NVIDIA NCA-AIIO: GPU Architecture for AI Infrastructure
AI infrastructure design starts with understanding why GPUs behave differently from general-purpose CPUs. Modern training and inference workloads contain large amounts of parallel…
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AI & Data.
Artificial intelligence, machine learning, data systems and analytics.
PUBLISHED COVERAGECurated by subject
NVIDIA NCA-AIIO: Observability for GPU Infrastructure
GPU infrastructure needs observability at several layers: device health, utilization, memory behavior, power and thermals, PCIe and NVLink traffic, node resources, scheduler state,…
Read articleNVIDIA NCA-AIIO: Power, Cooling, and Capacity for AI Systems
AI infrastructure pushes data-center power density and cooling design far beyond conventional server planning. Modern GPU systems can consume tens of kilowatts per…
Read articleSplunk SPLK-5001: CIM and Data Models for Security Analytics
The Splunk Common Information Model, or CIM, provides a vendor-neutral field and tag vocabulary for common data domains such as authentication, network traffic,…
Read articleSplunk SPLK-1003: Data Ingestion and Sources
Splunk becomes useful only after data reaches the platform with enough structure and context to support search, alerting, dashboards, and security analytics. Ingestion…
Read articleSplunk SPLK-1003: Indexes & Data Retention
Splunk indexes are the storage and search boundary for ingested events. An index contains buckets that move through lifecycle states as data ages,…
Read articleServiceNow CIS-DF: CMDB Health & Data Quality
A ServiceNow CMDB becomes valuable only when the configuration items and relationships inside it are trustworthy enough to support incident, change, asset, service,…
Read articleSalesforce Data Cloud Consultant: Data Cloud Identity Resolution
Identity resolution in Salesforce Data 360 links records from multiple sources into unified profiles for individuals, accounts, leads, or households. It uses rulesets…
Read articleSalesforce Data Cloud Consultant: Data Cloud Modeling & Harmonization
Data 360 modeling and harmonization convert source-specific records into a standardized customer data model that can support identity resolution, segmentation, calculated insights, activation,…
Read articleSalesforce Data Cloud Consultant: Data Cloud Activation & Segmentation
Salesforce Data Cloud was rebranded to Data 360 in October 2025, and current Salesforce documentation and certification naming use Data 360. The site’s…
Read articleSalesforce Agentforce Specialist: Prompt Templates & Agent Actions
Prompt templates and agent actions solve different parts of an Agentforce workflow. A prompt template structures generative AI input and can be grounded…
Read articleSalesforce Agentforce Specialist: Grounding Salesforce Agents with Trusted Data
Grounding gives an AI agent current, organization-specific context instead of relying only on a model’s general knowledge. In Salesforce, grounding can draw from…
Read articleSnowflake SnowPro Advanced Data Engineer: Snowpark for Data Engineering
Snowpark brings DataFrame-style programming into Snowflake so engineers can build transformations in languages such as Python while keeping execution close to governed data.…
Read articleSnowflake SnowPro Advanced Data Engineer: Dynamic Tables
Snowflake Dynamic Tables let data engineers define the result they want with a SELECT statement and let Snowflake manage refresh timing, dependency order,…
Read articleSnowflake SnowPro Advanced Data Engineer: ELT Pipelines in Snowflake
ELT pipelines load data into Snowflake before applying most transformation logic inside the platform. This approach takes advantage of scalable Snowflake compute, SQL,…
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