cloud Infrastructure Data Science Documentation, "PySpark in Notebooks".
NEW QUESTION # 157
Which of these options allow the sharing and loading back of ML models into a notebook session?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the mechanism for sharing and reloading ML models in OCI Data Science.
Evaluate Options:
A . Model provenance: Tracks model origin—informative but not a sharing mechanism.
B . Model taxonomy: Categorizes models (e.g., regression)—not for sharing/loading.
C . Model deployment: Makes models accessible as endpoints, not for notebook reloading.
D . Model catalog: Stores models and artifacts, enabling sharing and loading into sessions.
Reasoning: The Model Catalog is OCI’s centralized repository for saving, sharing, and retrieving models (e.g., via ADS SDK).
Conclusion: D is the correct tool.
The OCI Model Catalog “enables data scientists to save trained models and their artifacts, share them with team members, and load them back into notebook sessions for further use or evaluation.” Provenance (A) and taxonomy (B) are metadata, while deployment (C) serves inference, not notebook access. D is explicitly designed for this purpose.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Usage".
NEW QUESTION # 158
You are a data scientist trying to load data into your notebook session. You understand that Accelerated Data Science (ADS) SDK supports loading various data formats. Which of the following THREE are ADS-supported data formats?
Answer: B,D,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify three data formats supported by ADS SDK for loading data.
Understand ADS SDK: Facilitates data loading into notebook sessions via DatasetFactory.
Evaluate Options:
A . DOCX: Not natively supported—requires conversion (e.g., to text).
B . Pandas DataFrame: Supported—core format for data manipulation in ADS.
C . JSON: Supported—common structured data format.
D . Raw Images: Not directly supported—image data needs preprocessing (e.g., via Vision).
E . XML: Supported—parseable structured format.
Reasoning: ADS focuses on tabular/structured data—B, C, E align; A and D require external handling.
Conclusion: B, C, E are correct.
OCI documentation states: “ADS SDK’s DatasetFactory supports loading data from formats like Pandas DataFrames (B), JSON (C), and XML (E), enabling easy integration into notebook sessions.” DOCX (A) isn’t natively handled, and raw images (D) require preprocessing outside ADS—B, C, E match the supported list.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Supported Data Formats".
NEW QUESTION # 159
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which TWO of the following prevailing metrics would you use for evaluating a multiclass classification model?
Answer: A,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand Multiclass Classification: Metrics evaluate how well the model predicts multiple classes.
Evaluate Metrics:
A . Mean squared error: Used for regression, not classification.
B . Explained variance score: Regression metric, not suitable.
C . Recall: Measures true positive rate per class—key for classification.
D . F1-score: Balances precision and recall—widely used in multiclass.
E . R-squared: Regression metric, not applicable.
Select Two: Recall (C) and F1-score (D) are standard for multiclass classification.
Oracle AutoML supports metrics like recall and F1-score for multiclass classification, as they assess per-class performance and overall precision-recall balance, respectively. Regression metrics (A, B,E) are irrelevant here. (Oracle Cloud Infrastructure Data Science Documentation, "AutoML Metrics").
NEW QUESTION # 160
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