努力する人生と努力しない人生は全然違いますなので、あなたはのんびりした生活だけを楽しみしていき、更なる進歩を求めるのではないか？スマートを一方に置いて、我々Oracleの1Z0-1110-26試験問題集をピックアップします。弊社の1Z0-1110-26試験問題集によって、あなたの心と精神の満足度を向上させながら、勉強した後1Z0-1110-26試験資格認定書を受け取って努力する人生はすばらしいことであると認識られます。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: OCI Data Science Service | 30% | - Projects and notebooks
|
| Topic 2: MLOps and OCI Integration | 20% | - Automation and pipelines
|
| Topic 3: Model Development and Deployment | 30% | - Model deployment
|
| Topic 4: Machine Learning Fundamentals | 20% | - Supervised learning
|
何よりも、Oracleハイクラスの運用システムを備えているため、1Z0-1110-26学習教材を使用して1Z0-1110-26試験の準備を開始できるのは、支払い後わずか5〜10分です。 第二Jpshiken、1Z0-1110-26テスト問題の新しいバージョンをまとめたら、購入後1年間無料で最新バージョンの1Z0-1110-26トレーニング資料をお客様に送信します。 最後になりましたが、世界各地のアフターセールススタッフが、1日24時間、週7日、1Z0-1110-26トレーニングガイドでOracle Cloud Infrastructure Data Science Professionalアフターサービスを提供します。
質問 # 77
You are a data scientist working for a manufacturing company. You have developed a forecasting model to predict the sales demand in the upcoming months. You created a model artifact that contained custom logic requiring third-party libraries. When you deployed the model, it failed to run because you did not include all the third-party dependencies in the model artifact. What file should be modified to include the missing libraries?
正解：B
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Specify third-party libraries for model deployment.
Understand Artifacts: runtime.yaml defines runtime; score.py handles logic.
Evaluate Options:
A: Not a standard file—incorrect.
B: Inference code—not for dependencies.
C: Defines conda env with dependencies—correct.
D: Pip list—not used in OCI conda deployments.
Reasoning: runtime.yaml points to a conda env with all libraries.
Conclusion: C is correct.
OCI documentation states: “In runtime.yaml, specify the conda environment slug (e.g., ENVIRONMENT_SLUG: custom_env) containing all third-party libraries required by the model.” score.py (B) is for logic, requirements.txt (D) isn’t OCI-standard, and A doesn’t exist—C fixes the issue.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".
質問 # 78
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which TWO of the following prevailing metrics would you use for evaluating a multiclass classification model?
正解：A、B
解説：
Detailed Answer in Step-by-Step Solution:
Understand Multiclass Classification: Metrics evaluate how well the model predicts multiple classes.
Evaluate Metrics:
A . Mean squared error: Used for regression, not classification.
B . Explained variance score: Regression metric, not suitable.
C . Recall: Measures true positive rate per class—key for classification.
D . F1-score: Balances precision and recall—widely used in multiclass.
E . R-squared: Regression metric, not applicable.
Select Two: Recall (C) and F1-score (D) are standard for multiclass classification.
Oracle AutoML supports metrics like recall and F1-score for multiclass classification, as they assess per-class performance and overall precision-recall balance, respectively. Regression metrics (A, B,E) are irrelevant here. (Oracle Cloud Infrastructure Data Science Documentation, "AutoML Metrics").
質問 # 79
For your next data science project, you need access to public geospatial images. Which Oracle Cloud service provides free access to those images?
正解：B
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Find the OCI service for free geospatial images.
Evaluate Options:
A: Big Data Service—Spark processing, not datasets.
B: Analytics Cloud—Visualization, not data source.
C: Data Science—ML platform, not dataset provider.
D: Open Data—Free public datasets, including geospatial—correct.
Reasoning: Open Data is OCI’s public dataset hub.
Conclusion: D is correct.
OCI documentation states: “Oracle Open Data provides free access to curated datasets, including geospatial images, for public use.” A, B, and C serve other purposes—only D delivers free geospatial data.
1: Oracle Cloud Infrastructure Open Data Documentation, "Dataset Offerings".
質問 # 80
Which OCI service enables you to build, train, and deploy machine learning models in the cloud?
正解：A
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Identify the OCI service for ML model lifecycle.
Evaluate Options:
A: Data Catalog—Metadata management, not ML.
B: Data Integration—ETL, not ML.
C: Data Science—Full ML lifecycle—correct.
D: Data Flow—Spark processing, not full ML.
Reasoning: C supports building, training, deploying models.
Conclusion: C is correct.
OCI documentation states: “OCI Data Science (C) provides tools to build, train, and deploy machine learning models in the cloud, including notebooks and model catalog.” A, B, and D serve other purposes—only C fits the ML lifecycle per OCI’s offerings.
1: Oracle Cloud Infrastructure Data Science Documentation, "Service Overview".
質問 # 81
Which is NOT a part of Observability and Management Services?
正解：D
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Identify the non-Observability and Management (O&M) service in OCI.
Understand O&M: Includes monitoring, logging, events tools.
Evaluate Options:
A: Event Services—Triggers actions, part of O&M—correct.
B: OCI Management Service—Not a defined O&M service—incorrect.
C: Logging Analytics—Log analysis, O&M component—correct.
D: Logging—Log collection, O&M component—correct.
Reasoning: B isn’t listed in OCI’s O&M suite—others are.
Conclusion: B is correct (not part of O&M).
OCI documentation lists “Observability and Management Services as including Event Services (A), Logging Analytics (C), and Logging (D)—‘OCI Management Service’ (B) is not a recognized component.” B appears to be a misnomer—only A, C, D are O&M per OCI’s service catalog.
1: Oracle Cloud Infrastructure Observability and Management Documentation, "Service Overview".
質問 # 82
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