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CCNA AI Operations Questions

75 of 89 questions · Page 1/2 · AI Operations · Answers revealed

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1
MCQmedium

You are managing an Azure Machine Learning workspace and need to ensure that model training jobs are reproducible across environments. Which feature should be configured to capture the exact environment state, including Python packages and system dependencies?

A.Azure ML Datasets
B.Model Registries
C.Compute Targets
D.Environment objects
AnswerD

Environment objects define the exact dependencies, ensuring the same environment is used across local, compute instance, and cluster training.

Why this answer

Conda or Docker environments are the standard for reproducibility in Azure ML. Environment objects define the exact dependencies required for a job.

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2
MCQhard

In SageMaker, what is the role of an 'Endpoint Configuration'?

A.Defines the database password
B.Defines the data cleaning logic
C.Defines model variants and compute resources
D.Defines the training budget
AnswerC

Controls how the model is deployed.

Why this answer

Defines the model, instance type, and traffic distribution (variants) for the production endpoint.

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3
Multi-Selecthard

Which THREE components are critical to monitor in an AI pipeline to ensure operational availability?

Select 3 answers
A.Data source connectivity
B.Pipeline task execution status
C.Company stock price
D.Resource utilization (CPU/GPU)
E.Developer social media activity
AnswersA, B, D

Upstream dependency.

Why this answer

Availability depends on infrastructure health and task success.

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4
Multi-Selecthard

When designing an AI governance framework, which THREE roles should have oversight responsibility?

Select 3 answers
A.Legal Counsel
B.Marketing Intern
C.Janitorial Staff
D.ML Engineer
E.Data Privacy Officer
AnswersA, D, E

Ensures regulatory compliance.

Why this answer

Governance involves cross-functional stakeholders.

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5
MCQmedium

Which metric would best detect 'Label Drift' in a production model?

A.Target distribution shift (PSI)
B.Model latency
C.CPU utilization
D.Number of users
AnswerA

Population Stability Index is standard for label shift.

Why this answer

Label drift occurs when the output (target) distribution changes significantly from training.

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6
Multi-Selecthard

Which THREE documents are typically required for an AI Audit trail?

Select 3 answers
A.Model training logs
B.Model validation reports
C.Deployment approval records
D.Employee vacation calendar
E.Cafeteria lunch receipts
AnswersA, B, C

Shows the training process.

Why this answer

Audit trail requires evidence of development, testing, and deployment.

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7
MCQmedium

What is the purpose of 'Containerization' (e.g., Docker) in MLOps?

A.Encrypting the model
B.Ensuring environment consistency
C.Reducing data size
D.Increasing model accuracy
AnswerB

Solves 'works on my machine' issues.

Why this answer

Ensures the environment (dependencies, OS) is consistent across development, testing, and production.

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8
MCQmedium

In an AI Audit, what does 'Model Lineage' demonstrate?

A.The cloud provider's stock
B.Provenance of the final model
C.The developer's name
D.The model's name
AnswerB

Ensures transparency.

Why this answer

The path from raw data to the final model, including all code and transformation steps.

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9
MCQeasy

Which of the following is an example of an 'operational metric' for an AI system?

A.Request latency
B.F1 Score
C.ROC AUC
D.Training accuracy
AnswerA

Measures performance/uptime.

Why this answer

Operational metrics (system health) include latency, error rate, and throughput.

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10
MCQmedium

What is 'Shadow Deployment' in AI MLOps?

A.Running a model in parallel without serving results
B.Running only on local machine
C.Deleting the model
D.Replacing the production model
AnswerA

Safe validation method.

Why this answer

Running the new model alongside the production model, but without the new model's predictions influencing the final result, to validate performance.

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11
MCQhard

During an audit of an LLM pipeline, you discover that 'System Prompt' versioning is missing. What is the operational risk?

A.Higher cloud bills
B.Unpredictable model behavior changes
C.Increased latency
D.Database corruption
AnswerB

Prompts are part of the model logic.

Why this answer

Without system prompt versioning, changes to the instructions can cause unpredictable behavior changes that cannot be reverted.

