最新のNS0-901問題集で試験を準備する

The data pipeline that updates the vector database from the StorageGRID data lake fails. An MLOps engineer uses the BlueXP API to check the status of the copy and sync job responsible for moving new documents to a staging area for processing.
The API returns the following JSON response:
{
"id": "rel_5f8g7h2k",
"volumeName": "product_docs_source",
"svmName": "svm_datalake",
"relationshipStatus": "FAILED",
"s3Location": "s3://vector-staging-bucket/latest/",
"progress": 87,
"failureMessage": "Sync failed.
Target credentials invalid. Check permissions for data broker 'db-gcp-1' on target bucket 'vector- staging-bucket'."
}
Based on this output, what is the most direct cause of the pipeline failure?

• A. The BlueXP data broker service account or role lacks the necessary IAM permissions to write to the target S3 bucket.
• B. The source StorageGRID system is offline.
• C. The network connection between the data broker and the StorageGRID system is blocked by a firewall.
• D. The vector database embedding process has crashed.

A data science team reports that their Jupyter notebook pod, which was previously working, is now failing to start. The pod's status is 'CrashLoopBackOff'. An MLOps engineer investigates and finds that the pod's PersistentVolumeClaim (PVC) is bound, but the pod logs show a "Permission denied" error when trying to write to its '/data' mount point.
The engineer checks the Trident backend configuration associated with the pod's StorageClass:
apiVersion: trident.netapp.io/v1
kind: TridentBackendConfig
metadata:
name: ontap-nas-eco
spec:
version: 1
storageDriverName: ontap-nas
managementLIF: 10.10.20.5
dataLIF: 10.10.20.10
svm: svm-prod-ds
exportPolicy: read-only-policy
What is the most likely cause of the "Permission denied" error?

• A. The Trident backend is configured to use an export policy ('read-only-policy') that does not grant write permissions to the Kubernetes nodes.
• B. The 'dataLIF' is configured incorrectly and is unreachable from the Kubernetes nodes.
• C. The Kubernetes pod has an invalid 'securityContext' that prevents it from writing to any volume.
• D. The 'storageDriverName' should be 'ontap-san' for all AI workloads.

An AI infrastructure engineer is troubleshooting a poorly performing distributed training job. The job is running across multiple nodes, each equipped with powerful GPUs. The engineer observes that overall GPU utilization is unexpectedly low. System-level monitoring on the compute nodes provides the following metrics during a training run.
avg_gpu_utilization: 25%
avg_cpu_iowait_percent: 65%
avg_network_bandwidth_util: 95% (on a 10GbE network)
storage_array_latency: <1ms
Given these metrics, what is the most likely bottleneck causing the low GPU utilization?

• A. The network connecting the compute nodes and storage is saturated and has become the primary bottleneck.
• B. The CPU is underpowered and cannot preprocess the data fast enough for the GPUs.
• C. The storage array is too slow and cannot serve data quickly enough.
• D. The GPUs are faulty and cannot process data at their rated speed.

An organization is planning to deploy a large AI infrastructure but wants to avoid a large, upfront capital expenditure. They prefer an operational expenditure (OpEx) model where they pay for storage and compute resources as they are consumed. They also need the flexibility to scale resources up or down based on project demands.
Which NetApp consumption model is specifically designed to meet these financial and operational requirements?

• A. A standard capital purchase of AFF and ASA systems.
• B. NetApp Cloud Volumes ONTAP PAYGO licensing only.
• C. A perpetual licensing model for all ONTAP software features.
• D. NetApp Keystone, which provides a subscription-based, pay-as-you-go service for on-premises and cloud storage.

An AI platform is suffering from poor performance during distributed training jobs. The training data resides on a single, large NFS volume. Monitoring shows that while the overall network throughput to the storage system is high, individual GPU nodes experience significant I/O wait times, and the single ONTAP volume is becoming a performance bottleneck. The goal is to re- architect the storage layout to maximize read parallelism and throughput for the training cluster.
Which two actions should the architect take to address this performance bottleneck? (Choose 2.)

• A. Enable QoS maximums on the training volume to limit its IOPS.
• B. Increase the number of network ports connected to the storage controller.
• C. Replace the NFS protocol with iSCSI for all training data access.
• D. Use NetApp FlexCache to create a local cache of the training data on each compute node.
• E. Implement a NetApp FlexGroup volume to spread the dataset across multiple constituent volumes and aggregates.