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| Section | Objectives |
|---|---|
| Topic 1: 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 |
| Topic 2: Design and Set Up Data Science Workspace | - Create and configure Data Science projects - Use Accelerated Data Science SDK and open source tools - Manage notebook sessions and compute resources |
| Topic 3: Implement End-to-End Machine Learning Lifecycle | - Save and manage models using Model Catalog - Automate machine learning workflows and pipelines - Build, train, and evaluate machine learning models - Deploy models and consume model endpoints - Prepare and manage datasets |
| Topic 4: 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 |
| Topic 5: Apply MLOps Practices | - Use best practices for operationalizing ML solutions - Implement model lifecycle management - Monitor and maintain machine learning models |
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NEW QUESTION # 76
You are building a model and need input that represents data as morning, afternoon, or evening. However, the data contains a timestamp. What part of the Data Science lifecycle would you be in when creating the new variable?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Locate the lifecycle stage for transforming timestamps into categories.
Understand Lifecycle:
Model Type Selection: Choosing algorithms—post-data prep.
Model Validation: Evaluating performance—post-training.
Data Access: Retrieving raw data—pre-transformation.
Feature Engineering: Creating new features—correct.
Evaluate Options:
A: Too late—incorrect.
B: Post-model—incorrect.
C: Pre-transformation—incorrect.
D: Feature creation—correct.
Reasoning: Converting timestamps to time periods is feature engineering.
Conclusion: D is correct.
OCI documentation states: “Feature engineering (D) transforms raw data, such as timestamps into categorical variables (e.g., morning/afternoon), to enhance model inputs.” A, B, and C occur at different stages—only D fits OCI’s lifecycle for this task.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Stage".
NEW QUESTION # 77
You are attempting to save a model from a notebook session to the model catalog by using 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: A,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Troubleshoot a 404 authentication error when saving a model using ADS SDK with resource principal.
Understand Resource Principal: Allows notebook sessions to act as principals via dynamic groups and policies—no user credentials needed.
Analyze 404 Error: Indicates an authorization failure—likely missing permissions or misconfigured resource principal.
Evaluate Options:
A: True—Dynamic group must include notebook sessions (e.g., resource.type = 'datasciencenotebooksession') to authenticate.
B: False—Block volume stores artifacts locally, but saving to the catalog is a permission issue, not storage.
C: True—Policy must grant manage data-science-models to the dynamic group for catalog access.
D: False—Service gateway ensures network access, but 404 is auth-related, not connectivity.
E: False—Resource principal uses dynamic group policies, not user group policies.
Reasoning: A (group inclusion) and C (policy permission) are critical for resource principal auth—others are tangential.
Conclusion: A and C are correct.
OCI documentation states: “To use resource principal with ADS SDK for model catalog operations, ensure (1) a dynamic group includes the notebook session with a matching rule (e.g., all {resource.type = 'datasciencenotebooksession'}) and (2) a policy grants the dynamic group manage data-science-models permissions in the compartment.” B is unrelated (storage location), D is network-focused, and E applies to user auth—not resource principal. A 404 error flags missing auth, fixed by A and C.
1: Oracle Cloud Infrastructure Data Science Documentation, "Using Resource Principals with ADS SDK".
NEW QUESTION # 78
Select two reasons why it is important to rotate encryption keys when using Oracle Cloud Infrastructure (OCI) Vault to store credentials or other secrets.
Answer: A,D
NEW QUESTION # 79
You have an image classification model in the model catalog which is deployed as an HTTP endpoint using model deployments. Your tenancy administrator is seeing increased demands and has asked you to increase the load balancing bandwidth from the default of 10Mbps. You are provided with the following information:
Payload size in KB = 1024
Estimated requests per second = 120 requests/second (Monday through Friday, in every month, in every year) Buffer percentage = 20%What is the optimal load balancing bandwidth to redeploy your model?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Calculate optimal bandwidth for model deployment.
Given Data:
Payload size = 1024 KB = 1024 * 8 = 8192 Kb (kilobits).
Requests/sec = 120.
Buffer = 20% = 0.2.
Calculate Base Bandwidth:
Bits/sec = Payload * Requests = 8192 Kb * 120 = 983,040 Kb/s = 983.04 Mbps.
Add Buffer:
Total = Base * (1 + Buffer) = 983.04 * 1.2 = 1179.648 Mbps.
Evaluate Options: Closest to 1179.648 Mbps is 1152 Mbps (D)—realistic rounding.
Conclusion: D is correct.
OCI documentation advises: “Calculate bandwidth as payload size (in bits) * requests/sec, then add a buffer (e.g., 20%) for peak loads.” Here, 1024 KB = 8192 Kb, * 120 = 983.04 Mbps, * 1.2 = 1179.648 Mbps. D (1152 Mbps) is the closest practical option—452 (A) and 52 (B) are too low, 7052 (C) excessive.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - Load Balancing".
NEW QUESTION # 80
What is the first step in the data science process?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the initial data science step.
Define Process: Starts with problem definition, then data and modeling.
Evaluate Options:
A: Data collection—Second step after problem definition.
B: Modeling—Later stage.
C: Hypothesis—Sets the goal, first step—correct.
D: Data owners—Collaboration, not the start.
Reasoning: Hypothesis drives the process (e.g., “Can we predict churn?”).
Conclusion: C is correct.
OCI documentation states: “The data science process begins with defining an analytical hypothesis to address a business problem, followed by data collection and analysis.” C precedes A, B, and D—aligning with OCI’s structured approach.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Science Process".
NEW QUESTION # 81
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