Practice Oracle Cloud 1z0-1110-25 exam. Online Exam Practice Tests with detailed explanations! Pass 1z0-1110-25 with confidence!
1z0-1110-25 - Oracle Cloud Infrastructure 2025 Data Science Professional Practice Tests 2025 | Exam4Free
NEW QUESTION # 58
You are a data scientist designing an air traffic control model, and you choose to leverage Oracle AutoML.
You understand that the Oracle AutoML pipeline consists of multiple stages and automatically operates in a certain sequence. What is the correct sequence for the Oracle AutoML pipeline?
- A. Adaptive sampling, Algorithm selection, Feature selection, Hyperparameter tuning
- B. Algorithm selection, Feature selection, Adaptive sampling, Hyperparameter tuning
- C. Adaptive sampling, Feature selection, Algorithm selection, Hyperparameter tuning
- D. Algorithm selection, Adaptive sampling, Feature selection, Hyperparameter tuning
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Sequence OCI AutoML pipeline stages.
* Stages:
* Adaptive sampling: Reduces data size if large.
* Feature selection: Picks relevant features.
* Algorithm selection: Chooses best model type.
* Hyperparameter tuning: Optimizes model params.
* Evaluate: C (sampling, features, algorithms, tuning) matches logical flow-data first, then model.
* Reasoning: Sampling precedes feature work-standard in OCI.
* Conclusion: C is correct.
OCI documentation states: "AutoML pipeline runs 1) adaptive sampling, 2) feature selection, 3) algorithm selection, 4) hyperparameter tuning (C)." Sampling reduces data first, then features and models are optimized-other orders (A, B, D) misalign with OCI's sequence.
Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Sequence".
NEW QUESTION # 59
Which encryption is used for Oracle Data Science?
- A. Data Encryption Standard (DES)
- B. Triple DES (TDES)
- C. 256-bit Advanced Encryption Standard (AES-256)
- D. Twofish
- E. Rivest Shamir Adleman (RSA)
Answer: C
Explanation:
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* B: DES-Outdated, weak-incorrect.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.
Oracle Cloud Infrastructure Data Science Documentation, "Data Encryption".
NEW QUESTION # 60
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?
- A. Service Gateway with private subnet access
- B. FastConnect to an on-premises network
- C. The NAT Gateway with public internet access
- D. Primary Virtual Network Interface Card (VNIC)
Answer: C
Explanation:
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.
Oracle Cloud Infrastructure Data Science Documentation, "Networking for Notebook Sessions".
NEW QUESTION # 61
You want to build a multistep machine learning workflow by using the Oracle Cloud Infrastructure (OCI) Data Science Pipeline feature. How would you configure the conda environment to run a pipeline step?
- A. Use environmental variables
- B. Configure a block volume
- C. Use command-line variables
- D. Configure a compute shape
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Configure conda env for a pipeline step.
* Evaluate Options:
* A: Shape-Infra, not env config.
* B: Volume-Storage, not env.
* C: Command-line-Step args, not env.
* D: Env variables-Sets conda path-correct.
* Reasoning: D specifies runtime env (e.g., CONDA_ENV_SLUG).
* Conclusion: D is correct.
OCI documentation states: "Configure a pipeline step's conda environment using environment variables (D), such as CONDA_ENV_SLUG, in the step definition." A, B, and C address other aspects-only D fits env config.
Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Step Configuration".
NEW QUESTION # 62
How are datasets exported in the OCI Data Labeling service?
- A. As a binary file
- B. As an XML file
- C. As a line-delimited JSON file
- D. As a CSV file
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand OCI Data Labeling Export: After annotation, datasets are exported for ML use.
* Check Supported Formats: OCI Data Labeling exports annotations in a structured, machine-readable format.
* Evaluate Options:
* A: Binary isn't a standard export format for annotations.
* B: XML isn't used; JSON is preferred for flexibility.
* C: Line-delimited JSON is the correct format, aligning with ML workflows.
* D: CSV is common but not the default for OCI Data Labeling.
* Conclusion: C matches the official export format.
OCI Data Labeling exports annotated datasets as line-delimited JSON files, which store each annotation as a separate JSON object per line, suitable for ML pipelines. This is explicitly stated in the documentation.