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12
MCQmedium

When auditing an AI model, what does 'Concept Drift' signify to an operational team?

A.The training data was leaked
B.The model is overfitted
C.The model's predictive accuracy is likely degrading
D.The model is running too fast
AnswerC

Requires model retraining.

Why this answer

Concept drift occurs when the statistical relationship between input and target variable changes, rendering the model obsolete.

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13
MCQmedium

When auditing an ML pipeline, what does the 'DAG' (Directed Acyclic Graph) represent?

A.Model accuracy curve
B.The database schema
C.Pipeline task dependencies
D.The network architecture
AnswerC

Defines the execution order.

Why this answer

The DAG defines the sequence and dependency of operational steps in the pipeline.

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14
MCQhard

What is the risk of 'Feature Skew' in an AI pipeline?

A.Database corruption
B.Increased hardware costs
C.Incorrect model predictions
D.System crash
AnswerC

Direct consequence of skew.

Why this answer

If features used in training differ from those used in serving, the model produces incorrect results.

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15
MCQhard

Which operational process is required to ensure 'Data Privacy' when using user-generated data for model retraining?

A.Increasing compute power
B.Model quantization
C.Removing the model
D.Data anonymization
AnswerD

Ensures GDPR/CCPA compliance.

Why this answer

Anonymization or pseudonymization must be performed before training.

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16
MCQhard

For a production LLM deployment, which operational metric is most critical to monitor to identify 'hallucination' or grounding failure in real-time?

A.Throughput (TPS)
B.Context faithfulness score
C.Token generation latency
D.Error code distribution
E.Memory footprint
AnswerB

Measures how much the answer is supported by the context.

Why this answer

Retrieval metrics, specifically faithfulness or answer relevance, indicate if the model is hallucinating outside of retrieved context.

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17
MCQeasy

What is the primary purpose of a 'Champion-Challenger' deployment pattern in MLOps?

A.To manage database indexes
B.To compress model weights
C.To safely test a new model vs production
D.To increase training speed
AnswerC

Ensures risk mitigation before full cutover.

Why this answer

It allows safe A/B testing of a new model version against the current production model.

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18
MCQhard

An organization using AWS SageMaker is experiencing model drift in production. You need to implement automated monitoring to trigger alerts when the distribution of input data deviates from the baseline. Which tool should be configured?

A.AWS CloudWatch Alarms
B.SageMaker Clarify
C.SageMaker Pipelines
D.SageMaker Model Monitor
AnswerD

Model Monitor automates the detection of drift by comparing production data against a baseline dataset.

Why this answer

SageMaker Model Monitor uses baseline statistics to detect data quality issues in production endpoints.

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19
MCQhard

When auditing a Kubernetes-based AI deployment, what does a 'Pod Disruption Budget' (PDB) ensure?

A.The model version
B.Maximum allowed CPU usage
C.Minimum availability during maintenance
D.The cost of the cluster
AnswerC

Ensures high availability.

Why this answer

Ensures that a minimum number of replicas are available during voluntary disruptions (e.g., node upgrades).

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20
MCQhard

An auditor finds that a model's 'input feature importance' has changed significantly after a retrain. What is the most appropriate action?

A.Investigate source data and feature logic
B.Ignore the change
C.Delete the model
D.Upgrade the hardware
AnswerA

Identify root cause of the shift.

Why this answer

Investigate if the change is due to a shift in data or a feature engineering error.

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21
Multi-Selecthard

Which THREE items should be tracked in a 'Model Registry' for audit readiness?

Select 3 answers
A.Training dataset lineage
B.Source code reference (Git commit)
C.Cloud provider logo
D.Employee birthday
E.Model artifact hash
AnswersA, B, E

Provenace tracking.

Why this answer

Registration must link code, data, and model artifacts.

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22
Multi-Selectmedium

Which TWO are key 'AI Operational' documentation artifacts?

Select 2 answers
A.Personal employee diary
B.Restaurant menu
C.Pipeline Architecture Diagram
D.Model Card
E.Grocery shopping list
AnswersC, D

System documentation.

