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| Section | Objectives |
|---|---|
| Topic 1: Apply MLOps Practices | - Implement model lifecycle management - Monitor and maintain machine learning models - Use best practices for operationalizing ML solutions |
| Topic 2: 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 |
| Topic 3: OCI Data Science - Introduction and Configuration | - Use OCI Data Science notebooks and sessions - Configure and manage Data Science resources - Understand OCI Data Science service concepts and architecture |
| Topic 4: Implement End-to-End Machine Learning Lifecycle | - Save and manage models using Model Catalog - Prepare and manage datasets - Deploy models and consume model endpoints - Build, train, and evaluate machine learning models - Automate machine learning workflows and pipelines |
| Topic 5: Design and Set Up Data Science Workspace | - Use Accelerated Data Science SDK and open source tools - Create and configure Data Science projects - Manage notebook sessions and compute resources |
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NEW QUESTION # 151
Which of the following best describes the principal goal of data science?
Answer: D
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "What is Data Science?".
NEW QUESTION # 152
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 # 153
You want to write a program that performs document analysis tasks such as extracting text and tables from a document. Which Oracle AI service would you use?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Select an OCI AI service for text and table extraction from documents.
Evaluate Options:
A: Language—Text analysis, not extraction—incorrect.
B: Digital Assistant—Chatbots, not document tasks—incorrect.
C: Speech—Audio transcription, not documents—incorrect.
D: Vision—OCR for text/tables—correct.
Reasoning: Vision’s OCR extracts text and tables from document images.
Conclusion: D is correct.
OCI documentation states: “OCI Vision (D) uses OCR to extract text and tables from documents, supporting document analysis tasks.” A analyzes text post-extraction, B and C are unrelated—only D fits per OCI’s AI services.
1: Oracle Cloud Infrastructure Vision Documentation, "Document Analysis Features".
NEW QUESTION # 154
You have received machine learning model training code, without clear information about the optimal shape to run the training on. How would you proceed to identify the optimal compute shape for your model training that provides a balanced cost and processing time?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Find optimal compute shape balancing cost and time.
Approach: Iterative testing with metrics (e.g., CPU/memory usage, runtime).
Evaluate Options:
A: Tuning parameters when underutilized—focuses on model, not shape optimization.
B: Strongest shape—Costly, ignores balance; overkill likely.
C: Scale up from small shape when fully utilized—Balances cost/time effectively.
D: Random start with pre-tests—Unsystematic and inefficient.
Reasoning: C incrementally increases resources based on utilization, optimizing both factors.
Conclusion: C is correct.
OCI documentation advises: “To optimize compute shape for Jobs, start with a small shape, monitor utilization (e.g., CPU, memory) and runtime via OCI Monitoring. If fully utilized, scale up until performance plateaus—balancing cost and speed.” A misfocuses on model tuning, B wastes cost, and D lacks structure—only C aligns with this method.
1: Oracle Cloud Infrastructure Data Science Documentation, "Optimizing ComputeShapes for Jobs".
NEW QUESTION # 155
Where do calls to stdout and stderr from score.py go in the model deployment?
Answer: A
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 # 156
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