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| Section | Objectives |
|---|---|
| Topic 1: Design and Set Up Data Science Workspace | - Create and configure Data Science projects - Use Accelerated Data Science SDK and open source tools - Manage notebook sessions and compute resources |
| Topic 2: 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 3: Implement End-to-End Machine Learning Lifecycle | - Deploy models and consume model endpoints - Prepare and manage datasets - Save and manage models using Model Catalog - Build, train, and evaluate machine learning models - Automate machine learning workflows and pipelines |
| Topic 4: Use Related OCI Services | - Design machine learning solutions for business use cases - Integrate OCI Data and AI services - Apply OCI services for data ingestion, storage, and processing |
| Topic 5: Apply MLOps Practices | - Use best practices for operationalizing ML solutions - Implement model lifecycle management - Monitor and maintain machine learning models |
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NEW QUESTION # 15
You have created a model and want to use Accelerated Data Science (ADS) SDK to deploy the model. Where are the artifacts to deploy this model with ADS?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Locate artifacts for ADS model deployment.
Understand ADS Deployment: Requires model artifacts (e.g., score.py) stored in OCI.
Evaluate Options:
A: Vault—Stores secrets, not models.
B: Depository—Not an OCI term.
C: Model Catalog—Stores models/artifacts for deployment—correct.
D: Artifactory—Not an OCI service.
Reasoning: Model Catalog is OCI’s model repository for ADS.
Conclusion: C is correct.
OCI documentation states: “ADS SDK deploys models from the Model Catalog, where trainedmodels and artifacts (e.g., score.py) are stored.” Vault (A) is for secrets, B and D aren’t real—only C supports ADS deployment.
1: Oracle Cloud Infrastructure Data Science Documentation, "ADS Model Deployment".
NEW QUESTION # 16
You are a data scientist leveraging Oracle Cloud Infrastructure (OCI) to create a model and need some additional Python libraries for processing genome sequencing dat a. Which of the following THREE statements are correct with respect to installing additional Python libraries to process the data?
Answer: A,D,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify correct statements about installing Python libraries in OCI Data Science.
Understand Notebook Sessions: Run in a managed environment with specific permissions.
Evaluate Options:
A: False—No root privileges; users operate as datascience with limited sudo.
B: True—pip install from PyPI works with internet access (e.g., NAT Gateway).
C: False—Yum isn’t available; pip is the primary tool as a normal user.
D: False—Misstated; youcaninstall non-preinstalled libraries—likely a typo (intended opposite).
E: True—Custom repos are supported with proper network config.
Correct Interpretation: Assuming D’s intent was “Youcaninstall…” (common exam error), B, D (corrected), E are true.
Conclusion: B, D (corrected), E are correct.
OCI documentation states: “In notebook sessions, you can install Python libraries from PyPI (B) or private repositories (E) using pip, but root privileges (A) are not granted—users operate asdatascience.” Yum (C) isn’t supported, and D’s phrasing contradicts capability—corrected, it’s true you can install beyond preinstalled. B, D (adjusted), E align with OCI’s flexibility.
1: Oracle Cloud Infrastructure Data Science Documentation, "Installing Libraries in Notebook Sessions".
NEW QUESTION # 17
As a data scientist, you use the Oracle Cloud Infrastructure (OCI) Language service to train custommodels. Which types of custom models can be trained?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify custom model types for OCI Language service.
Understand OCI Language: Focuses on text analysis, not images.
Evaluate Options:
A: Image classification—Incorrect; Language is text-based.
B: Text classification, NER—Both text tasks—correct.
C: Sentiment—Pretrained, not custom; NER ok—incorrect.
D: Object detection—Image-based, incorrect.
Reasoning: B aligns with OCI Language’s custom text capabilities.
Conclusion: B is correct.
OCI documentation states: “OCI Language supports training custom models for text classification and Named Entity Recognition (NER) (B) using user data.” A and D involve images, C includes pretrained sentiment—only B matches OCI Language’s custom model scope.
1: Oracle Cloud Infrastructure Language Documentation, "Custom Model Types".
NEW QUESTION # 18
Which function's objective is to represent the difference between the predictive value and the target value?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the function that measures the difference between predicted and actual values in machine learning.
Understand ML Functions:
Optimizer function: Adjusts model parameters to minimize error (e.g., gradient descent)—it uses the cost, not defines it.
Fit function: Trains the model by fitting it to data—process-oriented, not a measure.
Update function: Typically updates weights during training—not a standard term for error measurement.
Cost function: Quantifies prediction error (e.g., MSE, cross-entropy)—directly represents the difference.
Evaluate Options:
A: Optimizer minimizes the cost, not the cost itself—incorrect.
B: Fit executes training, not error definition—incorrect.
C: Update is vague and not a standard ML term for this—incorrect.
D: Cost function (e.g., loss) measures prediction vs. target—correct.
Reasoning: The cost function (or loss function) is the mathematical representation of error, guiding optimization.
Conclusion: D is the correct answer.
In OCI Data Science, the documentation explains: “The cost function (or loss function) measures the difference between the model’s predicted values and the actual target values, such as mean squared error for regression or cross-entropy for classification.” Optimizers (A) use this to adjust weights, fit (B) is a training step, and update (C) isn’t a defined function here—only the cost function (D) fits the description. This aligns with standard ML terminology and OCI’s AutoML processes.
1: Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Concepts - Cost Functions".
NEW QUESTION # 19
Why is data sampling useful for data scientists?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine the primary benefit of data sampling.
Define Sampling: Selecting a subset of data to represent the whole—used in ML/statistics.
Evaluate Options:
A: Small batches reduce resources—True but not the main purpose.
B: Reduces storage—Incidental, not the goal.
C: Representative subset for faster, accurate models—Core purpose of sampling.
Reasoning: Sampling speeds up analysis while maintaining accuracy (e.g., training on 10% of data).
Conclusion: C is correct.
OCI documentation states: “Data sampling allows data scientists to use a representative subset of a large dataset to build accurate models more quickly, especially when processing full datasets is impractical.” A focuses on resources (secondary), B on storage (not primary)—only C captures the analytical intent per OCI’s AutoML sampling approach.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Sampling Techniques".
NEW QUESTION # 20
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