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| Section | Weight | Objectives |
|---|---|---|
| Machine Learning Fundamentals | 20% | - Supervised learning
|
| MLOps and OCI Integration | 20% | - Automation and pipelines
|
| OCI Data Science Service | 30% | - Projects and notebooks
|
| Model Development and Deployment | 30% | - Model training
|
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質問 # 152
Which encryption is used for Oracle Data Science?
正解：D
解説：
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B: DES—Outdated, weak—incorrect.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 B, C, D, and E are either outdated or not used—only A matches OCI’s encryption policy.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Encryption".
質問 # 153
You have received machine learning model training code, without clear information about the optimal shape to run the training on. How would you proceed to identify the optimal compute shape for your model training that provides a balanced cost and processing time?
正解：B
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Find optimal compute shape balancing cost and time.
Approach: Iterative testing with metrics (e.g., CPU/memory usage, runtime).
Evaluate Options:
A: Tuning parameters when underutilized—focuses on model, not shape optimization.
B: Strongest shape—Costly, ignores balance; overkill likely.
C: Scale up from small shape when fully utilized—Balances cost/time effectively.
D: Random start with pre-tests—Unsystematic and inefficient.
Reasoning: C incrementally increases resources based on utilization, optimizing both factors.
Conclusion: C is correct.
OCI documentation advises: “To optimize compute shape for Jobs, start with a small shape, monitor utilization (e.g., CPU, memory) and runtime via OCI Monitoring. If fully utilized, scale up until performance plateaus—balancing cost and speed.” A misfocuses on model tuning, B wastes cost, and D lacks structure—only C aligns with this method.
1: Oracle Cloud Infrastructure Data Science Documentation, "Optimizing ComputeShapes for Jobs".
質問 # 154
You are a data scientist with a set of text and image files that need annotation, and you want to use Oracle Cloud Infrastructure (OCI) Data Labeling. Which of the following THREE annotation classes are supported by the tool?
正解：B、C、D
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Identify supported annotation classes in OCI Data Labeling.
Understand Tool: Supports image/text annotations for ML.
Evaluate Options:
A: Object detection—Yes (bounding boxes).
B: Named entity—Text-specific, not primary for images.
C: Classification—Yes (labels for images/text).
D: Key-point—Not listed in OCI docs.
E: Polygonal—Not explicitly supported.
F: Semantic segmentation—Yes (pixel-level).
Reasoning: A, C, F match OCI’s image/text focus.
Conclusion: A, C, F are correct.
OCI Data Labeling supports “object detection (A), classification (C), and semantic segmentation (F) for images and text,” per documentation. B is text-specific, D and E aren’t highlighted—only A, C, F are core classes.
1: Oracle Cloud Infrastructure Data Labeling Documentation, "Annotation Types".
質問 # 155
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which of the following TWO prevailing metrics would you use for evaluating a multiclass classification model?
正解：A、C
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Select two metrics for multiclass classification in AutoML.
Understand Multiclass Metrics: Focus on class-specific performance—classification, not regression.
Evaluate Options:
A . Recall: Measures true positives per class—key for multiclass—correct.
B . Mean squared error: Regression metric—incorrect.
C . F1 Score: Balances precision and recall—standard for multiclass—correct.
D . R-Squared: Regression fit—incorrect.
E . Explained variance: Regression metric—incorrect.
Reasoning: A and C assess classification accuracy across multiple classes—fit AutoML’s evaluation.
Conclusion: A and C are correct.
OCI AutoML documentation states: “For multiclass classification, common evaluation metrics include recall (A) for per-class sensitivity and F1 Score (C) for balanced performance.” B, D, and E are regression-focused—only A and C are supported and relevant per OCI’s AutoML metrics suite.
1: Oracle Cloud Infrastructure AutoML Documentation, "Evaluation Metrics for Classification".
質問 # 156
You are a data scientist working inside a notebook session and you attempt to pip install a package from a public repository that is not included in your conda environment. After running this command, you get a network timeout error. What might be missing from your network configuration?
正解：C
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Diagnose a network timeout during pip install in a notebook session.
Understand Notebook Networking: Sessions run in a VCN; internet access requires specific configs.
Analyze Timeout: Indicates failure to reach the public PyPI repository—likely no internet outbound route.
Evaluate Options:
A: NAT Gateway—Provides internet access for private subnets—correct fix.
B: Service Gateway—Accesses OCI services privately, not public internet.
C: FastConnect—Links to on-premises, not public internet.
D: VNIC—Essential but present by default; doesn’t solve internet access.
Reasoning: NAT Gateway enables outbound traffic to public repos like PyPI.
Conclusion: A is correct.
OCI documentation notes: “Notebook sessions in a private subnet require a NAT Gateway to access public internet resources, such as PyPI, for package installation via pip. Without it, network timeouts occur.” Service Gateway (B) is for OCI services, FastConnect (C) is irrelevant, and VNIC (D) is standard—only A resolves the issue.
1: Oracle Cloud Infrastructure Data Science Documentation, "Networking for Notebook Sessions".
質問 # 157
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ご存知のように、私たちは今、非常に大きな競争圧力に直面しています。欲しいものを手に入れるにはもっと力が必要です。1Z0-1110-26無料の試験ガイドがこれらを提供するかもしれません。教材を使用すると、Oracle Cloud認定資格を取得できます。これにより、多くの競合他社の中で、あなたの能力がより明確になります。 1Z0-1110-26練習ファイルを使用することは、ソフトパワーを向上させるための重要なステップです。業界の他の製品と比較して、1Z0-1110-26学習教材が顧客を引き付けるために必要なものを理解するのに少し時間を割いていただければ幸いです。
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