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| Section | Objectives |
|---|---|
| Implement End-to-End Machine Learning Lifecycle | - Automate machine learning workflows and pipelines - Deploy models and consume model endpoints - Prepare and manage datasets - Build, train, and evaluate machine learning models - Save and manage models using Model Catalog |
| Apply MLOps Practices | - Monitor and maintain machine learning models - Use best practices for operationalizing ML solutions - Implement model lifecycle management |
| 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 |
| 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 |
| 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 |
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NEW QUESTION # 69
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: A
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 # 70
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: D
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 # 71
You are working in your notebook session and find that your notebook session does not have enough compute CPU and memory for your workload. How would you scale up your notebook session without losing your work?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Scale up a notebook session without losing work.
Understand Persistence: Block volume stores session data (e.g., /home/datascience).
Evaluate Options:
A: Recreating work—inefficient, risks loss.
B: Local download/upload—cumbersome, unnecessary.
C: Use block volume persistence, scale up—efficient, preserves work—correct.
D: Object Storage—extra steps, not needed with block volume.
Reasoning: C leverages OCI’s built-in persistence for seamless scaling.
Conclusion: C is correct.
OCI documentation states: “Files in /home/datascience are stored on the block volume. To scale up, deactivate the session, provision a new one with a larger shape, and the block volume persists your work.” A loses data, B and D add complexity—only C is optimal.
1: Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".
NEW QUESTION # 72
Which of the following programming languages are most widely used by data scientists?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify top languages for data science.
Evaluate Options:
A: C/C++—Low-level, less common for data tasks.
B: Python (ML, libraries), R (stats), SQL (data)—Industry standards.
C: Java (enterprise), JavaScript (web)—Not data-focused.
Reasoning: B aligns with data science tools (e.g., pandas, ggplot).
Conclusion: B is correct.
OCI documentation highlights “Python, R, and SQL as the most widely used languages in Data Science for modeling, analysis, and data querying.” C/C++ (A) and Java/JS (C) are less prevalent—B matches OCI’s notebook support and industry trends.
1: Oracle Cloud Infrastructure Data Science Documentation, "Supported Languages".
NEW QUESTION # 73
You are using a custom application with third-party APIs to manage application and data hosted in an Oracle Cloud Infrastructure (OCI) tenancy. Although your third-party APIs don’t support OCI’s signature-based authentication, you want them to communicate with OCI resources. Which authentication option must you use to ensure this?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Select an auth method for third-party APIs lacking OCI signature support.
Understand OCI Auth: Typically uses API keys, but alternatives exist for non-standard APIs.
Evaluate Options:
A: Username/password—Not API-friendly, insecure.
B: API Signing Key—Requires signature-based auth, unsupported here.
C: SSH Key—For instance access, not APIs.
D: Auth Token—Simple token for API calls—correct.
Reasoning: Auth Token provides a bearer token for APIs without signature complexity.
Conclusion: D is correct.
OCI documentation states: “For third-party APIs not supporting signature-based authentication, use an Auth Token (D), a secure, revocable token for accessing OCI resources via REST APIs.” A, B, and C don’t fit non-signature scenarios—only D ensures compatibility per OCI’s IAM options.
1: Oracle Cloud Infrastructure IAM Documentation, "Auth Tokens for API Access".
NEW QUESTION # 74
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