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NEW QUESTION # 132
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?
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 # 133
Where do calls to stdout and stderr from score.py go in the model deployment?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Locate stdout/stderr output from score.py in deployment.
* Understand Deployment: score.py runs in a model endpoint; logs are managed by OCI.
* Evaluate Options:
* A: False-No VM file is defined by default; logs go to OCI Logging.
* B: False-Console displays UI, not raw logs.
* C: False-Cloud Shell is a separate tool, not a log destination.
* D: True-Predict logs in OCI Logging capture stdout/stderr.
* Reasoning: OCI centralizes logs in its Logging service for deployments.
* Conclusion: D is correct.
OCI documentation states: "During model deployment, stdout and stderr from score.py are automatically sent to the predict log in the OCI Logging service, as configured in the deployment settings." A, B, and C don't align with this managed logging approach-only D is accurate.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - Logging".
NEW QUESTION # 134
After you have created and opened a notebook session, you want to use the Accelerated Data Science (ADS) SDK to access your data and get started with exploratory data analysis. From which TWO places can you access the ADS SDK?
Answer: D,E
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Locate sources for ADS SDK in OCI.
* Understand ADS SDK: A Python library for Data Science tasks (e.g., EDA).
* Evaluate Options:
* A: Big Data Service-Spark-focused, not ADS source.
* B: Machine Learning-Separate service, not ADS-related.
* C: Conda in OCI Data Science-Preinstalled ADS in notebook sessions.
* D: PyPI-Public source to install ADS (pip install oracle-ads).
* E: ADW-Database, not an SDK source.
* Reasoning: C (preinstalled) and D (installable) are practical access points.
* Conclusion: C and D are correct.
OCI documentation states: "The ADS SDK is available in OCI Data Science notebook sessions via preinstalled conda environments (C) and can be installed from PyPI (D) using pip install oracle-ads." Big Data (A), Machine Learning (B), and ADW (E) don't host ADS-only C and D apply.
Oracle Cloud Infrastructure Data Science Documentation, "ADS SDK Installation".
NEW QUESTION # 135
You want to build a multistep machine learning workflow by using the Oracle Cloud Infrastructure (OCI) Data Science Pipeline feature. How would you configure the conda environment to run a pipeline step?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Configure conda env for a pipeline step.
* Evaluate Options:
* A: Shape-Infra, not env config.
* B: Volume-Storage, not env.
* C: Command-line-Step args, not env.
* D: Env variables-Sets conda path-correct.
* Reasoning: D specifies runtime env (e.g., CONDA_ENV_SLUG).
* Conclusion: D is correct.
OCI documentation states: "Configure a pipeline step's conda environment using environment variables (D), such as CONDA_ENV_SLUG, in the step definition." A, B, and C address other aspects-only D fits env config.
Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Step Configuration".
NEW QUESTION # 136
Which encryption is used for Oracle Data Science?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify encryption standard for OCI Data Science.
* Understand OCI Encryption: Applies to data at rest and in transit.
* Evaluate Options:
* A: AES-256-Industry-standard, OCI default-correct.
* B: DES-Outdated, weak-incorrect.
* C: TDES-Older, less secure-incorrect.
* D: Twofish-Not OCI standard-incorrect.
* E: RSA-Asymmetric, not primary for data at rest-incorrect.
* Reasoning: AES-256 is OCI's go-to for Data Science resources.
* Conclusion: A is correct.
OCI documentation states: "Data Science services encrypt data at rest using AES-256 (A), ensuring high security for notebooks, jobs, and models." B, C, D, and E are either outdated or not used-only A matches OCI's encryption policy.
Oracle Cloud Infrastructure Data Science Documentation, "Data Encryption".
NEW QUESTION # 137
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