Why this answer

Governance requires documentation of models and pipelines.

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23
MCQhard

Which security control is most effective against 'Prompt Injection' attacks in a deployed LLM?

A.Enabling GPU acceleration
B.Prompt Guardrails
C.Increasing model parameters
D.Regular re-training
AnswerB

Filters malicious prompt patterns.

Why this answer

System-level prompt wrappers (Guardrails) inspect and sanitize user input before it reaches the model.

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24
MCQmedium

When auditing Kubeflow pipelines, which configuration file is used to define the resource requests for individual pipeline steps?

A.resource_spec in the SDK
B.Namespace policy
C.Kubeconfig file
D.Service mesh config
AnswerA

Defines compute requirements for k8s pods.

Why this answer

The 'pipeline.yaml' or the step-specific resource configuration in the SDK defines CPU/Memory limits.

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25
MCQmedium

When auditing an AI pipeline, where should 'Environment Variables' be stored to ensure security?

A.Git repository
B.Hardcoded in script
C.Publicly accessible folder
D.Secrets Manager
AnswerD

Secure storage.

Why this answer

Secrets/variables should be stored in a Secrets Manager, not in the code or environment files.

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26
MCQhard

During an audit of a model inference endpoint, you observe that 'Model Explanation' logs are missing. Which library or tool was likely omitted from the deployment?

A.Kubernetes manifest
B.TensorBoard logging
C.SHAP integration
D.Prometheus exporter
AnswerC

Required for feature importance explanation.

Why this answer

Tools like Captum (PyTorch), SHAP, or SageMaker Clarify are required to generate inference-time explanations.

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27
MCQeasy

Which environment should be used to perform 'Model Validation' before moving to production?

A.Training Cluster
B.Development Environment
C.Public Internet
D.Staging Environment
E.Local Desktop
AnswerD

Designed for final validation.

Why this answer

Staging or UAT environments mirror production and are used for testing.

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28
MCQeasy

When setting up a containerized deployment of an AI model using Docker, what is the primary purpose of a 'multi-stage build'?

A.To run multiple versions of the model simultaneously
B.To train the model during the container startup
C.To automatically scale the deployment based on traffic
D.To reduce the final image size and attack surface
AnswerD

By excluding build tools from the final image, the image is smaller and more secure.

Why this answer

Multi-stage builds allow for the creation of smaller, more secure images by separating build-time dependencies from the runtime environment.

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29
Multi-Selecthard

Which THREE items must be included in a 'Model Card' to comply with regulatory model risk management standards?

Select 3 answers
A.Intended use cases
B.Model performance metrics
C.Internal employee passwords
D.Cloud service provider revenue
E.Known limitations and failure modes
AnswersA, B, E

Defines the scope of the model.

Why this answer

Model cards summarize intended use, limitations, and performance characteristics.

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30
Multi-Selectmedium

Which TWO are common 'AI Security' threats that require operational oversight?

Select 2 answers
A.Slow training time
B.High memory usage
C.Model poisoning during training
D.Adversarial evasion attacks
E.Team burnout
AnswersC, D

Compromises training data.

Why this answer

Adversarial attacks and model poisoning are primary security concerns.

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31
MCQmedium

What is the function of a 'Circuit Breaker' in a microservice-based AI architecture?

A.Preventing failure propagation
B.Increasing latency
C.Training the model
D.Managing databases
AnswerA

Standard resilience pattern.

Why this answer

To stop requests to a failing AI service to prevent system-wide collapse.

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32
MCQhard

An organization experiences 'Data Leakage' in a machine learning project. What is the most likely cause during the training process?

A.Using target data in training features
B.Using standard Python libraries
C.Using secure cloud storage
D.Using too many GPUs
AnswerA

Leads to artificial performance inflation.

Why this answer

Using test/validation data during feature selection or training leads to leakage.

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33
MCQeasy

What is the primary function of a 'Version Control System' (like Git) in an MLOps team?

