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  1. Asked: December 11, 2025In: Cloud & DevOps

    why does Terraform ignore changes I make in the console?

    Marnus
    Marnus Begginer
    Added an answer on January 5, 2026 at 2:38 pm

    Terraform only notices changes when you run a plan or refresh. If ignore_changes is configured, Terraform will intentionally skip certain attributes. Otherwise, console changes will appear as drift the next time Terraform evaluates state. Manual changes and Terraform don’t mix well long-term. TakeawRead more

    Terraform only notices changes when you run a plan or refresh.

    If ignore_changes is configured, Terraform will intentionally skip certain attributes. Otherwise, console changes will appear as drift the next time Terraform evaluates state.

    Manual changes and Terraform don’t mix well long-term.

    Takeaway: Terraform works best as the single source of truth.

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  2. Asked: April 4, 2025In: Cloud & DevOps

    Why does my application lose permissions after a Kubernetes pod restart?

    Marnus
    Marnus Begginer
    Added an answer on January 5, 2026 at 2:37 pm

    Pods are ephemeral, and anything stored locally disappears on restart. If credentials are written to the filesystem instead of injected dynamically, they won’t survive restarts. Secrets, identity bindings, or token projection are the correct approach. Takeaway: Never rely on local storage for credenRead more

    Pods are ephemeral, and anything stored locally disappears on restart.

    If credentials are written to the filesystem instead of injected dynamically, they won’t survive restarts. Secrets, identity bindings, or token projection are the correct approach.

    Takeaway: Never rely on local storage for credentials in containers.

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  3. Asked: November 4, 2025In: Cloud & DevOps

    Why does my Docker container run as root even though I specified a user?

    Marnus
    Marnus Begginer
    Added an answer on January 5, 2026 at 2:36 pm

    The base image or entrypoint likely overrides the user setting. If the specified user doesn’t exist or the entrypoint switches back to root, Docker silently falls back. Checking the final image configuration usually reveals this. Takeaway: User settings only work if nothing overrides them later.

    The base image or entrypoint likely overrides the user setting.

    If the specified user doesn’t exist or the entrypoint switches back to root, Docker silently falls back. Checking the final image configuration usually reveals this.

    Takeaway: User settings only work if nothing overrides them later.

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  4. Asked: November 4, 2025In: Cloud & DevOps

    Why does my Docker container run as root even though I specified a user?

    Marnus
    Marnus Begginer
    Added an answer on January 5, 2026 at 2:35 pm

    The base image or entrypoint likely overrides the user setting. If the specified user doesn’t exist or the entrypoint switches back to root, Docker silently falls back. Checking the final image configuration usually reveals this. Takeaway: User settings only work if nothing overrides them later.

    The base image or entrypoint likely overrides the user setting.

    If the specified user doesn’t exist or the entrypoint switches back to root, Docker silently falls back. Checking the final image configuration usually reveals this.

    Takeaway: User settings only work if nothing overrides them later.

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  5. Asked: November 2, 2025In: Cloud & DevOps

    Why does my Kubernetes pod show ImagePullBackOff even though the image exists?

    Marnus
    Marnus Begginer
    Added an answer on January 5, 2026 at 2:34 pm

    When Kubernetes reports ImagePullBackOff, it’s almost never saying the image doesn’t exist. What it’s actually telling you is that it can’t pull the image, usually because it doesn’t have permission to do so. This most commonly happens with private registries. Even if you created an image pull secreRead more

    When Kubernetes reports ImagePullBackOff, it’s almost never saying the image doesn’t exist. What it’s actually telling you is that it can’t pull the image, usually because it doesn’t have permission to do so.

    This most commonly happens with private registries. Even if you created an image pull secret, Kubernetes won’t automatically use it unless it’s attached to the service account the pod is running under, and it must exist in the same namespace. Another surprisingly common issue is a tiny typo or case mismatch in the image name or tag. Container registries are strict, and Kubernetes won’t try to guess what you meant.

    People often waste time rebuilding or re-pushing images when the real issue is simply authentication.

    Takeaway: Treat ImagePullBackOff as a credentials or reference problem before assuming the image itself is broken.

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  6. Asked: September 25, 2025In: Cloud & DevOps

    Why does Kubernetes Horizontal Pod Autoscaler not scale even when CPU usage is high?

    Julie Robertson
    Julie Robertson Begginer
    Added an answer on January 5, 2026 at 2:31 pm

    Autoscaling relies on metrics and resource requests, not just raw CPU usage. If the metrics server isn’t working or your pods don’t define CPU requests, Kubernetes has nothing to scale against. CPU limits alone are not enough, which surprises many people the first time they configure autoscaling. WhRead more

    Autoscaling relies on metrics and resource requests, not just raw CPU usage.

