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NEW QUESTION # 45
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: B
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 # 46
Which Oracle Cloud Service provides restricted access to target resources?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the OCI service for restricted resource access.
* Evaluate Options:
* A: Bastion-Secure, temporary access to resources-correct.
* B: Internet Gateway-Public access, not restricted.
* C: Load Balancer-Distributes traffic, not access control.
* D: SSL Certificate-Secures comms, not access.
* Reasoning: Bastion limits access (e.g., SSH) to specific targets.
* Conclusion: A is correct.
OCI documentation states: "OCI Bastion (A) provides restricted, audited access to target resources like instances, typically via SSH." B, C, and D don't restrict-only A fits per OCI's security services.
Oracle Cloud Infrastructure Bastion Documentation, "Overview".
NEW QUESTION # 47
When preparing your model artifact to save it to the Oracle Cloud Infrastructure (OCI) DataScience model catalog, you create a score.py file. What is the purpose of the score.py file?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Define the role of score.py in OCI model artifacts.
* Understand Artifacts: score.py is key for deployment runtime.
* Evaluate Options:
* A: Infra config-Handled by OCI settings, not score.py.
* B: Inference logic-Correct; runs load_model(), predict().
* C: Scaling-Set in deployment, not score.py.
* D: Dependencies-In runtime.yaml, not score.py.
* Reasoning: B aligns with score.py's execution role.
* Conclusion: B is correct.
OCI documentation states: "score.py (B) contains the inference logic, including functions to load the model and predict outputs, executed by the deployment endpoint." A, C, and D are managed elsewhere-only B matches OCI's design.
Oracle Cloud Infrastructure Data Science Documentation, "Model Artifact - score.py".
NEW QUESTION # 48
You are asked to prepare data for a custom-built model that requires transcribing Spanish video recordings into a readable text format with profane words identified. Which Oracle Cloud Service would you use?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Transcribe Spanish video audio and identify profanity.
* Evaluate Options:
* A: Anomaly Detection-Not for transcription or text analysis.
* B: Speech-Converts audio to text (e.g., Spanish), base for further analysis-correct.
* C: Translation-Translates text, not transcription.
* D: Language-Analyzes text (e.g., profanity), but needs transcribed input.
* Reasoning: Speech (B) transcribes video audio; Language could follow for profanity.
* Conclusion: B is correct for transcription.
OCI Speech "transcribes audio from video or audio files into text, supporting languages like Spanish." Post- transcription, OCI Language could detect profanity, but B is the starting point-Anomaly (A) and Translation (C) don't fit.
Oracle Cloud Infrastructure Speech Documentation, "Transcription Features".
NEW QUESTION # 49
Which stage in the machine learning life cycle helps in identifying the imbalance present in the data?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Find the stage where data imbalance (e.g., skewed classes) is identified.
* Understand Stages:
* Data Modeling: Training models-assumes data is prepared.
* Data Monitoring: Post-deployment tracking-not for initial analysis.
* Data Exploration: Analyzing data properties (e.g., distributions)-key for imbalance.
* Data Access: Retrieving data-no analysis yet.
* Evaluate Options:
* A: Modeling uses data, doesn't detect imbalance-incorrect.
* B: Monitoring tracks performance, not initial data issues-incorrect.
* C: Exploration (e.g., via pandas) reveals imbalances-correct.
* D: Access is just retrieval-incorrect.
* Reasoning: Imbalance is assessed during exploration (e.g., class counts).
* Conclusion: C is correct.
OCI documentation notes: "Data Exploration involves analyzing the dataset to understand its characteristics, such as identifying class imbalances or missing values, using tools like ADS SDK or Jupyter notebooks." Modeling (A) and Monitoring (B) occur later, while Access (D) is pre-analysis-only Exploration (C) fits this role.
Oracle Cloud Infrastructure Data Science Documentation, "Data Exploration Stage".
NEW QUESTION # 50
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