GET READY FOR 1Z0-1110-25 WITH ORACLE'S REALISTIC EXAM QUESTIONS AND ACCURATE ANSWERS

Get Ready for 1z0-1110-25 with Oracle's Realistic Exam Questions and Accurate Answers

Get Ready for 1z0-1110-25 with Oracle's Realistic Exam Questions and Accurate Answers

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q71-Q76):

NEW QUESTION # 71
Which components are a part of the OCI Identity and Access Management service?

  • A. Compute instances
  • B. Policies
  • C. VCN
  • D. Regional subnets

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify IAM components in OCI.
* Understand IAM: Manages access via policies, groups, etc.
* Evaluate Options:
* A: Policies-Core IAM component-correct.
* B: Subnets-Networking, not IAM.
* C: Instances-Compute, not IAM.
* D: VCN-Networking, not IAM.
* Reasoning: Only A is an IAM element-others are resources.
* Conclusion: A is correct.
OCI documentation states: "IAM includes components like policies (A), groups, and compartments to control resource access." B, C, and D are infrastructure, not IAM-only A fits per OCI's IAM framework.
Oracle Cloud Infrastructure IAM Documentation, "IAM Components".


NEW QUESTION # 72
As a data scientist, you are trying to automate a machine learning (ML) workflow and have decided to use Oracle Cloud Infrastructure (OCI) AutoML Pipeline. Which THREE are part of the AutoML Pipeline?

  • A. Feature Selection
  • B. Algorithm Selection
  • C. Feature Extraction
  • D. Adaptive Sampling
  • E. Model Deployment

Answer: A,B,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three stages in OCI AutoML Pipeline.
* Understand Pipeline: Automates ML steps from data to model training.
* Evaluate Options:
* A: Feature Selection-Selects relevant features-correct.
* B: Adaptive Sampling-Reduces data size-correct.
* C: Model Deployment-Post-pipeline step-incorrect.
* D: Feature Extraction-Not explicit in OCI AutoML-incorrect.
* E: Algorithm Selection-Chooses best model-correct.
* Reasoning: A, B, E are core automated stages; C and D are separate.
* Conclusion: A, B, E are correct.
OCI documentation lists "AutoML Pipeline stages as adaptive sampling (B), feature selection (A), algorithm selection (E), and hyperparameter tuning." Deployment (C) is post-pipeline, and extraction (D) isn't highlighted-only A, B, E are included per OCI's design.
Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Components".


NEW QUESTION # 73
Arrange the following in the correct Git Repository workflow order:
* Install, configure, and authenticate Git.
* Configure SSH keys for the Git repository.
* Create a local and remote Git repository.
* Commit files to the local Git repository.
* Push the commit to the remote Git repository.

  • A. 3, 5, 1, 2, 4
  • B. 1, 2, 3, 4, 5
  • C. 4, 2, 3, 1, 5
  • D. 2, 3, 1, 4, 5

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Step 1: Install, configure, and authenticate Git: Git must be installed and configured (e.g., git config -- global user.name) before any repository actions.
* Step 2: Configure SSH keys: SSH keys are set up (e.g., ssh-keygen) and added to the remote service (e.
g., GitHub, OCI Code Repository) for secure access.
* Step 3: Create local and remote Git repository: Initialize a local repo (git init) and create/link a remote repo (e.g., git remote add origin).
* Step 4: Commit files: Add files (git add .) and commit them locally (git commit -m "message").
* Step 5: Push to remote: Push local commits to the remote repo (git push origin main).
* Evaluate Options: Only D (1, 2, 3, 4, 5) follows this logical sequence; others (e.g., A starts with SSH before Git installation) are illogical.
The standard Git workflow in OCI Data Science or general practice begins with installing Git (1), configuring SSH for secure access (2), creating repositories (3), committing locally (4), and pushing remotely (5). The OCI Code Repository documentation aligns with this: "First, install Git and configure authentication (e.g., SSH), then set up repositories and manage code." D is the only option reflecting this industry-standard process.
Oracle Cloud Infrastructure Code Repository Documentation, "Git Workflow Basics".


NEW QUESTION # 74
You have an embarrassingly parallel or distributed batch job on a large amount of data that you consider running using Data Science Jobs. What would be the best approach to run the workload?

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

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Optimize embarrassingly parallel workload in OCI Jobs.
* Evaluate Options:
* A: Sequential runs-Inefficient for parallel tasks.
* B: Simultaneous runs-Maximizes parallelism-correct.
* C: False-Jobs support parallelism.
* D: One job per run-Misstates capability, wasteful.
* Reasoning: B leverages OCI's parallel run support.
* Conclusion: B is correct.
OCI documentation states: "For embarrassingly parallel tasks, create one Job and launch multiple simultaneous Job Runs (B) to process data efficiently." A is slow, C is incorrect, and Dovercomplicates-B is the best approach.
Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Execution".


NEW QUESTION # 75
Where do calls to stdout and stderr from score.py go in a model deployment?

  • A. The predict log in the Oracle Cloud Infrastructure (OCI) Logging service as defined in the deployment
  • B. The OCI console
  • C. The OCI Cloud Shell, which can be accessed from the console
  • D. The file that was defined for them on the Virtual Machine (VM)

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Locate score.py output in OCI model deployment.
* Understand Deployment: Logs are centralized in OCI Logging.
* Evaluate Options:
* A: VM file-Not default; requires custom config-incorrect.
* B: Predict log in OCI Logging-Standard destination-correct.
* C: Cloud Shell-Separate tool, not logs-incorrect.
* D: Console-UI, not raw logs-incorrect.
* Reasoning: B aligns with OCI's logging integration.
* Conclusion: B is correct.
OCI documentation states: "score.py stdout and stderr are captured in the predict log within OCI Logging service (B), configured during deployment." A isn't standard, C and D don't receive logs-only B fits OCI's logging setup.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Logging".


NEW QUESTION # 76
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