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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q147-Q152):

NEW QUESTION # 147
For your next data science project, you need access to public geospatial images. Which Oracle Cloud service provides free access to those images?

A. Oracle Cloud Infrastructure (OCI) Data Science
B. Oracle Big Data Service
C. Oracle Open Data
D. Oracle Analytics Cloud

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Find the OCI service for free geospatial images.
* Evaluate Options:
* A: Big Data Service-Spark processing, not datasets.
* B: Analytics Cloud-Visualization, not data source.
* C: Data Science-ML platform, not dataset provider.
* D: Open Data-Free public datasets, including geospatial-correct.
* Reasoning: Open Data is OCI's public dataset hub.
* Conclusion: D is correct.
OCI documentation states: "Oracle Open Data provides free access to curated datasets, including geospatial images, for public use." A, B, and C serve other purposes-only D delivers free geospatial data.
Oracle Cloud Infrastructure Open Data Documentation, "Dataset Offerings".

NEW QUESTION # 148
In machine learning, what is the primary difference between supervised and unsupervised learning?

A. Supervised learning involves data that has been labeled and classified, while unsupervised learning data is unlabeled and unclassified.
B. Supervised learning is only used for image recognition, while unsupervised learning can be used for various analytics applications.
C. Supervised learning is created and managed by the Data Engineer.
D. Supervised learning is monitored closely by data scientists, while they don't play a role inunsupervised learning.

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the key difference between supervised and unsupervised learning.
* Define Types:
* Supervised: Uses labeled data (e.g., input-output pairs) to predict outcomes.
* Unsupervised: Uses unlabeled data to find patterns (e.g., clustering).
* Evaluate Options:
* A: Labeled vs. unlabeled-Core distinction, correct.
* B: Monitoring-Misleading, not the primary difference.
* C: Image recognition-False, supervised applies broadly.
* D: Data Engineer-Irrelevant to learning type.
* Reasoning: A captures the foundational data difference.
* Conclusion: A is correct.
OCI documentation states: "Supervised learning uses labeled data to train models for prediction, while unsupervised learning analyzes unlabeled data to discover patterns." B, C, and D misrepresent this-only A aligns with OCI's ML definitions and industry standards.
Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Types".

NEW QUESTION # 149
Which of the following best describes the principal goal of data science?

A. Data science is focused on output of the analysis.
B. To mine and analyze large amounts of data in order to uncover information that can be used for operational improvements and business gains.
C. To collect and archive exhaustive datasets from various source systems for corporate record-keeping uses.
D. To collect and prepare data for use as part of analytics applications.

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Define data science's main goal.
* Evaluate Options:
* A: Archiving-Not the focus; too narrow.
* B: Analyze for insights/business value-Core purpose-correct.
* C: Prep for analytics-Means, not the end goal.
* D: Output-focused-Vague, incomplete.
* Reasoning: B captures the actionable insight generation central to data science.
* Conclusion: B is correct.
OCI documentation defines data science as "mining and analyzing large datasets to uncoveractionable insights for operational improvements and business value." A is storage-focused, C is preparatory, and D is unclear-only B reflects the principal goal per OCI's mission.
Oracle Cloud Infrastructure Data Science Documentation, "What is Data Science?".

NEW QUESTION # 150
You have built a machine model to predict whether a bank customer is going to default on a loan. You want to use Local Interpretable Model-Agnostic Explanations (LIME) to understand a specific prediction. What is the key idea behind LIME?

A. Model-agnostic techniques are more interpretable than techniques that are dependent on the types of models
B. Local explanation techniques are model-agnostic, while global explanation techniques are not
C. Global and local behaviours of machine learning models are similar
D. Global behaviour of a machine learning model may be complex, while the local behaviour may be approximated with a simpler surrogate model

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Define LIME's core concept.
* Understand LIME: Explains individual predictions with local surrogate models.
* Evaluate Options:
* A: Complex global, simple local-Correct LIME principle.
* B: Agnosticism-True but not the key idea.
* C: Global/local similarity-False.
* D: Local vs. global agnosticism-Incorrect distinction.
* Reasoning: A captures LIME's local approximation focus.
* Conclusion: A is correct.
OCI documentation notes: "LIME (A) explains predictions by approximating complex global models with simpler local surrogate models around specific instances." B, C, and D misalign-only A reflects LIME's foundational idea per OCI's interpretability tools.
Oracle Cloud Infrastructure Data Science Documentation, "Model Interpretability - LIME".

NEW QUESTION # 151
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?

A. Model validation
B. Model type selection
C. Data access
D. Feature engineering

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.
Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Stage".

NEW QUESTION # 152
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