    If the metrics server isn’t working or your pods don’t define CPU requests, Kubernetes has nothing to scale against. CPU limits alone are not enough, which surprises many people the first time they configure autoscaling.

    When autoscaling doesn’t react, the issue is usually missing data rather than incorrect thresholds.

    Takeaway: Autoscaling only works when metrics and requests are both present.

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  7. Asked: November 4, 2025In: Cloud & DevOps

    Why does Terraform fail with “provider configuration not present” during destroy?

    Julie Robertson
    Julie Robertson Begginer
    Added an answer on January 5, 2026 at 2:29 pm

    Terraform still needs the provider configuration that was used to create the resource, even during destruction. If you removed or renamed a provider after resources were created, Terraform can no longer manage them. This often happens after refactoring modules or cleaning up unused providers too earRead more

    Terraform still needs the provider configuration that was used to create the resource, even during destruction.

    If you removed or renamed a provider after resources were created, Terraform can no longer manage them. This often happens after refactoring modules or cleaning up unused providers too early.

    Reintroducing the provider temporarily allows Terraform to finish cleanup safely.

    Takeaway: Never remove a provider until all resources using it are gone.

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  1. Asked: June 19, 2025In: Deep Learning

    Why does my autoencoder reconstruct training images well but fails on new ones?

    Jacob Fatu
    Jacob Fatu Begginer
    Added an answer on January 14, 2026 at 3:48 pm

    This happens because the autoencoder has overfit the training distribution. Instead of learning general representations, it memorized pixel-level details of the training images, which do not generalize. Autoencoders with too much capacity can easily become identity mappings, especially when trainedRead more

    This happens because the autoencoder has overfit the training distribution. Instead of learning general representations, it memorized pixel-level details of the training images, which do not generalize.

    Autoencoders with too much capacity can easily become identity mappings, especially when trained on small or uniform datasets. In this case, low loss simply means the network copied what it saw.

    Reducing model size, adding noise, or using variational autoencoders forces the model to learn meaningful latent representations instead of memorization.

    Common mistakes:

    • Using too large a bottleneck

    • No noise or regularization

    • Training on limited data

    The practical takeaway is that low reconstruction loss does not mean useful representations.

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  2. Asked: February 14, 2025In: Deep Learning

    Why does my object detection model miss small objects even though it detects large ones accurately?

    Jacob Fatu
    Jacob Fatu Begginer
    Added an answer on January 14, 2026 at 3:47 pm

    This happens because most detection architectures naturally favor large objects due to how feature maps are constructed. In convolutional networks, deeper layers capture high-level features but at the cost of spatial resolution. Small objects can disappear in these layers, making them difficult forRead more

    This happens because most detection architectures naturally favor large objects due to how feature maps are constructed. In convolutional networks, deeper layers capture high-level features but at the cost of spatial resolution. Small objects can disappear in these layers, making them difficult for the detector to recognize.

    If your model uses only high-level feature maps for detection, the network simply does not see enough detail to identify small items. This is why modern detectors use feature pyramids or multi-scale feature maps. Without these, the network cannot learn reliable representations for objects that occupy only a few pixels.

    Using architectures with feature pyramid networks (FPN), increasing input resolution, and adding more small-object examples to the training set all improve this behavior. You should also check anchor sizes and ensure they match the scale of objects in your dataset.

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  3. Asked: November 11, 2025In: Deep Learning

    Why does my medical imaging model perform well on one hospital’s data but poorly on another’s?

    Jacob Fatu
    Jacob Fatu Begginer
    Added an answer on January 14, 2026 at 3:44 pm

    This happens because the model learned scanner-specific patterns instead of disease features. Differences in equipment, resolution, contrast, and noise create hidden signatures that neural networks can easily latch onto. When the model sees data from a new hospital, those hidden cues disappear, so tRead more

    This happens because the model learned scanner-specific patterns instead of disease features. Differences in equipment, resolution, contrast, and noise create hidden signatures that neural networks can easily latch onto.

    When the model sees data from a new hospital, those hidden cues disappear, so the learned representations no longer match. This is a classic case of domain shift.

    Training on multi-source data, using domain-invariant features, and applying normalization across imaging styles improves cross-hospital generalization.

    Common mistakes:

    • Training on a single source

    • Ignoring domain variation

    • No normalization between datasets

    The practical takeaway is that medical models must be trained across domains to generalize safely.

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