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| Section | Objectives |
|---|---|
| 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 |
| Implement End-to-End Machine Learning Lifecycle | - Deploy models and consume model endpoints - Build, train, and evaluate machine learning models - Save and manage models using Model Catalog - Prepare and manage datasets - Automate machine learning workflows and pipelines |
| Use Related OCI Services | - Design machine learning solutions for business use cases - Integrate OCI Data and AI services - Apply OCI services for data ingestion, storage, and processing |
| Apply MLOps Practices | - Use best practices for operationalizing ML solutions - Implement model lifecycle management - Monitor and maintain machine learning models |
| 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 |
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NEW QUESTION # 123
Which cache rules criterion matches if the concatenation of the requested URL path and query are identical to the contents of the value field?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Match a cache rule criterion for exact URL path and query.
Understand Cache Rules: Used in OCI (e.g., WAF, CDN) to cache content.
Evaluate Options:
A: Contains—Partial match, not exact.
B: Is—Exact match of full URL (path + query)—correct.
C: Ends with—Matches end, not full URL.
D: Starts with—Matches start, not full URL.
Reasoning: “URL_IS” checks exact equality—fits requirement.
Conclusion: B is correct.
OCI documentation states: “The URL_IS (B) criterion in cache rules matches when the full URL (path and query) exactly equals the specified value.” A, C, and D are partial matches—only B ensures identical concatenation per OCI’s caching config.
1: Oracle Cloud Infrastructure WAF Documentation, "Cache Rules Criteria".
NEW QUESTION # 124
What is the minimum active storage duration for logs used by Logging Analytics to be archived?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine minimum log storage duration before archiving in Logging Analytics.
Understand Logging Analytics: Logs are active before archival.
Evaluate Options:
A: 60 days—Too long for minimum.
B: 10 days—Too short.
C: 30 days—Standard minimum—correct.
D: 15 days—Not OCI’s default.
Reasoning: 30 days is OCI’s documented minimum active period.
Conclusion: C is correct.
OCI documentation states: “Logs in Logging Analytics remain active for a minimum of 30 days (C) before archiving, ensuring availability for analysis.” B and D are shorter, A is longer—only C matches OCI’s policy.
1: Oracle Cloud Infrastructure Logging Analytics Documentation, "Log Retention".
NEW QUESTION # 125
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? Pick THREE.
Answer: A,C,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Prevent deployment failure due to high utilization.
Evaluate Options:
A: More instances—Scales capacity—correct.
B: Delete—Stops service, not a solution.
C: Fewer instances—Worsens utilization.
D: Larger VM—Increases resource capacity—correct.
E: Reduce bandwidth—Limits load—correct.
Reasoning: A and D boost capacity, E controls demand—proactive fixes.
Conclusion: A, D, E are correct.
OCI documentation advises: “To handle high utilization, increase instances (A), use a larger compute shape (D), or adjust load balancer bandwidth (E) to manage request volume.” B stops service, C reduces capacity—only A, D, E prevent failure per OCI’s scaling options.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Scaling".
NEW QUESTION # 126
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: A
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 # 127
After you have created and opened a notebook session, you want to use the Accelerated Data Science (ADS) SDK to access your data and get started with exploratory data analysis. From which TWO places can you access the ADS SDK?
Answer: C,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Locate sources for ADS SDK in OCI.
Understand ADS SDK: A Python library for Data Science tasks (e.g., EDA).
Evaluate Options:
A: Big Data Service—Spark-focused, not ADS source.
B: Machine Learning—Separate service, not ADS-related.
C: Conda in OCI Data Science—Preinstalled ADS in notebook sessions.
D: PyPI—Public source to install ADS (pip install oracle-ads).
E: ADW—Database, not an SDK source.
Reasoning: C (preinstalled) and D (installable) are practical access points.
Conclusion: C and D are correct.
OCI documentation states: “The ADS SDK is available in OCI Data Science notebook sessions via preinstalled conda environments (C) and can be installed from PyPI (D) using pip install oracle-ads.” Big Data (A), Machine Learning (B), and ADW (E) don’t host ADS—only C and D apply.
1: Oracle Cloud Infrastructure Data Science Documentation, "ADS SDK Installation".
NEW QUESTION # 128
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