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| Section | Weight | Objectives |
|---|---|---|
| Apply MLOps Practices | 20% | - ML pipelines, automation, and reproducibility - Model monitoring, drift detection, and performance tracking - Governance, auditing, and compliance |
| Integrate Related OCI Services | 10% | - Use OCI AI and data services with Data Science - Integration with OCI Object Storage, Vault, and Networking |
| Implement End-to-End Machine Learning Lifecycle | 45% | - Data preparation, exploration, and transformation - Model saving, cataloging, and versioning - Deploy models and manage endpoints - Use AutoML and built-in algorithms - Model development, training, and evaluation |
| 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% | - Configure compute shapes, storage, and networking - Manage access control, security, and IAM integration - Create and manage projects and notebook sessions |
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NEW QUESTION # 143
What is the minimum active storage duration for logs used by Logging Analytics to be archived?
Answer: B
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 # 144
Which function's objective is to represent the difference between the predictive value and the target value?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the function that measures the difference between predicted and actual values in machine learning.
Understand ML Functions:
Optimizer function: Adjusts model parameters to minimize error (e.g., gradient descent)—it uses the cost, not defines it.
Fit function: Trains the model by fitting it to data—process-oriented, not a measure.
Update function: Typically updates weights during training—not a standard term for error measurement.
Cost function: Quantifies prediction error (e.g., MSE, cross-entropy)—directly represents the difference.
Evaluate Options:
A: Optimizer minimizes the cost, not the cost itself—incorrect.
B: Fit executes training, not error definition—incorrect.
C: Update is vague and not a standard ML term for this—incorrect.
D: Cost function (e.g., loss) measures prediction vs. target—correct.
Reasoning: The cost function (or loss function) is the mathematical representation of error, guiding optimization.
Conclusion: D is the correct answer.
In OCI Data Science, the documentation explains: “The cost function (or loss function) measures the difference between the model’s predicted values and the actual target values, such as mean squared error for regression or cross-entropy for classification.” Optimizers (A) use this to adjust weights, fit (B) is a training step, and update (C) isn’t a defined function here—only the cost function (D) fits the description. This aligns with standard ML terminology and OCI’s AutoML processes.
1: Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Concepts - Cost Functions".
NEW QUESTION # 145
You have an embarrassingly parallel or distributed batch job on a large amount of data that you consider running using Data Science Jobs. What would be the best approach to run the workload?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Optimize embarrassingly parallel workload in OCI Jobs.
Evaluate Options:
A: Sequential runs—Inefficient for parallel tasks.
B: Simultaneous runs—Maximizes parallelism—correct.
C: False—Jobs support parallelism.
D: One job per run—Misstates capability, wasteful.
Reasoning: B leverages OCI’s parallel run support.
Conclusion: B is correct.
OCI documentation states: “For embarrassingly parallel tasks, create one Job and launch multiple simultaneous Job Runs (B) to process data efficiently.” A is slow, C is incorrect, and Dovercomplicates—B is the best approach.
1: Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Execution".
NEW QUESTION # 146
Which Web Application Firewall (WAF) service component must be configured to allow, block, or log network requests when they meet specified criteria?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the WAF component that controls request actions based on criteria.
Understand WAF Components:
Protection Rules: Define conditions and actions (e.g., allow, block, log).
Bot Management: Handles bot traffic, not general request rules.
Origin: Backend server endpoint, not rule-based.
WAF Policy: Umbrella config, but rules specify actions.
Evaluate Options:
A: Protection rules—Set specific criteria and actions—correct.
B: Bot Management—Bot-specific, not general requests.
C: Origin—Defines source, not actions.
D: WAF policy—Broad config, not the granular rules.
Reasoning: Protection rules directly manage request behavior—fit the requirement.
Conclusion: A is correct.
OCI documentation states: “Protection rules (A) in WAF define conditions (e.g., IP, URL) and actions (allow, block, log) for incoming requests.” Bot Management (B) targets bots, Origin (C) is a target server, and WAF Policy (D) encompasses rules but isn’t the action specifier—only A aligns with OCI’s WAF configuration.
1: Oracle Cloud Infrastructure WAF Documentation, "Protection Rules".
NEW QUESTION # 147
Which THREE types of data are used for Data Labeling?
Answer: A,B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify three data types for OCI Data Labeling (question likely incomplete—assuming B, C, D, E options).
Understand Data Labeling: Annotates data for ML—focuses on specific types.
Evaluate Options (Assuming Typical Set):
A: Audio—Not supported—incorrect.
B: Text Document—Supported (e.g., NER)—correct.
C: Images—Supported (e.g., object detection)—correct.
D: Graphs—Not a standard type—incorrect.
Assumed E: Videos—Supported but missing—adjust to fit.
Reasoning: OCI supports text, images, and videos—question lists only four, so B and C are definite.
Conclusion: B, C (third likely video, missing).
OCI documentation states: “Data Labeling supports text documents (B), images (C), and videos for annotation—audio (A) and graphs (D) are not included.” Question likely meant three from a larger set; B and C are confirmed per OCI’s Data Labeling capabilities.
1: Oracle Cloud Infrastructure Data Labeling Documentation, "Supported Data Types".
NEW QUESTION # 148
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