ORACLE NEW 1Z0-1110-25 EXAM NOTES: ORACLE CLOUD INFRASTRUCTURE 2025 DATA SCIENCE PROFESSIONAL - EASY4ENGINE INSTANT DOWNLOAD

Oracle New 1z0-1110-25 Exam Notes: Oracle Cloud Infrastructure 2025 Data Science Professional - Easy4Engine Instant Download

Oracle New 1z0-1110-25 Exam Notes: Oracle Cloud Infrastructure 2025 Data Science Professional - Easy4Engine Instant Download

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q113-Q118):

NEW QUESTION # 113
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?

  • A. On the job run, set the environment variable that helps increase the size of the storage
  • B. Your code using too much disk space. Refactor the code to identify the problem
  • C. Edit the job, change the size of the storage of your job, and start a new job run
  • D. Create a new job with increased storage size and then run the job

Answer: D

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.
Oracle Cloud Infrastructure Data Science Documentation, "Jobs - Storage Configuration".


NEW QUESTION # 114
You have an embarrassingly parallel or distributed batch job with a large amount of data running using Data Science Jobs. What would be the best approach to run the workload?

  • A. Create the job in Data Science Jobs and start a job run. When it is done, start a new job run until you achieve the number of runs required
  • B. Create a job in Data Science Jobs and then start the number of simultaneous job runs required for your workload
  • C. Create a new job for every job run that you have to run in parallel, because the Data Science Job service can have only one job per job
  • D. Reconfigure the job run because Data Science Jobs does not support embarrassingly parallel

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Optimize an embarrassingly parallel job in OCI Data Science Jobs.
* Define Embarrassingly Parallel: Tasks are independent, ideal for simultaneous runs.
* Evaluate Options:
* A: Multiple simultaneous runs-Leverages parallelism-correct.
* B: One job per run-Misstates capability; unnecessary complexity.
* C: Sequential runs-Inefficient, ignores parallelism.
* D: False-Jobs support parallelism.
* Reasoning: A maximizes efficiency for parallel tasks.
* Conclusion: A is correct.
OCI documentation states: "For embarrassingly parallel workloads, create a single Job and launch multiple simultaneous Job Runs to process data in parallel." B misinterprets limits, C wastes time, and D denies capability-only A fits OCI's design.
Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Runs".


NEW QUESTION # 115
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. 1152 Mbps
  • B. 7052 Mbps
  • C. 52 Mbps
  • D. 452 Mbps

Answer: A

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 # 116
You have created a model and want to use Accelerated Data Science (ADS) SDK to deploy the model. Where are the artifacts to deploy this model with ADS?

  • A. Data Science Artifactory
  • B. Model Catalog
  • C. Model Depository
  • D. OCI Vault

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Locate artifacts for ADS model deployment.
* Understand ADS Deployment: Requires model artifacts (e.g., score.py) stored in OCI.
* Evaluate Options:
* A: Vault-Stores secrets, not models.
* B: Depository-Not an OCI term.
* C: Model Catalog-Stores models/artifacts for deployment-correct.
* D: Artifactory-Not an OCI service.
* Reasoning: Model Catalog is OCI's model repository for ADS.
* Conclusion: C is correct.
OCI documentation states: "ADS SDK deploys models from the Model Catalog, where trainedmodels and artifacts (e.g., score.py) are stored." Vault (A) is for secrets, B and D aren't real-only C supports ADS deployment.
Oracle Cloud Infrastructure Data Science Documentation, "ADS Model Deployment".


NEW QUESTION # 117
Which technique can be used for feature engineering in the machine learning lifecycle?

  • A. Principal Component Analysis (PCA)
  • B. Support Vector Machines (SVM)
  • C. Gradient boosting
  • D. K-means clustering

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a feature engineering technique in ML.
* Understand Feature Engineering: Transforms raw data into model-ready features.
* Evaluate Options:
* A. PCA: Reduces dimensionality-feature engineering-correct.
* B. K-means: Clustering model-not feature engineering.
* C. SVM: Classification model-not feature engineering.
* D. Gradient boosting: Model training-not feature engineering.
* Reasoning: PCA creates new features via transformation-fits definition.
* Conclusion: A is correct.
OCI documentation states: "Feature engineering techniques like Principal Component Analysis (PCA) (A) transform data into new features to enhance model performance." B, C, and D are modeling techniques-only A aligns with OCI's feature engineering stage.
Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Techniques".


NEW QUESTION # 118
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