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| Section | Weight | Objectives |
|---|---|---|
| OCI Data Science Service | 30% | - Projects and notebooks
|
| Model Development and Deployment | 30% | - Model training
|
| Machine Learning Fundamentals | 20% | - Unsupervised learning
|
| MLOps and OCI Integration | 20% | - OCI ecosystem
|
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NEW QUESTION # 149
You are a data scientist building a pipeline in the Oracle Cloud Infrastructure (OCI) Data Science service for your machine learning project. You want to optimize the pipeline completion time by running some steps in parallel. Which statement is true about running pipeline steps in parallel?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Understand parallel execution in OCI Pipelines.
Evaluate Options:
A: False—Pipelines support parallelism.
B: True—DAG allows sequential/parallel steps—correct.
C: False—Not all steps must be parallel.
D: False—Independence enables parallelism.
Reasoning: B reflects OCI’s DAG-based flexibility.
Conclusion: B is correct.
OCI documentation states: “Pipeline steps can run sequentially or in parallel, defined by a directedacyclic graph (DAG) (B), allowing optimization of completion time.” A, C, and D misrepresent this—only B aligns with OCI’s pipeline design.
1: Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Parallelism".
NEW QUESTION # 150
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: B
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Artifact - score.py".
NEW QUESTION # 151
What is the primary difference between a data scientist and a data engineer?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Differentiate data scientist vs. data engineer roles.
Define Roles:
Data Engineer: Builds pipelines, prepares data.
Data Scientist: Analyzes data, builds models.
Evaluate Options:
A: Engineer preps, scientist analyzes—Correct division.
B: Reverses roles—Incorrect.
C: Overlaps roles—Scientist doesn’t typically build pipelines.
D: Misaligns—Analyst isn’t the focus.
Reasoning: A reflects standard role separation.
Conclusion: A is correct.
OCI documentation notes: “Data engineers focus on collecting and preparing data through pipelines, while data scientists analyze it to derive insights and build models.” A aligns, B inverts, C overcomplicates, and D shifts focus—only A is accurate.
1: Oracle Cloud Infrastructure Data Science Documentation, "Roles in Data Science".
NEW QUESTION # 152
You have just started as a data scientist at a healthcare company. You have been asked to analyze and improve a deep neural network model, which was built based on the electrocardiogram records of patients. There are no details about the model framework that was built. What would be the best way to find more details about the machine learning models inside the model catalog?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Context Analysis: You need to investigate an existing deep neural network model in the OCI Model Catalog with no prior information.
Understand Model Catalog: The Model Catalog stores trained models along with metadata, hyperparameters, and provenance (origin and history) details.
Evaluate Options:
A . Refer to the code inside the model: The model artifact (e.g., a serialized file like .pkl) doesn’t typically include readable source code; it’s a trained object, not the training script.
B . Check for model taxonomy details: Taxonomy (e.g., classification vs. regression) provides high-level categorization but lacks specifics like framework or architecture.
C . Check for metadata tags: Metadata includes name, description, and tags, offering some context but not detailed framework info (e.g., TensorFlow vs. PyTorch).
D . Check for provenance details: Provenance tracks the model’s creation process, including the framework, training environment, and data sources, providing the most comprehensive insight.
Reasoning: Provenance details are designed to document the “how” and “what” of model creation, making them ideal for uncovering the framework (e.g., Keras, PyTorch) and other specifics absent from initial handover.
Conclusion: D is the best approach for detailed investigation.
In OCI Data Science, the Model Catalog stores provenance information, which includes “details about the model’s origin, such as the framework used (e.g., TensorFlow, PyTorch), the training environment, and dataset references.” This is more informative than metadata tags (C), which are user-defined and less structured, or taxonomy (B), which is broad. The model artifact (A) is a binary file (e.g., pickle), not a readable codebase. Provenance (D) offers a detailed audit trail, critical for analyzing an undocumented deep neural network model like this one.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog - Provenance Details" section.
NEW QUESTION # 153
You are a data scientist working inside a notebook session and you attempt to pip install a package from a public repository that is not included in your conda environment. After running this command, you get a network timeout error. What might be missing from your networking configuration?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Fix network timeout for pip install in a notebook.
Evaluate Options:
A: FastConnect—On-premises link, not public internet.
B: VNIC—Default, not the issue.
C: NAT Gateway—Grants internet access—correct.
D: Service Gateway—OCI services, not PyPI.
Reasoning: C enables outbound traffic to public repos.
Conclusion: C is correct.
OCI documentation states: “A NAT Gateway (C) is required for notebook sessions in private subnets to access public internet repositories like PyPI.” A, B, and D don’t provide this—only C resolves the timeout.
1: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Networking".
NEW QUESTION # 154
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