1Z0-1110-26 Web-based Practice Exam

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Oracle 1Z0-1110-26 Exam Syllabus Topics:

Section	Objectives
Topic 1: Design and Set Up Data Science Workspace	- Manage notebook sessions and compute resources - Use Accelerated Data Science SDK and open source tools - Create and configure Data Science projects
Topic 2: OCI Data Science - Introduction and Configuration	- Use OCI Data Science notebooks and sessions - Understand OCI Data Science service concepts and architecture - Configure and manage Data Science resources
Topic 3: Use Related OCI Services	- Design machine learning solutions for business use cases - Apply OCI services for data ingestion, storage, and processing - Integrate OCI Data and AI services
Topic 4: Implement End-to-End Machine Learning Lifecycle	- Deploy models and consume model endpoints - Save and manage models using Model Catalog - Prepare and manage datasets - Automate machine learning workflows and pipelines - Build, train, and evaluate machine learning models
Topic 5: Apply MLOps Practices	- Use best practices for operationalizing ML solutions - Monitor and maintain machine learning models - Implement model lifecycle management

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Oracle Cloud Infrastructure Data Science Professional Sample Questions (Q47-Q52):

NEW QUESTION # 47
Which of the following programming languages are most widely used by data scientists?

A. Python, R, and SQL
B. C and C++
C. Java and JavaScript

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify top languages for data science.
Evaluate Options:
A: C/C++—Low-level, less common for data tasks.
B: Python (ML, libraries), R (stats), SQL (data)—Industry standards.
C: Java (enterprise), JavaScript (web)—Not data-focused.
Reasoning: B aligns with data science tools (e.g., pandas, ggplot).
Conclusion: B is correct.
OCI documentation highlights “Python, R, and SQL as the most widely used languages in Data Science for modeling, analysis, and data querying.” C/C++ (A) and Java/JS (C) are less prevalent—B matches OCI’s notebook support and industry trends.
1: Oracle Cloud Infrastructure Data Science Documentation, "Supported Languages".

NEW QUESTION # 48
You have received machine learning model training code, without clear information about the optimal shape to run the training. How would you proceed to identify the optimal compute shape for your model training that provides a balanced cost and processing time?

A. Start with a smaller shape and monitor the utilization metrics and time required to complete the model training. If the compute shape is fully utilized, change to compute that has more resources and rerun the job. Repeat the process until the processing time does not improve
B. Start with a smaller shape and monitor the Job Run metrics and time required to complete the model training. If the compute shape is not fully utilized, tune the model parameters, and rerun the job. Repeat the process until the shape resources are fully utilized
C. Start with a random compute shape and monitor the utilization metrics and time required to finish the model training. Perform model training optimizations and performance tests in advance to identify the right compute shape before running the model training as a job
D. Start with the strongest compute shape Jobs support and monitor the Job Run metrics and time required to complete the model training. Tune the model so that it utilizes as much compute resources as possible, even at an increased cost

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Optimize compute shape for cost and time.
Evaluate Options:
A: Tuning params—Focuses on model, not shape.
B: Strongest shape—Costly, unbalanced.
C: Scale up when utilized—Balances cost/time—correct.
D: Random start—Unsystematic.
Reasoning: C iteratively optimizes based on utilization.
Conclusion: C is correct.
OCI documentation advises: “Start with a small shape, monitor utilization and time (C); scale up if fully utilized until performance stabilizes—optimizes cost and speed.” A misfocuses, B overspends, D lacks method—only C aligns.
1: Oracle Cloud Infrastructure Data Science Documentation, "Compute Shape Optimization".

NEW QUESTION # 49
As a data scientist, you create models for cancer prediction based on mammographic images. The correct identification is very crucial in this case. After evaluating two models, you arrive at the following confusion matrix. Which model would you prefer and why?
Model 1 has Test accuracy is 80% and recall is 70%
Model 2 has Test accuracy is 75% and recall is 85%

A. Model 1, because recall has lesser impact on predictions in this use case
B. Model 2, because recall is high
C. Model 2, because recall has more impact on predictions in this use case
D. Model 1, because the test accuracy is high

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Choose the better model for cancer prediction based on metrics.
Understand Metrics:
Accuracy: Overall correct predictions.
Recall: True positives / (True positives + False negatives)—crucial for cancer (minimizing misses).
Context: Cancer prediction prioritizes recall—false negatives (missed cancers) are critical.
Evaluate Models:
Model 1: 80% accuracy, 70% recall—Misses more cancers.
Model 2: 75% accuracy, 85% recall—Misses fewer cancers.
Evaluate Options:
A: High recall—True, but lacks context.
B: High accuracy—Misses recall’s importance.
C: Recall’s impact—Correct for cancer use case—best.
D: Lesser recall impact—Incorrect for this priority.
Reasoning: C emphasizes recall’s critical role—aligns with medical needs.
Conclusion: C is correct.
OCI documentation advises: “For critical predictions like cancer detection, prioritize recall (e.g., Model 2 at 85%) over accuracy (Model 1 at 80%) to minimize false negatives, as missing cases has severe consequences (C).” A is partial, B overlooks context, D reverses priority—only C fits OCI’s ML evaluation guidance for this scenario.
1: Oracle Cloud Infrastructure Data Science Documentation, "Evaluating Classification Models".

NEW QUESTION # 50
You are a computer vision engineer building an image recognition model. You decide to use Oracle Data Labeling to annotate your image dat a. Which of the following THREE are possible ways to annotate an image in Data Labeling?

A. Adding labels to an image using object detection, by drawing bounding boxes to an image
B. Adding multiple labels to an image
C. Adding labels to an image by drawing a bounding box to an image is not supported by Data Labeling
D. Adding a single label to an image
E. Adding labels to an image using semantic segmentation, by drawing multiple bounding boxes to an image

Answer: A,B,D

NEW QUESTION # 51
Using Oracle AutoML, you are tuning hyperparameters on a supported model class and have specified a time budget. AutoML terminates computation once the time budget is exhausted. What would you expect AutoML to return in case the time budget is exhausted before hyperparameter tuning is completed?

A. A random hyperparameter configuration
B. The current best-known hyperparameter configuration
C. The last generated hyperparameter configuration
D. A hyperparameter configuration with a minimum learning rate

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Predict AutoML’s output when time runs out during tuning.
Understand AutoML Tuning: Iteratively tests hyperparameters, tracks best results.
Evaluate Options:
A: Best-known config—Logical, reflects optimization goal—correct.
B: Last config—Ignores prior better results—incorrect.
C: Minimum learning rate—Arbitrary, not performance-based.
D: Random—Defeats tuning purpose.
Reasoning: AutoML prioritizes the best config found within the budget.
Conclusion: A is correct.
OCI AutoML documentation states: “If the time budget expires, AutoML returns the best hyperparameter configuration (A) identified during tuning based on performance metrics.” Last (B), minimum (C), or random (D) configs aren’t selected—only A aligns with OCI’s optimization strategy.
1: Oracle Cloud Infrastructure AutoML Documentation, "Hyperparameter Tuning - Time Budget".

NEW QUESTION # 52
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