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| Section | Weight | Objectives |
|---|---|---|
| Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| Implement End-to-End Machine Learning Lifecycle | 45% | - Use AutoML and built-in algorithms - Data preparation, exploration, and transformation - Model saving, cataloging, and versioning - Model development, training, and evaluation - Deploy models and manage endpoints |
| Design and Set Up Data Science Workspace | 15% | - Manage access control, security, and IAM integration - Configure compute shapes, storage, and networking - Create and manage projects and notebook sessions |
| Apply MLOps Practices | 20% | - Governance, auditing, and compliance - Model monitoring, drift detection, and performance tracking - ML pipelines, automation, and reproducibility |
| OCI Data Science - Introduction & Configuration | 10% | - Capabilities of the Accelerated Data Science (ADS) SDK - Tenancy and environment configuration for Data Science - Overview and core concepts of OCI Data Science |
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NEW QUESTION # 84
On which option do you set Oracle Cloud Infrastructure Budget?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine where OCI budgets are set.
Understand Budgets: Track spending across OCI resources.
Evaluate Options:
A: Compartments—Scoped within tenancy, not budget root.
B: Instances—Specific resources, not budget scope.
C: Tags—Filter costs, not budget setting.
D: Tenancy—Top-level scope for budgets—correct.
Reasoning: Budgets apply at tenancy, optionally filtered (e.g., by compartment).
Conclusion: D is correct.
OCI documentation states: “Budgets are set at the tenancy level (D), with optional filters like compartments or tags to monitor spending.” A, B, and C are sub-elements—only D is the primary scope per OCI’s cost management.
1: Oracle Cloud Infrastructure Cost Management Documentation, "Setting Budgets".
NEW QUESTION # 85
True or false? Data scientists typically need a combination of technical skills, nontechnical ones, and suitable personality traits to be successful.
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Assess required skills for data scientists.
Analyze Skills:
Technical: Coding, stats, ML.
Nontechnical: Communication, business acumen.
Traits: Curiosity, problem-solving.
Reasoning: Success requires this mix—e.g., explaining models to stakeholders.
Conclusion: A (True) is correct.
OCI documentation states: “Effective data scientists combine technical skills (e.g., Python), nontechnical skills (e.g., storytelling), and traits like analytical thinking.” This holistic requirement is true (A), not false (B).
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Scientist Skills".
NEW QUESTION # 86
You are working as a data scientist for a healthcare company. They decided to analyze the data to find patterns in a large volume of electronic medical records. You are asked to build a PySpark solution to analyze these records in a JupyterLab notebook. What is the order of recommended steps to develop a PySpark application in OCI Data Science?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Sequence steps for a PySpark app in OCI Data Science.
Evaluate Steps:
Launch notebook: First—provides the environment.
Install PySpark conda: Second—sets up Spark libraries.
Configure core-site.xml: Third—connects to data (e.g., Object Storage).
Develop app: Fourth—writes the PySpark code.
Data Flow: Fifth—optional scaling, post-development.
Check Options: D (1, 2, 3, 4, 5) matches this logical flow.
Reasoning: Notebook first, then setup, coding, and scaling.
Conclusion: D is correct.
OCI documentation recommends: “1) Launch a notebook session, 2) install a PySpark conda environment, 3) configure core-site.xml for data access, 4) develop your PySpark application, and 5) optionally use Data Flow for scale.” D follows this—others (A, B, C) misorder critical steps like launching the notebook.
1: Oracle Cloud Infrastructure Data Science Documentation, "PySpark in Notebooks".
NEW QUESTION # 87
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 # 88
During a job run, you receive an error message that no space is left on your disk device. To solve the problem, you must increase the size of the job storage. What would be the most efficient way to do this with Data Science Jobs?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Efficiently increase storage for an OCI Job.
Understand Jobs: Storage (block volume) is set at job creation, not dynamically adjustable.
Evaluate Options:
A: False—Jobs can’t edit storage post-creation; it’s fixed.
B: False—No environment variable adjusts storage size.
C: True—Create a new job with larger storage (e.g., 200 GB) and run it.
D: False—Refactoring code is inefficient compared to increasing storage.
Reasoning: C is the standard OCI process for adjusting resources.
Conclusion: C is correct.
OCI documentation states: “Storage size for a Data Science Job is specified during job creation (e.g., block volume size). To increase it, create a new job with a larger storage configuration and initiate a new run.” Editing (A) isn’t supported, variables (B) don’t apply, and refactoring (D) avoids the issue—only C is efficient.
1: Oracle Cloud Infrastructure Data Science Documentation, "Jobs - Storage Configuration".
NEW QUESTION # 89
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