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NVIDIA AI Operations Sample Questions (Q28-Q33):

NEW QUESTION # 28
When deploying a DOCA application that utilizes DPDK on a BlueField-2 DPU, what are the key considerations for ensuring optimal performance?

A. Disable all interrupts to avoid context switching and maximize throughput.
B. Use the correct DPDK drivers for the Mellanox ConnectX adapters to enable hardware offload capabilities.
C. Allocate sufficient huge pages to DPDK to minimize TLB misses and improve memory access performance.
D. Disable CPU frequency scaling to ensure consistent performance.
E. Bind DPDK application threads to specific CPU cores to avoid context switching overhead.

Answer: B,C,E

Explanation:
DPDK performance relies on huge pages, CPU affinity, and proper driver usage. CPU frequency scaling should be tuned, not necessarily disabled, and disabling all interrupts is not a feasible solution.

NEW QUESTION # 29
Explain the process to perform a Blue-Green deployment for an AI model serving application running on a BCM-managed Kubernetes cluster. How do you minimize downtime and ensure a smooth transition?

A. Update the existing deployment in place, using a rolling update strategy with a small 'maxSurge' and 'maxUnavailable' to minimize disruption.
B. Take the existing application offline, deploy the new version, and then bring the application back online.
C. Create a new Kubernetes namespace for the new version, deploy the application, and then migrate traffic using DNS changes.
D. Deploy the new version of the application alongside the existing version, then switch the service to point to the new version once it's ready.
E. Use a service mesh (e.g., Istio) to gradually shift traffic from the old version to the new version, monitoring metrics and performing rollbacks if necessary.

Answer: D,E

Explanation:
Blue-green involves deploying a parallel, identical environment (the 'blue' and 'green' versions) and switching traffic. A direct service switch after verifying the new version minimizes downtime. Service meshes provide fine-grained traffic control, enabling gradual rollouts and rollbacks. Rolling updates are more like incremental updates rather than switching. DNS migration isn't instant. Taking the app offline causes significant downtime. The service mesh can provide a safe path to Blue-Green.

NEW QUESTION # 30
You're using TensorFlow with TFRecords stored on a Lustre file system for a distributed training job. Despite having a high-performance Lustre setup, your training is slow. What aspect related to the TFRecords format itself can significantly impact storage performance in this scenario, and what can you do about it?

A. The size of individual records within the TFRecords. Many small records will result in a lot of I/O operations. Use larger records by batching multiple samples into one record.
B. The number of TFRecord files. More files will always improve performance on Lustre.
C. The compression algorithm used for TFRecords. Uncompressed TFRecords are always faster on Lustre.
D. The TFRecords format inherently causes metadata overhead that slows down Lustre.
E. The Python version used by TensorFlow. Upgrade to the latest Python version.

Answer: A

Explanation:
The size of the records affects Lustre performance when many small records exist. By using larger records by batching multiple samples into one record, the I/O operations get reduced therefore enhancing performance.

NEW QUESTION # 31
You have a Kubernetes cluster running AI workloads. The pods are experiencing intermittent storage performance issues, particularly when writing checkpoints. You are using a Container Storage Interface (CSI) driver for your storage. How would you go about troubleshooting this issue, focusing on the CSI driver and Kubernetes interaction?

A. Check kubernetes component logs such as kube-scheduler for any failures in scheduling the pods
B. Restart all pods in the cluster to clear any potential caching issues.
C. Monitor the performance metrics of the underlying storage system (e.g., IOPS, latency, throughput) to identify any bottlenecks.
D. Check the Kubernetes events related to the PersistentVolumeClaims (PVCs) used by the pods for any storage-related errors or delays.
E. Examine the logs of the CSI driver controller and node components for errors or warnings related to volume provisioning, attachment, and detachment.

Answer: C,D,E

Explanation:
The CSI driver logs provide insights into storage operations initiated by Kubernetes. Examining Kubernetes events related to PVCs can reveal errors during provisioning or attachment. Underlying storage metrics highlight performance bottlenecks. Restarting all pods is a bad idea and should not be done unless you have a very good reason.

NEW QUESTION # 32
You are tasked with deploying a DOCA application on a DPU running in an environment with strict security requirements. The application needs to access sensitive data, and you need to ensure that the data is protected at all times. Which of the following security measures should be implemented?

A. Secure boot: Enable secure boot to ensure that only trusted code is executed on the DPU.
B. Data encryption: Encrypt all sensitive data at rest and in transit using strong encryption algorithms.
C. Intrusion detection and prevention: Deploy intrusion detection and prevention systems to detect and prevent unauthorized access to sensitive data.
D. Enable debug mode: to see all packets
E. Access control: Implement strict access control policies to limit access to sensitive data to authorized users and processes only.

Answer: A,B,C,E

Explanation:
Data encryption, access control, secure boot, and intrusion detection/prevention are all essential security measures for protecting sensitive data. Enable debug mode exposes the sensitive data, and therefore not a solution for secure deployment.

NEW QUESTION # 33
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