A.Executing Python scripts
B.Managing code changes
C.Storing raw video
D.Managing databases
AnswerB

Foundation of DevOps/MLOps.

Why this answer

To manage code changes, enable collaboration, and allow rollbacks.

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34
MCQhard

When using DVC (Data Version Control) in an AI project, what is the best practice to ensure the 'data version' is immutable for audit purposes?

A.Use absolute file paths
B.Commit .dvc files to git
C.Disable data caching
D.Store data on local drive
AnswerB

Ensures the data state is tracked alongside code.

Why this answer

Committing the '.dvc' file (which contains a hash of the data) to Git ensures that the specific data version is locked to the code version.

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35
MCQmedium

What is the primary objective of a 'Model Inventory' in an enterprise AI audit?

A.To log user feedback
B.Centralized visibility of all models
C.To optimize GPU usage
D.To store model weights
AnswerB

Ensures no 'shadow AI' exists.

Why this answer

To have a centralized, authoritative list of all models in production for compliance and risk tracking.

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36
MCQmedium

Which AWS SageMaker feature allows auditors to verify that a model was trained on encrypted data?

A.Instance type selection
B.Model registry labels
C.Notebook instance tags
D.KMS Key Usage in CloudTrail
AnswerD

Audit trail shows key usage for training data.

Why this answer

SageMaker Training Jobs support 'Data Encryption' via KMS keys, which is logged in CloudTrail.

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37
MCQmedium

When an AI system utilizes a 'Feature Store', how does it ensure consistency between training and inference?

A.Deleting training data after use
B.Caching features on local disk
C.Converting features to text
D.Serving features from a unified source
AnswerD

Prevents training-serving skew.

Why this answer

The feature store ensures the exact same logic and code are used to fetch features for both training and serving.

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38
MCQeasy

In a GCP Vertex AI pipeline, you want to ensure that every model artifact is associated with the specific training data and code version used. Which component provides this lineage tracking?

A.Feature Store
B.Vertex AI Metadata
C.Vertex AI Pipelines
D.Vertex AI Experiments
AnswerB

Metadata provides the tracking for artifacts and their associations in the pipeline graph.

Why this answer

Vertex AI Metadata tracks the lineage of artifacts throughout the lifecycle.

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39
Multi-Selecthard

When auditing a model deployment pipeline, which TWO aspects are critical to verify to ensure compliance with AI governance frameworks?

Select 2 answers
A.Whether the team uses a specific cloud provider
B.Validation of data processing against privacy policies
C.Existence of a complete audit trail of model lineage
D.The speed of the training job
E.Usage of proprietary algorithms only
AnswersB, C

Verifying data handling is essential for legal and ethical compliance.

Why this answer

Governance requires both reproducibility and data privacy/security checks.

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40
MCQhard

When deploying an AI model on a public cloud, what is the best practice for 'Secret Management' of API keys?

A.Using a dedicated Secret Vault service
B.Printing to logs
C.Hardcoding in the script
D.Storing in a Git repo
AnswerA

Secures credentials from code/logs.

Why this answer

Secrets must be stored in a dedicated vault (e.g., AWS Secrets Manager, Azure Key Vault) and injected at runtime.

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41
MCQmedium

What is the most effective way to audit 'Data Lineage' in a complex AI ecosystem?

A.Automated Metadata Tracking
B.Asking developers
C.Manual spreadsheet tracking
D.Checking project folder names
AnswerA

Provides a trail of data provenance.

Why this answer

Using metadata tracking tools that record the transformations from source to final model input.

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42
MCQhard

You are performing an audit on an ML project utilizing Kubeflow. The team is using Katib for hyperparameter tuning. Which configuration step is critical to ensure that individual trials do not starve the production inference service of resources?

A.Increasing node count on the master node
B.Disabling auto-scaling on the cluster
C.Setting the Katib algorithm to random search
D.Implementing Kubernetes ResourceQuotas
AnswerD

ResourceQuotas enforce usage limits on namespaces, preventing trial pods from exhausting cluster resources.