(Reference: Oracle Cloud Infrastructure Data Labeling Service Documentation, "Exporting Datasets").
NEW QUESTION # 63
You have a complex Python code project that could benefit from using Data Science Jobs as it is a repeatable machine learning model training task. The project contains many sub-folders and classes. What is the best way to run this project as a Job?
- A. Rewrite your code so that it is a single executable Python or Bash/Shell script file
- B. ZIP the entire code project folder, upload it as a Job artifact on job creation, and set JOB_RUN_ENTRYPOINT to point to the main executable file
- C. ZIP the entire code project folder and upload it as a Job artifact. Jobs automatically identifies the main top-level where the code is run
- D. ZIP the entire code project folder and upload it as a Job artifact on job creation. Jobs identifies the main executable file automatically
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Run a complex Python project as an OCI Job.
* Evaluate Options:
* A: Auto-identification-False; entrypoint must be set.
* B: Rewrite-Unnecessary, inefficient.
* C: Auto-executable-False; needs explicit entrypoint.
* D: ZIP with entrypoint-Correct, flexible approach.
* Reasoning: D preserves structure, specifies execution.
* Conclusion: D is correct.
OCI documentation states: "For complex projects, ZIP the folder and upload as a Job artifact, then set JOB_RUN_ENTRYPOINT (D) to the main executable (e.g., main.py)." Auto-detection (A, C) isn't supported, and B discards structure-D is best.
Oracle Cloud Infrastructure Data Science Documentation, "Job Artifacts".
NEW QUESTION # 64
Select two reasons why it is important to rotate encryption keys when using Oracle Cloud Infrastructure (OCI) Vault to store credentials or other secrets.
- A. Key rotation allows you to encrypt no more than five keys at a time
- B. Periodically rotating keys limits the amount of data encrypted by one key version
- C. Periodically rotating keys makes it easier to reuse keys
- D. Key rotation improves encryption efficiency
- E. Key rotation reduces risk if a key is ever compromised
Answer: B,E
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify two reasons for key rotation in OCI Vault.
* Understand Key Rotation: Enhances security by updating keys.
* Evaluate Options:
* A: Five-key limit-False, no such restriction.
* B: Efficiency-False, not the purpose.
* C: Reuse-False, rotation prevents reuse.
* D: Reduces risk-True, limits exposure-correct.
* E: Limits data-True, reduces breach scope-correct.
* Reasoning: D and E are security-focused-key Vault benefits.
* Conclusion: D and E are correct.
OCI documentation states: "Key rotation in Vault (D) reduces risk if a key is compromised and (E) limits the data encrypted by a single key version, enhancing security." A, B, and C misrepresent rotation's purpose- only D and E align with OCI's Vault best practices.
Oracle Cloud Infrastructure Vault Documentation, "Key Rotation Benefits".
NEW QUESTION # 65
You are using a custom application with third-party APIs to manage application and data hosted in an Oracle Cloud Infrastructure (OCI) tenancy. Although your third-party APIs don't support OCI's signature-based authentication, you want them to communicate with OCI resources. Which authentication option must you use to ensure this?
- A. Auth Token
- B. API Signing Key
- C. SSH Key Pair with 2048-bit algorithm
- D. OCI username and password
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select an auth method for third-party APIs lacking OCI signature support.
* Understand OCI Auth: Typically uses API keys, but alternatives exist for non-standard APIs.
* Evaluate Options:
* A: Username/password-Not API-friendly, insecure.
* B: API Signing Key-Requires signature-based auth, unsupported here.
* C: SSH Key-For instance access, not APIs.
* D: Auth Token-Simple token for API calls-correct.
* Reasoning: Auth Token provides a bearer token for APIs without signature complexity.
* Conclusion: D is correct.
OCI documentation states: "For third-party APIs not supporting signature-based authentication, use an Auth Token (D), a secure, revocable token for accessing OCI resources via REST APIs." A, B, and C don't fit non- signature scenarios-only D ensures compatibility per OCI's IAM options.
Oracle Cloud Infrastructure IAM Documentation, "Auth Tokens for API Access".
NEW QUESTION # 66
Which Oracle Cloud Infrastructure (OCI) Data Science policy is invalid?
