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| Section | Objectives |
|---|---|
| Use Related OCI Services | - Apply OCI services for data ingestion, storage, and processing - Integrate OCI Data and AI services - Design machine learning solutions for business use cases |
| OCI Data Science - Introduction and Configuration | - Understand OCI Data Science service concepts and architecture - Use OCI Data Science notebooks and sessions - Configure and manage Data Science resources |
| Design and Set Up Data Science Workspace | - Create and configure Data Science projects - Manage notebook sessions and compute resources - Use Accelerated Data Science SDK and open source tools |
| Apply MLOps Practices | - Monitor and maintain machine learning models - Implement model lifecycle management - Use best practices for operationalizing ML solutions |
| Implement End-to-End Machine Learning Lifecycle | - Build, train, and evaluate machine learning models - Deploy models and consume model endpoints - Save and manage models using Model Catalog - Automate machine learning workflows and pipelines - Prepare and manage datasets |
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NEW QUESTION # 104
Which technique can be used for feature engineering in the machine learning lifecycle?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a feature engineering technique in ML.
Understand Feature Engineering: Transforms raw data into model-ready features.
Evaluate Options:
A . PCA: Reduces dimensionality—feature engineering—correct.
B . K-means: Clustering model—not feature engineering.
C . SVM: Classification model—not feature engineering.
D . Gradient boosting: Model training—not feature engineering.
Reasoning: PCA creates new features via transformation—fits definition.
Conclusion: A is correct.
OCI documentation states: “Feature engineering techniques like Principal Component Analysis (PCA) (A) transform data into new features to enhance model performance.” B, C, and D are modeling techniques—only A aligns with OCI’s feature engineering stage.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Techniques".
NEW QUESTION # 105
You are attempting to save a model from a notebook session to the model catalog by using the Accelerated Data Science (ADS) SDK, with resource principal as the authentication signer, and you get a 404 authentication error. Which two should you look for to ensure permissions are set up correctly?
Answer: B,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Troubleshoot a 404 auth error when saving a model with resource principal.
Understand Resource Principal: Allows notebook sessions to act as principals via dynamic groups and policies—no user credentials.
Analyze 404 Error: Indicates permission failure—likely IAM misconfiguration.
Evaluate Options:
A: Block volume storage—Irrelevant to auth; it’s about saving locally—incorrect.
B: Dynamic group matching—Ensures notebook is recognized—correct.
C: User group policy—Not used with resource principal—incorrect.
D: Dynamic group policy—Grants catalog access—correct.
E: Service Gateway—Network-related, not auth-specific—incorrect.
Reasoning: Resource principal needs B (group inclusion) and D (policy perms)—404 points to these.
Conclusion: B and D are correct.
OCI documentation states: “For ADS SDK to save to the Model Catalog using resource principal, ensure (1) a dynamic group includes notebook sessions with matching rules (e.g., resource.type ='datasciencenotebooksession') (B), and (2) a policy grants manage data-science-models to that dynamic group (D).” A is storage, C is user-based, E is network—only B and D fix the auth issue per OCI’s IAM setup.
1: Oracle Cloud Infrastructure Data Science Documentation, "Resource Principal with Model Catalog".
NEW QUESTION # 106
How can you convert a fixed load balancer to a flexible load balancer?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Convert fixed to flexible load balancer in OCI.
Understand Load Balancers: Fixed (e.g., 10 Mbps) vs. flexible (dynamic shapes).
Evaluate Options:
A: False—Conversion possible via recreation.
B: Update Shape—For flexible only, not conversion.
C: Delete and recreate—Standard method—correct.
D: Edit Listener—Configures rules, not type.
Reasoning: OCI requires new creation for type change.
Conclusion: C is correct.
OCI documentation states: “To change from a fixed to a flexible load balancer, delete the existing fixed load balancer and create a new flexible one (C)—direct conversion isn’t supported.” A is too absolute, B and D don’t apply—only C matches OCI’s process.
1: Oracle Cloud Infrastructure Load Balancing Documentation, "Changing Load Balancer Type".
NEW QUESTION # 107
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: A,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 # 108
You want to make your model more frugal to reduce the cost of collecting and processing dat a. You plan to do this by removing features that are highly correlated. You would like to create a heatmap that displays the correlation so that you can identify candidate features to remove. Which Accelerated Data Science (ADS) SDK method is appropriate to display the comparability between Continuous and Categorical features?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Visualize correlation between continuous and categorical features.
Evaluate Options:
A: Pearson—Continuous vs. continuous—incorrect.
B: Cramer’s V—Categorical vs. categorical—incorrect.
C: Correlation ratio—Continuous vs. categorical—correct.
D: General correlation—Not specific to mixed types.
Reasoning: Correlation ratio handles mixed feature types for heatmaps.
Conclusion: C is correct.
OCI documentation states: “correlation_ratio_plot() (C) in ADS SDK visualizes correlations between continuous and categorical features, ideal for mixed-type heatmaps.” Pearson (A) and Cramer’s (B) are type-specific, corr() (D) is broad—only C fits per ADS capabilities.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization".
NEW QUESTION # 109
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