Why this answer

Resource quotas in Kubernetes prevent training trials from consuming resources allocated to production services.

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43
MCQmedium

In an AI Audit, what does 'Model Fairness' assessment typically involve?

A.Counting model parameters
B.Checking model speed
C.Disparity analysis across segments
D.Verifying code documentation
AnswerC

Identifies if the model treats groups differently.

Why this answer

Checking for bias across protected groups (e.g., race, gender) in model performance metrics.

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44
MCQmedium

What does a 'Model Approval' workflow ensure?

A.Human oversight of deployment
B.Faster training
C.Reduced cloud costs
D.Automatic deletion
AnswerA

Governance standard.

Why this answer

That a human reviewer has vetted the model against compliance and quality standards before production deployment.

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45
MCQhard

When auditing an AI system, how do you verify 'Model Reproducibility'?

A.Asking the team
B.Verifying pipeline run with identical inputs
C.Checking cloud bills
D.Reading the documentation
AnswerB

The definition of reproducibility.

Why this answer

Verify that you can re-run the training pipeline with the same code version and data snapshot to get the same model weights.

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46
MCQmedium

In a CI/CD pipeline for AI, where should the 'Model Validation' gate be located?

A.During feature engineering
B.Between training and registration
C.Before source code check-in
D.After deployment
AnswerB

Prevents promotion of unqualified models.

Why this answer

Validation must occur after training but before model registration to prevent bad models from reaching production.

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47
MCQeasy

What is the primary function of an 'AI Pipeline Orchestrator' like Kubeflow or Airflow?

A.Automating task dependencies
B.Encrypting network traffic
C.Writing code for the model
D.Optimizing SQL queries
AnswerA

Defines and runs DAGs of tasks.

Why this answer

Orchestration automates the sequence of tasks: data ingestion, training, and deployment.

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48
Multi-Selectmedium

Which THREE of the following are essential components of an MLOps maturity model for ensuring operational stability?

Select 3 answers
A.Storing raw data directly in the model repository
B.Centralized model registry and artifact management
C.Automated retraining pipelines
D.Manual model validation processes
E.Real-time monitoring and alerting for model performance
AnswersB, C, E

Centralized governance is essential for reproducibility.

Why this answer

Maturity models focus on automation, monitoring, and governance.

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49
MCQmedium

In the context of 'AI Pipelines', what is the role of a 'Model Registry'?

A.Storing training logs
B.Version control for model artifacts
C.Monitoring compute costs
D.Optimizing SQL queries
AnswerB

Provides centralized management.

Why this answer

The Model Registry provides a central repository to store, version, and manage the lifecycle of trained models.

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50
MCQmedium

In an AI Audit, what does an 'Input Feature Schema' define?

A.The training duration
B.Expected data format and constraints
C.The database name
D.The model architecture
AnswerB

Crucial for interface contracts.

Why this answer

It defines the expected data types, shapes, and constraints for input features, ensuring integration stability.

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51
Multi-Selectmedium

Which TWO of the following are considered 'Model Metadata' that should be tracked in a registry?

Select 2 answers
A.Hyperparameters
B.Training dataset version
C.Employee vacation schedule
D.Cafeteria menu
E.Office Wi-Fi password
AnswersA, B

Essential for reproduction.

Why this answer

Registry must store enough info to recreate the model state.

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52
MCQeasy

Which of these is a valid reason to 'Roll Back' a model deployment?

A.To save storage space
B.To change the model name
C.Detected production performance issues
D.Developer preference
AnswerC

Standard incident response.

Why this answer

If the model in production starts showing unexpected errors or performance drops, rolling back is the immediate safety action.

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53
MCQmedium

You are configuring a CI/CD pipeline for ML using GitHub Actions to deploy to a Kubernetes cluster. To ensure sensitive credentials for your container registry are not exposed in logs, what is the best practice?

A.Store credentials in GitHub Secrets
B.Use environment variables in the workflow file
C.Hardcode credentials in the Dockerfile
D.Commit a .env file to the repository
AnswerA

GitHub Secrets masks values in logs and encrypts them at rest.