- A. Allow group DataScienceGroup to use data-science-model-sessions in compartment DataScience
- B. Allow dynamic-group DataScienceDynamicGroup to manage data-science-projects in compartment DataScience
- C. Allow dynamic-group DataScienceDynamicGroup to manage data-science-family in compartment DataScience
- D. Allow group DataScienceGroup to use virtual-network-family in compartment DataScience
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Spot the invalid OCI IAM policy.
* Understand Policies: Use verbs (e.g., use, manage) and resource types.
* Evaluate Options:
* A: Valid-virtual-network-family is a resource type.
* B: Invalid-No data-science-model-sessions resource type; likely meant data-science-models.
* C: Valid-data-science-projects is correct.
* D: Valid-data-science-family aggregates Data Science resources.
* Reasoning: B uses a nonexistent resource type-error in syntax.
* Conclusion: B is incorrect.
OCI IAM documentation lists valid resource types: "data-science-models, data-science-projects, data-science- family-but not data-science-model-sessions (B)." A, C, D are correct-B is invalid due to this typo
/misunderstanding.
Oracle Cloud Infrastructure IAM Documentation, "Data Science Resource Types".
NEW QUESTION # 67
Which Security Zone policy is NOT valid?
- A. A compute instance cannot be moved from a security zone to a standard compartment
- B. Resources in a security zone should not be accessible from the public internet
- C. Resources in a security zone must be automatically backed up regularly
- D. A boot volume can be moved from a security zone to a standard compartment
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Spot the invalid Security Zone policy.
* Understand Security Zones: Enforce strict security-resources can't leave easily.
* Evaluate Options:
* A: Boot volume move-Not allowed; violates zone rules-invalid.
* B: Compute no-move-True, enforces security.
* C: No public access-True, standard policy.
* D: Auto-backup-True, required for compliance.
* Reasoning: A contradicts Security Zone immutability.
* Conclusion: A is incorrect.
OCI documentation states: "Resources like boot volumes (A) cannot be moved from a Security Zone to a standard compartment-B, C, and D are enforced policies." A violates OCI's Security Zone restrictions- only A is not valid.
Oracle Cloud Infrastructure Security Zones Documentation, "Policy Restrictions".
NEW QUESTION # 68
You have an embarrassingly parallel or distributed batch job on a large amount of data that you consider running using Data Science Jobs. What would be the best approach to run the workload?
- A. Reconfigure the job run because Data Science Jobs does not support embarrassingly parallel workloads
- B. Create a new job for every job run that you have to run in parallel, because the Data Science Jobs service can have only one job run per job
- C. Create the job in Data Science Jobs and then start the number of simultaneous job runs required for your workload
- D. 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
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Optimize embarrassingly parallel workload in OCI Jobs.
* Evaluate Options:
* A: Sequential runs-Inefficient for parallel tasks.
* B: Simultaneous runs-Maximizes parallelism-correct.
* C: False-Jobs support parallelism.
* D: One job per run-Misstates capability, wasteful.
* Reasoning: B leverages OCI's parallel run support.
* Conclusion: B is correct.
OCI documentation states: "For embarrassingly parallel tasks, create one Job and launch multiple simultaneous Job Runs (B) to process data efficiently." A is slow, C is incorrect, and Dovercomplicates-B is the best approach.
Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Execution".
NEW QUESTION # 69
A bike sharing platform has collected user commute data for the past 3 years. For increasing profitability and making useful inferences, a machine learning model needs to be built from the accumulated data. Which of the following options has the correct order of the required machine learning tasks for building a model?
- A. Data Access, Data Exploration, Feature Engineering, Feature Exploration, Modeling
- B. Data Access, Feature Exploration, Data Exploration, Feature Engineering, Modeling
- C. Data Access, Data Exploration, Feature Exploration, Feature Engineering, Modeling
- D. Data Access, Feature Exploration, Feature Engineering, Data Exploration, Modeling
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Data Access: The first step in any machine learning workflow is accessing the raw data. This involves retrieving the user commute data collected over the past 3 years from the bike-sharing platform's storage system.
* Data Exploration: Once data is accessed, it's explored to understand its structure, quality, and patterns (e.g., missing values, distributions). This step helps identify what preprocessing is needed.
* Feature Engineering: After understanding the data, features are created or transformed (e.g., commute duration, time of day) to improve model performance. This step precedes feature exploration because you need engineered features to analyze further.
