Fix link to host preflight YAML for Embedded Cluster

Updated link to host preflight spec for Embedded Cluster.

Author: adamancini

static/vendor/embedded-using.md

@@ -98,7 +98,7 @@ During installation, Embedded Cluster automatically runs a default set of _host
 * The system has at least 2G of memory and 2 CPU cores
 * The system clock is synchronized
 
-For Embedded Cluster requirements, see [Embedded Cluster Installation Requirements](/enterprise/installing-embedded-requirements). For the full default host preflight spec for Embedded Cluster, see [`host-preflight.yaml`](https://github.com/replicatedhq/embedded-cluster/blob/main/pkg/preflights/host-preflight.yaml) in the `embedded-cluster` repository in GitHub.
+For Embedded Cluster requirements, see [Embedded Cluster Installation Requirements](/enterprise/installing-embedded-requirements). For the full default host preflight spec for Embedded Cluster, see [`host-preflight.yaml`](https://github.com/replicatedhq/embedded-cluster/blob/main/pkg-new/preflights/host-preflight.yaml) in the `embedded-cluster` repository in GitHub.
 
 If any of the host preflight checks fail, installation is blocked and a message describing the failure is displayed. For more information about host preflight checks for installations on VMs or bare metal servers, see [About Host Preflights](preflight-support-bundle-about#host-preflights).
 
@@ -290,4 +290,4 @@ When the containerd options are configured as shown above, the NVIDIA GPU Operat
 If you include the NVIDIA GPU Operator as a Helm extension, remove any existing containerd services that are running on the host (such as those deployed by Docker) before attempting to install the release with Embedded Cluster. If there are any containerd services on the host, the NVIDIA GPU Operator will generate an invalid containerd config, causing the installation to fail. For more information, see [Installation failure when NVIDIA GPU Operator is included as Helm extension](#nvidia) in _Troubleshooting Embedded Cluster_.
 
 This is the result of a known issue with v24.9.x of the NVIDIA GPU Operator. For more information about the known issue, see [container-toolkit does not modify the containerd config correctly when there are multiple instances of the containerd binary](https://github.com/NVIDIA/nvidia-container-toolkit/issues/982) in the nvidia-container-toolkit repository in GitHub.
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