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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement End-to-End Machine Learning Lifecycle | 45% | - Model saving, cataloging, and versioning - Data preparation, exploration, and transformation - Model development, training, and evaluation - Deploy models and manage endpoints - Use AutoML and built-in algorithms |
| Topic 2: Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| Topic 3: Apply MLOps Practices | 20% | - ML pipelines, automation, and reproducibility - Governance, auditing, and compliance - Model monitoring, drift detection, and performance tracking |
| Topic 4: OCI Data Science - Introduction & Configuration | 10% | - Tenancy and environment configuration for Data Science - Overview and core concepts of OCI Data Science - Capabilities of the Accelerated Data Science (ADS) SDK |
| Topic 5: Design and Set Up Data Science Workspace | 15% | - Manage access control, security, and IAM integration - Configure compute shapes, storage, and networking - Create and manage projects and notebook sessions |
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NEW QUESTION # 45
As a data scientist, you are working on a global health dataset that has data from more than 50 countries. You want to encode three features, such as 'countries', 'race', and 'body organ' as categories. Which option would you use to encode the categorical feature?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Encode categorical features in a Data Science context (likely ADS SDK).
Understand Encoding: Converts categories (e.g., countries) to numerical forms.
Evaluate Options:
A: Not a standard ADS method—incorrect.
B: General transformation, not specific encoding—incorrect.
C: OneHotEncoder—Standard for categorical encoding—correct.
D: Visualization, not encoding—incorrect.
Reasoning: One-hot encoding creates binary columns—ideal for multiple categories.
Conclusion: C is correct.
OCI documentation states: “In ADS SDK, use OneHotEncoder (C) from sklearn (or similar) to encode categorical features like ‘countries’ into binary vectors for modeling.” A isn’t real, B is too broad, D is unrelated—only C fits OCI’s encoding practice.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Encoding with ADS".
NEW QUESTION # 46
You want to write a Python script to create a collection of different projects for your data science team. Which Oracle Cloud Infrastructure (OCI) Data Science interface would you use?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Choose an interface for a Python script to manage projects.
Evaluate Options:
A: OCI SDK—Python-based, scriptable—correct.
B: Console—GUI, not scriptable.
C: CLI—Command-based, not Python-native.
D: Mobile App—Not for scripting.
Reasoning: A enables programmatic project creation.
Conclusion: A is correct.
OCI documentation states: “Use the OCI Python SDK (A) to programmatically manage Data Science resources, like creating projects, via Python scripts.” B, C, and D don’t support Python scripting—only A fits.
1: Oracle Cloud Infrastructure SDK Documentation, "Data Science API".
NEW QUESTION # 47
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 # 48
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,D,E
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 # 49
You are asked to prepare data for a custom-built model that requires transcribing Spanish video recordings into a readable text format with profane words identified. Which Oracle Cloud Service would you use?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Transcribe Spanish video audio and identify profanity.
Evaluate Options:
A: Anomaly Detection—Not for transcription or text analysis.
B: Speech—Converts audio to text (e.g., Spanish), base for further analysis—correct.
C: Translation—Translates text, not transcription.
D: Language—Analyzes text (e.g., profanity), but needs transcribed input.
Reasoning: Speech (B) transcribes video audio; Language could follow for profanity.
Conclusion: B is correct for transcription.
OCI Speech “transcribes audio from video or audio files into text, supporting languages like Spanish.” Post-transcription, OCI Language could detect profanity, but B is the starting point—Anomaly (A) and Translation (C) don’t fit.
1: Oracle Cloud Infrastructure Speech Documentation, "Transcription Features".
NEW QUESTION # 50
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