Oracle 1Z0-1110-25 Exam Dumps

Detailed Answer in Step-by-Step Solution:

Objective: Identify the initial data science step.

Define Process: Starts with problem definition, then data and modeling.

Evaluate Options:

A: Data collection---Second step after problem definition.

B: Modeling---Later stage.

C: Hypothesis---Sets the goal, first step---correct.

D: Data owners---Collaboration, not the start.

Reasoning: Hypothesis drives the process (e.g., ''Can we predict churn?'').

Conclusion: C is correct.

OCI documentation states: ''The data science process begins with defining an analytical hypothesis to address a business problem, followed by data collection and analysis.'' C precedes A, B, and D---aligning with OCI's structured approach.

: Oracle Cloud Infrastructure Data Science Documentation, 'Data Science Process'.

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'.

Detailed Answer in Step-by-Step Solution:

Objective: Encode categorical features in a Data Science context (likely ADS SDK).

Understand Encoding: Converts categories (e.g., countries) to numerical forms.

Evaluate Options:

A: Not a standard ADS method---incorrect.

B: General transformation, not specific encoding---incorrect.

C: OneHotEncoder---Standard for categorical encoding---correct.

D: Visualization, not encoding---incorrect.

Reasoning: One-hot encoding creates binary columns---ideal for multiple categories.

Conclusion: C is correct.

OCI documentation states: ''In ADS SDK, use OneHotEncoder (C) from sklearn (or similar) to encode categorical features like 'countries' into binary vectors for modeling.'' A isn't real, B is too broad, D is unrelated---only C fits OCI's encoding practice.

: Oracle Cloud Infrastructure Data Science Documentation, 'Feature Encoding with ADS'.

Detailed Answer in Step-by-Step Solution:

Objective: Identify Apache Spark's ML library.

Understand Spark: A big data framework with specialized libraries.

Evaluate Options:

A: MLib (correctly MLlib)---Spark's machine learning library.

B: GraphX---Graph processing, not ML.

C: Structured Streaming---Streaming data, not ML.

D: HadoopML---Not a Spark library (Hadoop-related).

Reasoning: MLlib is Spark's official ML toolkit (e.g., regression, clustering).

Conclusion: A is correct (noting ''MLib'' should be ''MLlib'').

OCI Data Science supports Spark via Data Flow, where ''MLlib (Machine Learning library) provides scalable ML algorithms.'' GraphX (B) and Structured Streaming (C) serve other purposes, and HadoopML (D) isn't real---MLlib (A) is the standard, despite the typo.

: Oracle Cloud Infrastructure Data Flow Documentation, 'Apache Spark MLlib'.

Detailed Answer in Step-by-Step Solution:

Objective: Determine where to specify the conda environment for an OCI model deployment.

Understand Model Deployment: Requires artifacts like score.py and runtime.yaml to define runtime settings.

Evaluate Options:

A . score.py: Contains inference logic (e.g., load_model(), predict())---not for environment specs.

B . runtime.yaml: Defines deployment runtime, including conda environment path---correct.

C . requirements.txt: Lists pip dependencies---not used in OCI for conda environments.

D . model_artifact_validate.py: Not a standard artifact; doesn't exist in OCI deployment.

Reasoning: runtime.yaml specifies the conda env (e.g., slug: pyspark30_p37_cpu_v2)---failure to set this causes deployment errors.

Conclusion: B is correct.

OCI documentation states: ''The runtime.yaml file in a model artifact specifies the runtime environment, including the conda environment path (e.g., ENVIRONMENT_SLUG: pyspark30_p37_cpu_v2), ensuring the deployed model uses the correct dependencies.'' score.py (A) handles inference, requirements.txt (C) is for pip (not conda in OCI), and D isn't valid---only B addresses the conda issue per OCI's deployment process.

: Oracle Cloud Infrastructure Data Science Documentation, 'Model Deployment - runtime.yaml'.