Free Practice Questions for Snowflake MLA-B01 Certification

Use ML preprocessing functions, (e.g., MinMaxScaOneHotEncoder) to transform large data sets within Snowflake

Load and transform data with DataFrames

Analyze use cases to determine when to use SQL vs Snowpark

Select appropriate data storage methods for training and inference datasets (e.g., external/internal stages, tables, data shares) based on performance, cost, and governance requirements

Define and configure Feature Store entities to establish data models for ML features

Create feature views to encapsulate complex transformations and provide consistent interfaces

Implement Feature Store architecture to enable centralized feature management and reuse

Register ML features as centralized metadata objects to enable discovery and reuse across multiple use cases and teams

Configure external feature views to integrate and standardize features from data sources outside Snowflake while maintaining Feature Store consistency

Track versions of features using version control

Use Snowpark dataframe to build out a feature store

Identify how dynamic tables are used in the Feature Store

Manage data sets for training, validation, and inference across ML workflows

Design lookups using Snowflake Feature Store views to generate training sets that prevent temporal data leakage

Verify consistency between offline training sets and low-latency online feature retrieval

Ensure Dev/Prod feature consistency

Snowflake Lineage visualization and querying

Configure streaming data ingestion for near real-time feature generation

Implement Snowflake Data Quality Monitoring to validate data integrity at ingestion

Configure feature validation rules beyond basic data quality checks

Monitor feature freshness, data quality, schema changes, and drift using Snowflake observability tool

Package and version feature transformations using Snowpark, SQL, or dynamic tables independently from model code

Scale batch and near-real-time feature computation using Snowflake warehouses or compute pools

Deploy and promote feature pipelines across Dev/Test/Prod using tasks and dynamic table refresh policies with offline/online parity

Deploy custom model runtime environments using containerized images to support libraries not natively available in Snowpark

Operate ML Jobs

Configure and compare ML distributed APIs across warehouse vs container runtime environments

Configure compute pools for ML workloads and Snowflake Container Services

Optimize resource allocation between warehouses and Container Runtime

Train models within Snowflake Notebooks in Workspaces to leverage distributed data processing capabilities

Analyze use cases to determine when to develop in Workspaces vs stored procedures, SQL, or Python files

Use open-source packages to build and evaluate models

Use DataConnector with open source packages to speed up data ingestion for ML work

Identify the default GPU allocation constraints for individual notebook instances within the Workspaces environment

Structure nested experiments to log performance metrics, hyperparameters, and artifacts using Snowpark ML metadata

Execute hyperparameter optimization (HPO) workflows within Snowflake's ML framework

Implement CustomModel API for integrating external models and custom algorithms into the Snowflake ML framework

Monitor comprehensive experiment tracking for model development lifecycle management

Associate models with specific dataset versions to ensure full lineage and experiment reproducibility

Register model artifacts, custom models, versions, and aliases with proper metadata and dependencies

Archive and deprecate model versions

Execute model rollback procedures when performance degrades

Assign model aliases (e.g., @prod) to decouple client applications from specific underlying model versions

Extract and manage model artifacts including pickle files and other serialized model components

Validate model artifact integrity during registration and deployment processes

Deploy registered models across different environments and accounts

Configure model promotion workflows between dev/test/prod environments

Validate model compatibility across environment configurations

Deploy models to managed HTTPS endpoints in Snowpark Container Services for real-time, low-latency inference

Execute batch inference pipelines using registry-backed execution to process large datasets within dedicated virtual warehouses

Deploy batch inference as service functions using the same service used for online model deployment

Run large batch workloads on Snowpark Container Services using job-based batch inference (without service creation)

Process unstructured data and multimodal models through batch inference capabilities

Track metrics for Real-time Inference via REST API endpoints

Migrate existing models from third-party platforms into Snowflake

Identify Snowflake-native alternatives to external MLOps tools and workflows

Develop automated pipelines that chain data validation, training, and deployment

Integrate ML capabilities with developer tools including Snowflake CLI, SnowSQL, and language-specific SDKs

Configure Git integration in Snowflake to automate ML code deployment

Leverage Snowflake CLI for automation

Deploy ML assets using Snowflake-native promotion workflows

Configure Snowflake object dependencies for ML pipeline deployment

Define automated retraining policies triggered by data drift alerts or performance degradation metrics

Troubleshoot pipeline failures related to warehouse capacity, container scalability, or data dependency issues

Ensure appropriate privileges are granted to roles for accessing Snowflake ML components (e.g., Feature Store, Model Registry, or compute pools)

Apply Snowflake Dynamic Data Masking and row-level Security to ensure sensitive data is protected during training and inference

Establish data governance policies for ML model compliance across feature stores and model registries

Track data drift and statistical anomalies using Snowflake's integrated monitoring and alerting

Audit all model interactions and inference calls to maintain lineage for regulated industries

Configure alerts for ML data governance violations

Configure automated model performance degradation alerts

Implement model accuracy and drift threshold monitoring

Monitor model serving performance metrics (latency, throughput, availability) to enhance operational monitoring coverage

Tune warehouse optimization strategies and compute pool sizing to balance ML performance with cost efficiency

Track costs for ML features with granular attribution

Track costs of Snowpark Container Services compute pool and optimize it if necessary