* Feature Exploration: This involves analyzing the engineered features (e.g., correlation analysis, importance ranking) to refine them or select the most relevant ones for modeling.
* Modeling: Finally, the prepared data and features are used to train and evaluate a machine learning model.
Option C (Data Access, Data Exploration, Feature Engineering, Feature Exploration, Modeling) follows this logical sequence, aligning with standard ML workflows.
The correct order reflects the machine learning lifecycle as outlined in Oracle's OCI Data Science documentation. Data Access is the initial step to retrieve data, followed by Data Exploration to assess it (e.g., using OCI Data Science Notebook Sessions with tools like pandas). Feature Engineering transforms raw data into meaningful inputs, followed by Feature Exploration to analyze feature importance (e.g., using ADS SDK' s correlation tools). Modeling is the final step where the model is built and trained. This sequence is consistent with Oracle's recommended practices for building ML models in OCI Data Science (Reference: Oracle Cloud Infrastructure Data Science Service Documentation, "Machine Learning Lifecycle").
NEW QUESTION # 70
You have an image classification model in the model catalog which is deployed as an HTTP endpoint using model deployments. Your tenancy administrator is seeing increased demands and has asked you to increase the load balancing bandwidth from the default of 10Mbps. You are provided with the following information:
* Payload size in KB = 1024
* Estimated requests per second = 120 requests/second (Monday through Friday, in every month, in every year)
* Buffer percentage = 20%What is the optimal load balancing bandwidth to redeploy your model?
- A. 7052 Mbps
- B. 1152 Mbps
- C. 452 Mbps
- D. 52 Mbps
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Calculate optimal bandwidth for model deployment.
* Given Data:
* Payload size = 1024 KB = 1024 * 8 = 8192 Kb (kilobits).
* Requests/sec = 120.
* Buffer = 20% = 0.2.
* Calculate Base Bandwidth:
* Bits/sec = Payload * Requests = 8192 Kb * 120 = 983,040 Kb/s = 983.04 Mbps.
* Add Buffer:
* Total = Base * (1 + Buffer) = 983.04 * 1.2 = 1179.648 Mbps.
* Evaluate Options: Closest to 1179.648 Mbps is 1152 Mbps (D)-realistic rounding.
* Conclusion: D is correct.
OCI documentation advises: "Calculate bandwidth as payload size (in bits) * requests/sec, then add a buffer (e.
g., 20%) for peak loads." Here, 1024 KB = 8192 Kb, * 120 = 983.04 Mbps, * 1.2 = 1179.648 Mbps. D (1152 Mbps) is the closest practical option-452 (A) and 52 (B) are too low, 7052 (C) excessive.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - Load Balancing".
NEW QUESTION # 71
You have configured the Management Agent on an Oracle Cloud Infrastructure (OCI) Linux instance for log ingestion purposes. Which is a required configuration for OCI Logging Analytics service to collect data from multiple logs of this instance?
- A. Entity - Log Association
- B. Log Group - Source Association
- C. Source - Entity Association
- D. Log - Log Group Association
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the required configuration for OCI Logging Analytics to collect logs from an instance.
* Understand Logging Analytics: Collects and analyzes logs from OCI resources via Management Agents.
* Key Concepts:
* Entity: Represents the instance (e.g., Linux VM).
* Source: Defines log locations (e.g., file paths).
* Log Group: Organizes logs for analysis.
* Evaluate Options:
* A: Log-Log Group-Groups logs, not collection setup.
* B: Entity-Log-Links instance to logs, but not source-specific.
* C: Source-Entity-Maps log sources to the instance-correct.
* D: Log Group-Source-Post-collection organization, not ingestion.
* Reasoning: C establishes the link between the instance and its log sources-key for ingestion.
* Conclusion: C is correct.
OCI documentation states: "To collect logs using Logging Analytics, configure a Source-Entity Association (C) to link the Management Agent on the instance (entity) to specific log sources (e.g., file paths)." A and D organize logs post-collection, B is less specific-only C is required for ingestion per OCI's Logging Analytics setup.
Oracle Cloud Infrastructure Logging Analytics Documentation, "Configuring Log Collection".