Why this answer

Using GitHub Secrets is the standard way to inject sensitive data into CI/CD pipelines securely.

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54
Multi-Selecteasy

Which TWO of the following are primary benefits of implementing a Model Registry in an AI production environment?

Select 2 answers
A.Allows for direct modification of training data
B.Enables versioning and lifecycle management of models
C.Increases the hardware utilization of GPUs
D.Facilitates standardized deployment across different environments
E.Automatically deletes old training instances
AnswersB, D

This is the core function of a model registry.

Why this answer

Model registries ensure version control and facilitate collaboration.

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55
MCQmedium

Which of the following is a key component of 'MLOps Maturity'?

A.Manual data entry
B.Single engineer silo
C.No documentation
D.Automated pipeline triggers
AnswerD

Essential for mature operations.

Why this answer

High maturity involves full CI/CD, automated testing, and active model monitoring.

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56
Multi-Selecthard

Which THREE factors influence 'AI Pipeline' latency?

Select 3 answers
A.Company CEO name
B.Model parameter size
C.Network transfer speed
D.Data preprocessing complexity
E.Office building height
AnswersB, C, D

Memory/CPU bound task.

Why this answer

Latency is affected by data processing, model size, and compute overhead.

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57
MCQeasy

What is the purpose of 'Performance Monitoring' in AI?

A.Encrypting data
B.Changing model weights
C.Renaming models
D.Ensuring SLA compliance
AnswerD

Primary monitoring goal.

Why this answer

To detect if the system is meeting its latency, throughput, and accuracy targets.

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58
MCQmedium

What is the primary function of a 'Canary Deployment' in AI model release?

A.Gradual rollout to test stability
B.To update documentation
C.To delete old models
D.To increase training performance
E.To encrypt model weights
AnswerA

Mitigates impact of failures.

Why this answer

Releasing the new model to a small subset of traffic to monitor performance before full rollout.

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59
MCQmedium

A company uses MLflow for model registry and tracking. You need to transition a model from the 'Staging' environment to 'Production' only after it passes an automated integration test. How is this state transition managed in MLflow?

A.Using the 'transition_model_version_stage' API
B.Updating the 'version' tag in the git repo
C.Deleting the model in Staging and re-uploading to Production
D.Manually copying files in the S3 bucket
AnswerA

This API call allows for programmatic state management of registry versions.

Why this answer

Transitioning model versions in the MLflow Model Registry is done via the 'transition_model_version_stage' API or UI.

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60
MCQhard

What is the consequence of 'Pipeline Staleness' in an AI system?

A.Model degradation due to outdated data
B.Unauthorized access
C.Increased hardware costs
D.System crash
AnswerA

Outdated data is the primary cause of poor results.

Why this answer

If the pipeline is not refreshed, it trains on outdated data, leading to model degradation.

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61
MCQmedium

What is the role of an 'A/B Test' in AI production?

A.Encrypting data
B.Reducing training time
C.Comparing two models in production
D.Deleting unused models
AnswerC

Statistical model comparison.

Why this answer

A/B testing allows comparing the performance of a new model against an existing one with real user traffic.

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62
MCQmedium

In the context of AI regulatory compliance, what is the 'Model Risk Management' (MRM) framework mainly concerned with?

A.Reducing cloud expenses
B.Increasing developer productivity
C.Marketing model features
D.Governance and validation controls
AnswerD

Primary focus of MRM.

Why this answer

MRM manages the risks associated with model usage, including governance, validation, and control.

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63
MCQeasy

In the context of AI Operations, what does 'CI/CD' stand for?

A.Computer Intelligence and Cloud Data
B.Continuous Integration and Continuous Deployment
C.Continuous Inference and Cloud Deployment
D.Code Integration and Cloud Delivery
AnswerB

Standard MLOps terminology.

Why this answer

Continuous Integration and Continuous Deployment/Delivery.

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64
Multi-Selecthard

Which THREE tasks are required for a 'Model Decommissioning' workflow?

