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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q49-Q54):
NEW QUESTION # 49
You have an image classification model in the model catalog which is deployed as an HTTP endpoint using model deployments. Your tenancy administrator is seeing increased demands and has asked you to increase the load balancing bandwidth from the default of 10Mbps. You are provided with the following information:
* Payload size in KB = 1024
* Estimated requests per second = 120 requests/second (Monday through Friday, in every month, in every year)
* Buffer percentage = 20%What is the optimal load balancing bandwidth to redeploy your model?
- A. 452 Mbps
- B. 52 Mbps
- C. 1152 Mbps
- D. 7052 Mbps
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Calculate optimal bandwidth for model deployment.
* Given Data:
* Payload size = 1024 KB = 1024 * 8 = 8192 Kb (kilobits).
* Requests/sec = 120.
* Buffer = 20% = 0.2.
* Calculate Base Bandwidth:
* Bits/sec = Payload * Requests = 8192 Kb * 120 = 983,040 Kb/s = 983.04 Mbps.
* Add Buffer:
* Total = Base * (1 + Buffer) = 983.04 * 1.2 = 1179.648 Mbps.
* Evaluate Options: Closest to 1179.648 Mbps is 1152 Mbps (D)-realistic rounding.
* Conclusion: D is correct.
OCI documentation advises: "Calculate bandwidth as payload size (in bits) * requests/sec, then add a buffer (e.
g., 20%) for peak loads." Here, 1024 KB = 8192 Kb, * 120 = 983.04 Mbps, * 1.2 = 1179.648 Mbps. D (1152 Mbps) is the closest practical option-452 (A) and 52 (B) are too low, 7052 (C) excessive.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - Load Balancing".
NEW QUESTION # 50
Which function's objective is to represent the difference between the predictive value and the target value?
- A. Cost function
- B. Update function
- C. Optimizer function
- D. Fit function
Answer: A
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.
Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Concepts - Cost Functions".
NEW QUESTION # 51
How are datasets exported in the OCI Data Labeling service?
- A. As a line-delimited JSON file
- B. As a binary file
- C. As an XML file
- D. As a CSV file
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand OCI Data Labeling Export: After annotation, datasets are exported for ML use.
* Check Supported Formats: OCI Data Labeling exports annotations in a structured, machine-readable format.
* Evaluate Options:
* A: Binary isn't a standard export format for annotations.
* B: XML isn't used; JSON is preferred for flexibility.
* C: Line-delimited JSON is the correct format, aligning with ML workflows.
* D: CSV is common but not the default for OCI Data Labeling.
* Conclusion: C matches the official export format.
OCI Data Labeling exports annotated datasets as line-delimited JSON files, which store each annotation as a separate JSON object per line, suitable for ML pipelines. This is explicitly stated in the documentation.
(Reference: Oracle Cloud Infrastructure Data Labeling Service Documentation, "Exporting Datasets").
NEW QUESTION # 52
Which type of file system does File Storage use?
- A. NVMe SSD
- B. iSCSI
- C. NFSv3
- D. Paravirtualized
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the file system type for OCI File Storage.
* Understand File Storage: Network-attached storage in OCI.
* Evaluate Options:
* A: NFSv3-Network File System, used by File Storage-correct.
* B: iSCSI-Block storage protocol, not File Storage.
* C: Paravirtualized-Virtualization mode, not file system.
* D: NVMe SSD-Hardware, not file system.
* Reasoning: NFSv3 is OCI File Storage's protocol.
* Conclusion: A is correct.
OCI documentation states: "File Storage uses NFSv3 (A) as its file system protocol, providing shared storage across instances." B, C, and D are unrelated-only A aligns with OCI's File Storage design.
Oracle Cloud Infrastructure File Storage Documentation, "File System Protocol".
NEW QUESTION # 53
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?
- A. Ensure your files and environments are written to the block volume storage under the /home
/datascience directory, deactivate the notebook session, and activate the notebook with a larger compute shape selected - B. Create a temporary bucket in Object Storage, write all your files and data to Object Storage, delete the notebook session, provision a new notebook session on a larger compute shape, and copy your files and data from your temporary bucket to your new notebook session
- C. Download your files and data to your local machine, delete your notebook session, provision a new notebook session on a larger compute shape, and upload your files from your local machine to the new notebook session
- D. Deactivate your notebook session, provision a new notebook session on a larger compute shape, and recreate all your file changes
Answer: A
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.
Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".
NEW QUESTION # 54
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