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| Section | Weight | Objectives |
|---|---|---|
| Implement End-to-End Machine Learning Lifecycle | 45% | - Model saving, cataloging, and versioning - Use AutoML and built-in algorithms - Deploy models and manage endpoints - Model development, training, and evaluation - Data preparation, exploration, and transformation |
| Apply MLOps Practices | 20% | - ML pipelines, automation, and reproducibility - Model monitoring, drift detection, and performance tracking - Governance, auditing, and compliance |
| Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| Design and Set Up Data Science Workspace | 15% | - Manage access control, security, and IAM integration - Create and manage projects and notebook sessions - Configure compute shapes, storage, and networking |
| OCI Data Science - Introduction & Configuration | 10% | - Overview and core concepts of OCI Data Science - Capabilities of the Accelerated Data Science (ADS) SDK - Tenancy and environment configuration for Data Science |
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NEW QUESTION # 156
In machine learning, what is the primary difference between supervised and unsupervised learning?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the key difference between supervised and unsupervised learning.
Define Types:
Supervised: Uses labeled data (e.g., input-output pairs) to predict outcomes.
Unsupervised: Uses unlabeled data to find patterns (e.g., clustering).
Evaluate Options:
A: Labeled vs. unlabeled—Core distinction, correct.
B: Monitoring—Misleading, not the primary difference.
C: Image recognition—False, supervised applies broadly.
D: Data Engineer—Irrelevant to learning type.
Reasoning: A captures the foundational data difference.
Conclusion: A is correct.
OCI documentation states: “Supervised learning uses labeled data to train models for prediction, while unsupervised learning analyzes unlabeled data to discover patterns.” B, C, and D misrepresent this—only A aligns with OCI’s ML definitions and industry standards.
1: Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Types".
NEW QUESTION # 157
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?
Answer: D,E
Explanation:
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.
1: Oracle Cloud Infrastructure Language Documentation, "Text Analysis Features".
NEW QUESTION # 158
You are working as a data scientist for a healthcare company. They decide 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 stepsto develop a PySpark application in Oracle Cloud Infrastructure (OCI) Data Science?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Sequence steps for PySpark app development.
Steps:
Launch notebook: First—sets up environment.
Install PySpark conda: Second—adds Spark libraries.
Configure core-site.xml: Third—connects to data.
Develop app: Fourth—writes code.
Data Flow: Fifth—scales (optional).
Evaluate: D (1, 2, 3, 4, 5) matches this logical order.
Reasoning: Notebook first, then setup and coding.
Conclusion: D is correct.
OCI documentation states: “1) Launch a notebook session, 2) install a PySpark conda env, 3) configure core-site.xml, 4) develop your PySpark app, 5) optionally use Data Flow (D).” Other orders (A, B, C) misplace notebook launch or config—D is correct.
1: Oracle Cloud Infrastructure Data Science Documentation, "PySpark Development".
NEW QUESTION # 159
You have a dataset with fewer than 1000 observations, and you are using Oracle AutoML to build a classifier. While visualizing the results of each stage of the Oracle AutoML pipeline, you notice that no visualization has been generated for one of the stages. Which stage is not visualized?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the non-visualized AutoML stage with small data.
Understand AutoML Pipeline: Includes sampling, feature/algorithm selection, tuning.
Evaluate Options:
A: Feature selection—Visualized (e.g., feature importance).
B: Algorithm selection—Visualized (e.g., algorithm scores).
C: Adaptive sampling—Skipped/visualization absent for <1000 rows.
D: Hyperparameter tuning—Visualized (e.g., trial plots).
Reasoning: Adaptive sampling optimizes large datasets; small data skips it, omitting visuals.
Conclusion: C is correct.
OCI AutoML documentation notes: “Adaptive sampling is applied to large datasets (>1000 rows) to reduce size; for smaller datasets, it’s skipped, and no visualization is generated.” Other stages (A, B,D) produce visuals—only C is absent here.
1: Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Stages".
NEW QUESTION # 160
You are a data scientist working for a manufacturing company. You have developed a forecasting model to predict the sales demand in the upcoming months. You created a model artifact that contained custom logic requiring third-party libraries. When you deployed the model, it failed to run because you did not include all the third-party dependencies in the model artifact. What file should be modified to include the missing libraries?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Specify third-party libraries for model deployment.
Understand Artifacts: runtime.yaml defines runtime; score.py handles logic.
Evaluate Options:
A: Not a standard file—incorrect.
B: Inference code—not for dependencies.
C: Defines conda env with dependencies—correct.
D: Pip list—not used in OCI conda deployments.
Reasoning: runtime.yaml points to a conda env with all libraries.
Conclusion: C is correct.
OCI documentation states: “In runtime.yaml, specify the conda environment slug (e.g., ENVIRONMENT_SLUG: custom_env) containing all third-party libraries required by the model.” score.py (B) is for logic, requirements.txt (D) isn’t OCI-standard, and A doesn’t exist—C fixes the issue.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".
NEW QUESTION # 161
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