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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: MLOps and OCI Integration | 20% | - OCI ecosystem
|
| Topic 2: OCI Data Science Service | 30% | - Projects and notebooks
|
| Topic 3: Machine Learning Fundamentals | 20% | - Supervised learning
|
| Topic 4: Model Development and Deployment | 30% | - Model training
|
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NEW QUESTION # 11
Which type of firewalls are designed to protect against web application attacks, such as SQL injection and cross-site scripting?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the firewall type protecting against web app attacks like SQL injection and XSS.
Understand Firewall Types:
Stateful Inspection: Tracks connection states, not app-specific.
Web Application Firewall (WAF): Targets web app vulnerabilities.
Incident Firewall: Not a recognized term.
Packet Filtering: Basic packet rules, not app-aware.
Evaluate Options:
A: Stateful—General network, not web-specific—incorrect.
B: WAF—Designed for SQLi, XSS—correct.
C: Incident—Non-existent—incorrect.
D: Packet—Low-level, not app-focused—incorrect.
Reasoning: WAF specializes in web app security—matches requirement.
Conclusion: B is correct.
OCI documentation states: “Web Application Firewall (WAF) (B) protects against web application attacks like SQL injection and cross-site scripting by inspecting HTTP traffic.” A and D handle network-level threats, C isn’t real—only B aligns with OCI’s WAF purpose.
1: Oracle Cloud Infrastructure WAF Documentation, "Overview".
NEW QUESTION # 12
Which Oracle Data Safe feature minimizes the amount of personal data and allows internal test, development, and analytics teams to operate with reduced risk?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the Data Safe feature that reduces personal data exposure.
Understand Data Safe: Secures sensitive data in OCI databases.
Evaluate Options:
A: Encryption—Protects data, doesn’t minimize it.
B: Assessment—Identifies risks, doesn’t alter data.
C: Masking—Obfuscates personal data (e.g., SSNs)—correct.
D: Discovery—Locates sensitive data, doesn’t reduce it.
E: Auditing—Tracks access, doesn’t minimize data.
Reasoning: Masking replaces sensitive data, reducing risk for teams—fits goal.
Conclusion: C is correct.
OCI documentation states: “Data masking (C) in Data Safe transforms sensitive data into anonymized versions, minimizing exposure for test, dev, and analytics use.” A protects, B assesses, D finds, E audits—only C reduces data per OCI’s Data Safe features.
1: Oracle Cloud Infrastructure Data Safe Documentation, "Data Masking Overview".
NEW QUESTION # 13
As a data scientist, you require a pipeline to train ML models. When can a pipeline run be initiated?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine when an OCI Data Science pipeline can start.
Understand Pipelines: They’re workflows with defined steps, executed on demand or scheduled.
Evaluate Options:
A: Once created, a pipeline can be run immediately—correct.
B: “During run state” implies it’s already running—illogical.
C: “After active state” is unclear; pipelines run when triggered, not post-state.
D: “Before active state” is vague—creation precedes running.
Reasoning: Pipelines are executable post-creation via UI/CLI—simplest interpretation is A.
Conclusion: A is correct.
OCI Data Science documentation states: “After a pipeline is created, you can initiate a pipeline run immediately or schedule it using the OCI Console, CLI, or SDK.” B, C, and D misalign with this—running starts post-creation (A), not during/after ambiguous states.
1: Oracle Cloud Infrastructure Data Science Documentation, "Pipelines - Running a Pipeline".
NEW QUESTION # 14
You want to make API calls against other OCI services from your instance without configuring user credentials. How would you achieve this?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Enable credential-less API calls from an instance.
Understand Resource Principal: Allows instances to authenticate via IAM without user creds.
Evaluate Options:
A: Dynamic group + policy—Correct; groups instance, grants access.
B: Dynamic group only—Incomplete; needs policy.
C: User group—Irrelevant for instances.
D: No config—False; setup required.
Reasoning: A sets up resource principal fully—group and perms.
Conclusion: A is correct.
OCI documentation states: “To make API calls without credentials, create a dynamic group including the instance and add a policy (A) granting access to OCI services—enables resource principal.” B lacks policy, C is user-based, D is false—only A completes the process per OCI’s IAM setup.
1: Oracle Cloud Infrastructure IAM Documentation, "Resource Principal Configuration".
NEW QUESTION # 15
A bike sharing platform has collected user commute data for the past 3 years. For increasing profitability and making useful inferences, a machine learning model needs to be built from the accumulated data. Which of the following options has the correct order of the required machine learning tasks for building a model?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Data Access: The first step in any machine learning workflow is accessing the raw data. This involves retrieving the user commute data collected over the past 3 years from the bike-sharing platform’s storage system.
Data Exploration: Once data is accessed, it’s explored to understand its structure, quality, and patterns (e.g., missing values, distributions). This step helps identify what preprocessing is needed.
Feature Engineering: After understanding the data, features are created or transformed (e.g., commute duration, time of day) to improve model performance. This step precedes feature exploration because you need engineered features to analyze further.
Feature Exploration: This involves analyzing the engineered features (e.g., correlation analysis, importance ranking) to refine them or select the most relevant ones for modeling.
Modeling: Finally, the prepared data and features are used to train and evaluate a machine learning model.
Option C (Data Access, Data Exploration, Feature Engineering, Feature Exploration, Modeling) follows this logical sequence, aligning with standard ML workflows.
The correct order reflects the machine learning lifecycle as outlined in Oracle’s OCI Data Science documentation. Data Access is the initial step to retrieve data, followed by Data Exploration to assess it (e.g., using OCI Data Science Notebook Sessions with tools like pandas). Feature Engineering transforms raw data into meaningful inputs, followed by Feature Exploration to analyze feature importance (e.g., using ADS SDK’s correlation tools). Modeling is the final step where the model is built and trained. This sequence is consistent with Oracle’s recommended practices for building ML models in OCI Data Science (Oracle Cloud Infrastructure Data Science Service Documentation, "Machine Learning Lifecycle").
NEW QUESTION # 16
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