100% Pass Perfect Oracle - 1Z0-1110-26 - Oracle Cloud Infrastructure Data Science Professional Detailed Study Dumps

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Oracle 1Z0-1110-26 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: OCI Data Science Service	30%	- Model catalog 1. Model metadata 2. Model versioning - Projects and notebooks 1. Notebook sessions 2. Conda environments
Topic 2: Machine Learning Fundamentals	20%	- Supervised learning 1. Classification 2. Regression - Unsupervised learning 1. Dimensionality reduction 2. Clustering
Topic 3: Model Development and Deployment	30%	- Model training 1. Hyperparameter optimization 2. Experiments - Model deployment 1. Deployment creation 2. Prediction endpoints
Topic 4: MLOps and OCI Integration	20%	- OCI ecosystem 1. Object Storage 2. IAM and security - Automation and pipelines 1. CI/CD integration 2. Model lifecycle management

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Oracle Cloud Infrastructure Data Science Professional Sample Questions (Q151-Q156):

NEW QUESTION # 151
True or false? Bias is a common problem in data science applications.

A. True
B. False

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Assess if bias is a common issue in data science.
Define Bias: Systematic errors in data/models (e.g., skewed training data).
Evaluate Statement:
Bias arises from unrepresentative data, poor feature selection, or algorithmic flaws—widely recognized in ML.
Examples: Gender bias in hiring models, racial bias in facial recognition.
Reasoning: Literature and practice (e.g., fairness in AI) confirm bias as prevalent.
Conclusion: A (True) is correct.
OCI documentation notes: “Bias is a common challenge in data science, stemming from imbalanced datasets or flawed assumptions, requiring techniques like re-weighting or fairness checks.” This aligns with industry standards—bias is a well-documented issue, making A true.
1: Oracle Cloud Infrastructure Data Science Documentation, "Addressing Bias in Models".

NEW QUESTION # 152
As a data scientist, you are tasked with creating a model training job that is expected to take different hyperparameter values on every run. What is the most efficient way to set those parameters with Oracle Data Science Jobs?

A. Create a new job by setting the required parameters in your code and create a new job for every code change
B. Create your code to expect different parameters as command-line arguments and create a new job every time you run the code
C. Create a new job every time you need to run your code and pass the parameters as environment variables
D. Create your code to expect different parameters either as environment variables or as command-line arguments, which are set on every job run with different values

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Efficiently vary hyperparameters in OCI Jobs.
Evaluate Options:
A: New job per run—Wastes setup time.
B: Code changes per job—Inefficient, error-prone.
C: Flexible params per run—Efficient, reusable—correct.
D: New job per run—Redundant effort.
Reasoning: C minimizes job creation, maximizes flexibility.
Conclusion: C is correct.
OCI documentation states: “For varying hyperparameters, configure a single Job with code accepting environment variables or command-line arguments (C), set per run—most efficient.” A and D over-create jobs, B ties params to code—only C optimizes.
1: Oracle Cloud Infrastructure Data Science Documentation, "Job Parameterization".

NEW QUESTION # 153
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. Algorithm Selection
B. Feature Extraction
C. Adaptive Sampling
D. Model Deployment
E. Feature Selection

Answer: A,C,E

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.
1: Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Components".

NEW QUESTION # 154
You realize that your model deployment is about to reach its utilization limit. What would you do to avoid the issue before requests start to fail? Pick THREE.

A. Update the deployment to use a larger virtual machine (more CPUs/memory)
B. Update the deployment to add more instances
C. Delete the deployment
D. Update the deployment to use fewer instances
E. Reduce the load balancer bandwidth limit so that fewer requests come in

Answer: A,B,E

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Prevent deployment failure due to high utilization.
Evaluate Options:
A: More instances—Scales capacity—correct.
B: Delete—Stops service, not a solution.
C: Fewer instances—Worsens utilization.
D: Larger VM—Increases resource capacity—correct.
E: Reduce bandwidth—Limits load—correct.
Reasoning: A and D boost capacity, E controls demand—proactive fixes.
Conclusion: A, D, E are correct.
OCI documentation advises: “To handle high utilization, increase instances (A), use a larger compute shape (D), or adjust load balancer bandwidth (E) to manage request volume.” B stops service, C reduces capacity—only A, D, E prevent failure per OCI’s scaling options.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Scaling".

NEW QUESTION # 155
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 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
B. 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
C. Reconfigure the job run because Data Science Jobs does not support embarrassingly parallel
D. Create a job in Data Science Jobs and then start the number of simultaneous job runs required for your workload

Answer: D

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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Runs".

NEW QUESTION # 156
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