Select 3 answers
A.Terminate serving infrastructure
B.Notify stakeholders
C.Ask developer for coffee
D.Delete all company records
E.Archive model artifacts
AnswersA, B, E

Stop resource billing.

Why this answer

Decommissioning requires archival, notification, and resource cleanup.

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65
MCQeasy

What is 'Model Versioning' used for in an AI project?

A.Reducing model size
B.Improving training time
C.Increasing security
D.Ensuring reproducibility and rollback
AnswerD

Core MLOps capability.

Why this answer

It allows tracking changes, rolling back to previous versions, and ensuring reproducibility.

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66
Multi-Selectmedium

Which TWO are standard practices for securing AI model endpoints?

Select 2 answers
A.Mutual TLS (mTLS)
B.Unprotected endpoints
C.Storing logs on public S3
D.Public internet access
E.API Rate Limiting
AnswersA, E

Authenticates both client and server.

Why this answer

Security requires network and authentication controls.

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67
Multi-Selectmedium

Which TWO are common 'AI Operational' challenges?

Select 2 answers
A.Selecting a font
B.Data quality management
C.Writing poetry
D.Buying new office chairs
E.Model maintenance
AnswersB, E

Ensures model health.

Why this answer

Managing data quality and model maintenance are core challenges.

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68
MCQeasy

What is the main benefit of 'Automated Retraining'?

A.Eliminates the need for humans
B.Reduces electricity costs
C.Keeps models updated with new data
D.Speeds up inference
AnswerC

Reduces drift impact.

Why this answer

It ensures the model adapts to new data patterns without manual intervention.

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69
MCQhard

Why must you conduct a 'Bias Audit' on a model that has been fine-tuned on new data?

A.It speeds up inference
B.It fixes hardware bugs
C.Fine-tuning can introduce new biases
D.It reduces model size
AnswerC

Data distribution shifts can correlate with sensitive attributes.

Why this answer

Fine-tuning on new data can inadvertently introduce new biases or exacerbate existing ones.

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70
MCQhard

In Google Vertex AI Pipelines, which mechanism prevents 'training-serving skew' when deploying a custom container model?

A.Custom prediction routines
B.Artifact metadata locks
C.Model Monitoring skew detection
D.Pipeline concurrency limits
AnswerC

Automates comparison of feature distributions.

Why this answer

Vertex AI Model Monitoring provides built-in skew detection by comparing training data statistics against serving traffic statistics.

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71
MCQhard

What is the most effective way to address 'Adversarial Input' during inference?

A.Reducing training time
B.Adversarial training and input filtering
C.Increasing model depth
D.Ignoring the input
AnswerB

Hardens the model against inputs.

Why this answer

Implementing input validation/sanitization and adversarial training (training on adversarial examples) improves robustness.

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72
MCQmedium

In SageMaker, what does 'Model Monitor' specifically detect?

A.Database uptime
B.Model size
C.Employee attendance
D.Data and model drift
AnswerD

Core feature of SageMaker Model Monitor.

Why this answer

It monitors for data drift and model quality degradation in production.

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73
Multi-Selecthard

Which THREE criteria are essential when selecting an AI deployment strategy?

Select 3 answers
A.Color of the UI
B.Number of coffee machines
C.Service Level Agreements (SLAs)
D.Risk tolerance
E.Compute resource availability
AnswersC, D, E

Defines uptime requirements.

Why this answer

Strategy must balance risk, speed, and resource availability.

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74
MCQmedium

What does a 'Backtesting' report verify in an AI Audit?

A.Future accuracy
B.Cloud service costs
C.Developer salary
D.Historical model performance
AnswerD

Validates reliability.

Why this answer

Backtesting uses historical data to verify how the model *would* have performed, ensuring reliability.

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75
MCQmedium

What is the role of a 'Deployment Pipeline' in MLOps?

A.Automating deployment steps
B.Storing logs
C.Writing code
D.Labeling data
AnswerA

Reduces human error.

Why this answer

To automate the packaging, testing, and deployment of a model.

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