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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and Set Up Data Science Workspace | 15% | - Manage access control, security, and IAM integration - Create and manage projects and notebook sessions - Configure compute shapes, storage, and networking |
| Topic 2: Integrate Related OCI Services | 10% | - Use OCI AI and data services with Data Science - Integration with OCI Object Storage, Vault, and Networking |
| Topic 3: Apply MLOps Practices | 20% | - Model monitoring, drift detection, and performance tracking - Governance, auditing, and compliance - ML pipelines, automation, and reproducibility |
| Topic 4: Implement End-to-End Machine Learning Lifecycle | 45% | - Model development, training, and evaluation - Use AutoML and built-in algorithms - Data preparation, exploration, and transformation - Deploy models and manage endpoints - Model saving, cataloging, and versioning |
| Topic 5: OCI Data Science - Introduction & Configuration | 10% | - Capabilities of the Accelerated Data Science (ADS) SDK - Overview and core concepts of OCI Data Science - Tenancy and environment configuration for Data Science |
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NEW QUESTION # 153
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which TWO of the following prevailing metrics would you use for evaluating a multiclass classification model?
Answer: D,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand Multiclass Classification: Metrics evaluate how well the model predicts multiple classes.
Evaluate Metrics:
A . Mean squared error: Used for regression, not classification.
B . Explained variance score: Regression metric, not suitable.
C . Recall: Measures true positive rate per class—key for classification.
D . F1-score: Balances precision and recall—widely used in multiclass.
E . R-squared: Regression metric, not applicable.
Select Two: Recall (C) and F1-score (D) are standard for multiclass classification.
Oracle AutoML supports metrics like recall and F1-score for multiclass classification, as they assess per-class performance and overall precision-recall balance, respectively. Regression metrics (A, B,E) are irrelevant here. (Oracle Cloud Infrastructure Data Science Documentation, "AutoML Metrics").
NEW QUESTION # 154
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 # 155
What does the Data Science Service template in Oracle Resource Manager (ORM) NOTautomatically create?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand ORM Template: It automates OCI Data Science setup with predefined configurations.
Evaluate Components:
A: User groups are created for role-based access—automated.
B: Dynamic groups (e.g., for notebook sessions) are included—automated.
C: Individual users require manual creation via IAM—not automated.
D: Basic policies (e.g., access to Data Science resources) are included—automated.
Reasoning: ORM focuses on infrastructure and permissions, not user accounts.
Conclusion: C is the exception.
The OCI Resource Manager template for Data Science “automatically provisions user groups, dynamic groups, and policies for basic use cases,” but “individual users must be created separately in IAM and assigned to groups.” C is the only item not handled by the template, per the documentation.
1: Oracle Cloud Infrastructure Resource Manager Documentation, "Data Science Template".
NEW QUESTION # 156
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 vary hyperparameters in OCI Jobs.
Evaluate Options:
A: New job per run—Wastes setup time.
B: Code changes per job—Inefficient, error-prone.
C: Flexible params per run—Efficient, reusable—correct.
D: New job per run—Redundant effort.
Reasoning: C minimizes job creation, maximizes flexibility.
Conclusion: C is correct.
OCI documentation states: “For varying hyperparameters, configure a single Job with code accepting environment variables or command-line arguments (C), set per run—most efficient.” A and D over-create jobs, B ties params to code—only C optimizes.
1: Oracle Cloud Infrastructure Data Science Documentation, "Job Parameterization".
NEW QUESTION # 157
You are working as a Data Scientist for a healthcare company. You have a series of neurophysiological data on OCI Data Science and have developed a convolutional neural network (CNN) classification model. It predicts the source of seizures in drug-resistant epileptic patients. You created a model artifact with all the necessary files. When you deployed the model, it failed to run because you did not point to the correct conda environment in the model artifact. Where would you provide instructions to use the correct conda environment?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine where to specify the conda environment for an OCI model deployment.
Understand Model Deployment: Requires artifacts like score.py and runtime.yaml to define runtime settings.
Evaluate Options:
A . score.py: Contains inference logic (e.g., load_model(), predict())—not for environment specs.
B . runtime.yaml: Defines deployment runtime, including conda environment path—correct.
C . requirements.txt: Lists pip dependencies—not used in OCI for conda environments.
D . model_artifact_validate.py: Not a standard artifact; doesn’t exist in OCI deployment.
Reasoning: runtime.yaml specifies the conda env (e.g., slug: pyspark30_p37_cpu_v2)—failure to set this causes deployment errors.
Conclusion: B is correct.
OCI documentation states: “The runtime.yaml file in a model artifact specifies the runtime environment, including the conda environment path (e.g., ENVIRONMENT_SLUG: pyspark30_p37_cpu_v2), ensuring the deployed model uses the correct dependencies.” score.py (A) handles inference, requirements.txt (C) is for pip (not conda in OCI), and D isn’t valid—only B addresses the conda issue per OCI’s deployment process.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".
NEW QUESTION # 158
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