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| Section | Objectives |
|---|---|
| Use Related OCI Services | - Integrate OCI Data and AI services - Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases |
| Design and Set Up Data Science Workspace | - Create and configure Data Science projects - Manage notebook sessions and compute resources - Use Accelerated Data Science SDK and open source tools |
| Apply MLOps Practices | - Use best practices for operationalizing ML solutions - Implement model lifecycle management - Monitor and maintain machine learning models |
| Implement End-to-End Machine Learning Lifecycle | - Save and manage models using Model Catalog - Deploy models and consume model endpoints - Build, train, and evaluate machine learning models - Automate machine learning workflows and pipelines - Prepare and manage datasets |
| OCI Data Science - Introduction and Configuration | - Configure and manage Data Science resources - Use OCI Data Science notebooks and sessions - Understand OCI Data Science service concepts and architecture |
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NEW QUESTION # 72
What is feature engineering in machine learning used for?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Define Feature Engineering: It’s the process of creating or modifying features to improve model performance.
Evaluate Options:
A: Parameter tuning adjusts model hyperparameters (e.g., learning rate), not features.
B: Model interpretation (e.g., SHAP values) explains predictions, not feature creation.
C: Transforming features (e.g., normalizing, encoding) is the core of feature engineering—correct.
D: Understanding features occurs during exploration, not engineering.
Reasoning: Feature engineering directly manipulates data inputs (e.g., converting timestamps to day-of-week), distinct from tuning or interpretation.
Conclusion: C is the precise definition.
OCI Data Science documentation defines feature engineering as “the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy.” Examples include scaling or creating interaction terms, aligning 1with C. Other options (A, B, D) relate to different ML stages.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Overview".
NEW QUESTION # 73
You are using Oracle Cloud Infrastructure (OCI) Anomaly Detection to train a model to detect anomalies in pump sensor dat a. What are you trying to determine? How does the required False Alarm Probability setting affect an anomaly detection model?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Understand FAP’s effect in OCI Anomaly Detection.
Evaluate Options:
A: Disable reporting—Incorrect; FAP sets threshold.
B: Sensitivity—Correct; lower FAP reduces false positives.
C: Error message—Incorrect; not a count mechanism.
D: Score per signal—Incorrect; FAP is a global setting.
Reasoning: FAP adjusts detection threshold—key to sensitivity.
Conclusion: B is correct.
OCI documentation states: “False Alarm Probability (FAP) controls the model’s sensitivity—lower values reduce false positives, higher values increase detection.” B aligns—others misrepresent FAP’s role.
1: Oracle Cloud Infrastructure Anomaly Detection Documentation, "FAP Configuration".
NEW QUESTION # 74
As a data scientist, you are tasked with creating a model training job that is expected to take different hyperparameter values on every run. What is the most efficient way to set those parameters with Oracle Data Science Jobs?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Efficiently manage varying hyperparameters in OCI Data Science Jobs.
Understand OCI Jobs: Jobs execute predefined tasks with configurable inputs (e.g., env vars, args).
Evaluate Options:
A: New job per run with env vars—Redundant job creation, inefficient.
B: New job per run with args—Similarly inefficient due to repeated setup.
C: Hardcode params, new job per change—Highly inefficient, requires code edits.
D: Single job, flexible params via env vars or args—Efficient, reusable—correct.
Reasoning: D minimizes job creation, allows runtime flexibility via configuration—best practice.
Conclusion: D is correct.
OCI documentation states: “For Jobs with varying hyperparameters, write code to accept environment variables or command-line arguments (D), then configure these per Job Run using the OCI Console or SDK—most efficient approach.” Options A, B, and C involve unnecessary job proliferation or code changes—only D aligns with OCI’s design for parameterized runs.
1: Oracle Cloud Infrastructure Data Science Documentation, "Configuring Job Runs with Parameters".
NEW QUESTION # 75
Where do calls to stdout and stderr from score.py go in the model deployment?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Locate stdout/stderr output from score.py in deployment.
Understand Deployment: score.py runs in a model endpoint; logs are managed by OCI.
Evaluate Options:
A: False—No VM file is defined by default; logs go to OCI Logging.
B: False—Console displays UI, not raw logs.
C: False—Cloud Shell is a separate tool, not a log destination.
D: True—Predict logs in OCI Logging capture stdout/stderr.
Reasoning: OCI centralizes logs in its Logging service for deployments.
Conclusion: D is correct.
OCI documentation states: “During model deployment, stdout and stderr from score.py are automatically sent to the predict log in the OCI Logging service, as configured in the deployment settings.” A, B, and C don’t align with this managed logging approach—only D is accurate.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - Logging".
NEW QUESTION # 76
Which stage in the machine learning life cycle helps in identifying the imbalance present in the data?
Answer: C
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Exploration Stage".
NEW QUESTION # 77
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