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| Section | Weight | Objectives |
|---|---|---|
| OCI Data Science - Introduction & Configuration | 10% | - Capabilities of the Accelerated Data Science (ADS) SDK - Tenancy and environment configuration for Data Science - Overview and core concepts of OCI Data Science |
| Design and Set Up Data Science Workspace | 15% | - Create and manage projects and notebook sessions - Configure compute shapes, storage, and networking - Manage access control, security, and IAM integration |
| Apply MLOps Practices | 20% | - Governance, auditing, and compliance - ML pipelines, automation, and reproducibility - Model monitoring, drift detection, and performance tracking |
| Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| Implement End-to-End Machine Learning Lifecycle | 45% | - Use AutoML and built-in algorithms - Model saving, cataloging, and versioning - Deploy models and manage endpoints - Model development, training, and evaluation - Data preparation, exploration, and transformation |
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NEW QUESTION # 13
Which of the following analytical and statistical techniques do data scientists commonly use?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify common data science techniques.
Define Techniques:
Classification: Predicts categories (e.g., spam vs. not).
Regression: Predicts continuous values (e.g., sales).
Clustering: Groups data (e.g., customer segments).
Evaluate Options:
A, B, C: All are standard ML/statistical methods.
D: Encompasses all—correct as they’re widely used.
Reasoning: These are foundational in data science workflows.
Conclusion: D is correct.
OCI documentation lists “classification, regression, and clustering as core techniques in data science, supported by tools like ADS SDK and AutoML.” All (D) are common per OCI’s ML framework, not just subsets (A, B, C).
1: Oracle Cloud Infrastructure Data Science Documentation, "Analytical Techniques".
NEW QUESTION # 14
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: B,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 # 15
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 # 16
You are preparing a configuration object necessary to create a Data Flow application. Which THREE parameter values should you provide?
Answer: A,D,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify three required params for an OCI Data Flow app config.
Understand Data Flow: Runs Spark apps; needs compartment, storage, and identity.
Evaluate Options:
A: Archive path—Optional if script is in Object Storage—incorrect.
B: Local script path—Not needed; script is uploaded—incorrect.
C: Compartment—Required for resource scope—correct.
D: Bucket—Required for script storage/access—correct.
E: Display name—Required for app identification—correct.
Reasoning: C, D, E are mandatory metadata for Data Flow creation—script location is specified via bucket.
Conclusion: C, D, E are correct.
OCI documentation states: “To create a Data Flow application, configure the compartment OCID (C), Object Storage bucket for the PySpark script (D), and a display name (E) in the application object.” Local paths (B) or archives (A) are optional or handled separately—only C, D, E are required per OCI’s Data Flow API spec.
1: Oracle Cloud Infrastructure Data Flow Documentation, "Creating Applications".
NEW QUESTION # 17
You have created a conda environment in your notebook session. This is the first time you are working with published conda environments. You have also created an Object Storage bucket with permission to manage the bucket. Which TWO commands are required to publish the conda environment?
Answer: A,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Publish a conda env to Object Storage.
Process: Initialize bucket config, then publish env.
Evaluate Options:
A: Publishes env with slug—correct final step.
B: Lists envs—unrelated to publishing.
C: Sets bucket details—required setup—correct.
D: Creates env—not publishing.
E: Activates env—not for sharing.
Reasoning: C sets up, A executes—standard workflow.
Conclusion: A and C are correct.
OCI documentation states: “To publish a conda environment, first run odsc conda init (C) with bucket namespace and name, then odsc conda publish (A) with a slug to upload to Object Storage.” B, D, and E serve other purposes—only A and C are required per OCI’s process.
1: Oracle Cloud Infrastructure Data Science CLI Reference, "Publishing Conda Environments".
NEW QUESTION # 18
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