よくできた1Z0-1110-26認定デベロッパー &資格試験におけるリーダーオファー &正確的な1Z0-1110-26模擬試験問題集

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Oracle 1Z0-1110-26 Exam Syllabus Topics:

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

>> 1Z0-1110-26認定デベロッパー <<

1Z0-1110-26模擬試験問題集 & 1Z0-1110-26無料過去問

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Oracle Cloud Infrastructure Data Science Professional 認定 1Z0-1110-26 試験問題 (Q29-Q34):

質問 # 29
Which of these protects customer data at rest and in transit in a way that allows customers to meet their security and compliance requirements for cryptographic algorithms and key management?

A. Security controls
B. Customer isolation
C. Identity Federation
D. Data encryption

正解：D

解説：
Detailed Answer in Step-by-Step Solution:
Objective: Identify protection for data at rest/transit with customer control.
Evaluate Options:
A: Controls—Broad, not specific to encryption.
B: Isolation—Separates tenants, not crypto-focused.
C: Encryption—Secures data, allows key management—correct.
D: Federation—Auth sharing, not data protection.
Reasoning: C provides crypto control (e.g., Vault keys).
Conclusion: C is correct.
OCI documentation states: “Data encryption (C) protects data at rest and in transit, with customer-managed keys in OCI Vault meeting compliance needs.” A and B are broader, D is unrelated—only C fits per OCI’s security model.
1: Oracle Cloud Infrastructure Security Documentation, "Data Encryption".

質問 # 30
You are creating an Oracle Cloud Infrastructure (OCI) Data Science job that will run on a recurring basis in a production environment. This job will pick up sensitive data from an Object Storage Bucket, train a model, and save it to the model catalog. How would you design the authentication mechanism for the job?

A. Create a pre-authenticated request (PAR) for the Object Storage bucket and use that in the job code
B. Use the resource principal of the job run as the signer in the job code, ensuring there is a dynamic group for this job run with appropriate access to Object Storage and the model catalog
C. Store your personal OCI config file and keys in the Vault, and access the Vault through the job run resource principal
D. Package your personal OCI config file and keys in the job artifact

正解：B

解説：
Detailed Answer in Step-by-Step Solution:
Objective: Securely authenticate a recurring OCI Job.
Evaluate Options:
A: PAR—Limited duration, insecure for recurring jobs.
B: Resource principal—Secure, managed auth for Jobs—correct.
C: Personal config—Unscalable, security risk.
D: Vault with personal keys—Complex, still uses user creds.
Reasoning: B uses OCI’s native, secure resource principal mechanism.
Conclusion: B is correct.
OCI documentation states: “For Jobs accessing sensitive data, use resource principals with a dynamic group (e.g., resource.type = 'datasciencejobrun') and policies granting access to Object Storage and Model Catalog—secure and scalable.” A is temporary, C and D risk credential exposure—B is best practice.
1: Oracle Cloud Infrastructure Data Science Documentation, "Job Authentication".

質問 # 31
Which technique can be used for feature engineering in the machine learning lifecycle?

A. Support Vector Machines (SVM)
B. K-means clustering
C. Principal Component Analysis (PCA)
D. Gradient boosting

正解：C

解説：
Detailed Answer in Step-by-Step Solution:
Objective: Identify a feature engineering technique in ML.
Understand Feature Engineering: Transforms raw data into model-ready features.
Evaluate Options:
A . PCA: Reduces dimensionality—feature engineering—correct.
B . K-means: Clustering model—not feature engineering.
C . SVM: Classification model—not feature engineering.
D . Gradient boosting: Model training—not feature engineering.
Reasoning: PCA creates new features via transformation—fits definition.
Conclusion: A is correct.
OCI documentation states: “Feature engineering techniques like Principal Component Analysis (PCA) (A) transform data into new features to enhance model performance.” B, C, and D are modeling techniques—only A aligns with OCI’s feature engineering stage.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Techniques".

質問 # 32
Which encryption is used for Oracle Data Science?

A. Data Encryption Standard (DES)
B. 256-bit Advanced Encryption Standard (AES-256)
C. Triple DES (TDES)
D. Rivest Shamir Adleman (RSA)
E. Twofish

正解：B

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 # 33
Which statement about Oracle Cloud Infrastructure Anomaly Detection is true?

A. It is an important tool for detecting fraud, network intrusions, and discrepancies in sensor time series analysis
B. Data used for analysis can be text or numerical in nature
C. Accepted file types are SQL and Python
D. It is trained on a combination of customer and general industry datasets

正解：A

解説：
Detailed Answer in Step-by-Step Solution:
Objective: Find a true statement about OCI Anomaly Detection.
Understand Service: Detects anomalies in multivariate data (e.g., time series).
Evaluate Options:
A: False—Accepted types are CSV/JSON, not SQL/Python.
B: Partially true—Focuses on numerical data (e.g., sensors), not text broadly.
C: True—Used for fraud, intrusions, and sensor anomalies (key use cases).
D: False—Trained on customer data only, not general datasets.
Reasoning: C aligns with documented applications; others misalign.
Conclusion: C is correct.
OCI Anomaly Detection documentation states: “The service is designed to detect anomalies in time series data, making it valuable for fraud detection, network intrusion analysis, and sensor discrepancies.” A is incorrect (file formats), B overgeneralizes (numerical focus), and D misstates training data—only C matches the service’s purpose.
1: Oracle Cloud Infrastructure Anomaly Detection Documentation, "Use Cases".

質問 # 34
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1Z0-1110-26模擬試験問題集: https://www.jpexam.com/1Z0-1110-26_exam.html