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| Section | Objectives |
|---|---|
| OCI Data Science - Introduction and Configuration | - Use OCI Data Science notebooks and sessions - Configure and manage Data Science resources - Understand OCI Data Science service concepts and architecture |
| Implement End-to-End Machine Learning Lifecycle | - Automate machine learning workflows and pipelines - Save and manage models using Model Catalog - Build, train, and evaluate machine learning models - Prepare and manage datasets - Deploy models and consume model endpoints |
| Apply MLOps Practices | - Use best practices for operationalizing ML solutions - Monitor and maintain machine learning models - Implement model lifecycle management |
| Design and Set Up Data Science Workspace | - Use Accelerated Data Science SDK and open source tools - Manage notebook sessions and compute resources - Create and configure Data Science projects |
| Use Related OCI Services | - Integrate OCI Data and AI services - Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases |
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126. Frage
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?
Antwort: D,E
Begründung:
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".
127. Frage
Which Oracle Accelerated Data Science (ADS) classes can be used for easy access to datasets fromreference libraries and index websites, such as scikit-learn?
Antwort: D
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Identify ADS class for accessing datasets (e.g., scikit-learn).
Evaluate Options:
A: DatasetBrowser—Not an ADS class.
B: DatasetFactory—Loads datasets from sources like scikit-learn—correct.
C: ADSTuner—Hyperparameter tuning, not data access.
D: SecretKeeper—Manages credentials, not datasets.
Reasoning: DatasetFactory simplifies dataset loading (e.g., DatasetFactory.open()).
Conclusion: B is correct.
OCI documentation states: “DatasetFactory in ADS SDK provides methods to easily load datasets from libraries like scikit-learn or other sources (e.g., DatasetFactory.open('sklearn.datasets:load_iris')).” A isn’t real, C tunes models, and D handles secrets—only B fits.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "DatasetFactory".
128. Frage
What is feature engineering in machine learning used for?
Antwort: C
Begründung:
Detailed Answer in Step-by-Step Solution:
Define Feature Engineering: It’s the process of creating or modifying features to improve model performance.
Evaluate Options:
A: Parameter tuning adjusts model hyperparameters (e.g., learning rate), not features.
B: Model interpretation (e.g., SHAP values) explains predictions, not feature creation.
C: Transforming features (e.g., normalizing, encoding) is the core of feature engineering—correct.
D: Understanding features occurs during exploration, not engineering.
Reasoning: Feature engineering directly manipulates data inputs (e.g., converting timestamps to day-of-week), distinct from tuning or interpretation.
Conclusion: C is the precise definition.
OCI Data Science documentation defines feature engineering as “the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy.” Examples include scaling or creating interaction terms, aligning 1with C. Other options (A, B, D) relate to different ML stages.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Overview".
129. Frage
You are using a custom application with third-party APIs to manage application and data hosted in an Oracle Cloud Infrastructure (OCI) tenancy. Although your third-party APIs don’t support OCI’s signature-based authentication, you want them to communicate with OCI resources. Which authentication option must you use to ensure this?
Antwort: C
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Select an auth method for third-party APIs lacking OCI signature support.
Understand OCI Auth: Typically uses API keys, but alternatives exist for non-standard APIs.
Evaluate Options:
A: Username/password—Not API-friendly, insecure.
B: API Signing Key—Requires signature-based auth, unsupported here.
C: SSH Key—For instance access, not APIs.
D: Auth Token—Simple token for API calls—correct.
Reasoning: Auth Token provides a bearer token for APIs without signature complexity.
Conclusion: D is correct.
OCI documentation states: “For third-party APIs not supporting signature-based authentication, use an Auth Token (D), a secure, revocable token for accessing OCI resources via REST APIs.” A, B, and C don’t fit non-signature scenarios—only D ensures compatibility per OCI’s IAM options.
1: Oracle Cloud Infrastructure IAM Documentation, "Auth Tokens for API Access".
130. Frage
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?
Antwort: C,D,E
Begründung:
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Scaling".
131. Frage
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