NEW QUESTION # 72
Which of these options allow the sharing and loading back of ML models into a notebook session?
- A. Model catalog
- B. Model provenance
- C. Model deployment
- D. Model taxonomy
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the mechanism for sharing and reloading ML models in OCI Data Science.
* Evaluate Options:
* A. Model provenance: Tracks model origin-informative but not a sharing mechanism.
* B. Model taxonomy: Categorizes models (e.g., regression)-not for sharing/loading.
* C. Model deployment: Makes models accessible as endpoints, not for notebook reloading.
* D. Model catalog: Stores models and artifacts, enabling sharing and loading into sessions.
* Reasoning: The Model Catalog is OCI's centralized repository for saving, sharing, and retrieving models (e.g., via ADS SDK).
* Conclusion: D is the correct tool.
The OCI Model Catalog "enables data scientists to save trained models and their artifacts, share them with team members, and load them back into notebook sessions for further use or evaluation." Provenance (A) and taxonomy (B) are metadata, while deployment (C) serves inference, not notebook access. D is explicitly designed for this purpose.
Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Usage".
NEW QUESTION # 73
You want to make your model more frugal to reduce the cost of collecting and processing data. You plan to do this by removing features that are highly correlated. You would like to create a heatmap that displays the correlation so that you can identify candidate features to remove. Which Accelerated Data Science (ADS) SDK method is appropriate to display the comparability between Continuous and Categorical features?
- A. correlation_ratio_plot()
- B. cramersv_plot()
- C. pearson_plot()
- D. corr()
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Visualize correlation between continuous and categorical features.
* Evaluate Options:
* A: Pearson-Continuous vs. continuous-incorrect.
* B: Cramer's V-Categorical vs. categorical-incorrect.
* C: Correlation ratio-Continuous vs. categorical-correct.
* D: General correlation-Not specific to mixed types.
* Reasoning: Correlation ratio handles mixed feature types for heatmaps.
* Conclusion: C is correct.
OCI documentation states: "correlation_ratio_plot() (C) in ADS SDK visualizes correlations between continuous and categorical features, ideal for mixed-type heatmaps." Pearson (A) and Cramer's (B) are type- specific, corr() (D) is broad-only C fits per ADS capabilities.
Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization".
NEW QUESTION # 74
Where do calls to stdout and stderr from score.py go in the model deployment?
- A. The file that was defined for them on the virtual machine (VM)
- B. The OCI Console
- C. The OCI Cloud Shell, which can be accessed from the console
- D. The predict log in the Oracle Cloud Infrastructure (OCI) Logging service as defined in the deployment
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Locate stdout/stderr output from score.py in deployment.
* Understand Deployment: score.py runs in a model endpoint; logs are managed by OCI.
* Evaluate Options:
* A: False-No VM file is defined by default; logs go to OCI Logging.
* B: False-Console displays UI, not raw logs.
* C: False-Cloud Shell is a separate tool, not a log destination.
* D: True-Predict logs in OCI Logging capture stdout/stderr.
* Reasoning: OCI centralizes logs in its Logging service for deployments.
* Conclusion: D is correct.
OCI documentation states: "During model deployment, stdout and stderr from score.py are automatically sent to the predict log in the OCI Logging service, as configured in the deployment settings." A, B, and C don't align with this managed logging approach-only D is accurate.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - Logging".
NEW QUESTION # 75
On which option do you set Oracle Cloud Infrastructure Budget?
- A. Instances
- B. Tenancy
- C. Compartments
- D. Free-form tags
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine where OCI budgets are set.
* Understand Budgets: Track spending across OCI resources.
* Evaluate Options:
* A: Compartments-Scoped within tenancy, not budget root.
* B: Instances-Specific resources, not budget scope.
* C: Tags-Filter costs, not budget setting.
* D: Tenancy-Top-level scope for budgets-correct.
* Reasoning: Budgets apply at tenancy, optionally filtered (e.g., by compartment).
* Conclusion: D is correct.
OCI documentation states: "Budgets are set at the tenancy level (D), with optional filters like compartments or tags to monitor spending." A, B, and C are sub-elements-only D is the primary scope per OCI's cost management.
Oracle Cloud Infrastructure Cost Management Documentation, "Setting Budgets".
NEW QUESTION # 76
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