Network Appliance NS0-604 Practice Test - Questions Answers, Page 5

The issue where the Linux client shows that the NFS volume is full, despite the AWS dashboard showing available capacity in the Amazon FSx for NetApp ONTAP file system, suggests that the allocated volume size within ONTAP is smaller than the total capacity available. To resolve this, the customer should enable volume autosizing. Autosizing allows the volume to automatically increase in size as needed, preventing issues where the volume becomes full while the underlying file system still has available storage.

Increasing the capacity of the file system (B) is not necessary since the file system already has free space. Deleting snapshots (C) can free up some space, but autosizing is a more efficient solution. Tiering cold data (D) addresses long-term storage management but won't resolve the immediate issue of the volume being full.

If a customer needs to run some of their NetApp Cloud Volumes ONTAP instances on-premises, the best solution would be Azure Stack. Azure Stack extends Azure cloud capabilities to on-premises environments and supports hybrid workloads, including those based on Cloud Volumes ONTAP.

Other options like VMware Cloud (A), AWS Outpost (B), and Google Anthos (C) are cloud extensions but are not directly integrated with Cloud Volumes ONTAP in the same way as Azure Stack, which provides a native extension of Azure services to on-premises infrastructure.

To rapidly expand a storage-heavy VMware environment using Azure services, the company should utilize:

Azure VMware Solution (A): This service allows VMware workloads to be seamlessly migrated or expanded into Azure, providing a familiar environment with the scalability and flexibility of the Azure cloud. It's an ideal solution for extending on-premises VMware deployments to Azure.

Azure NetApp Files (D): Azure NetApp Files provides highly performant, scalable file storage in Azure. It integrates well with Azure VMware Solution, offering a robust storage backend for VMware workloads, especially when they are storage-heavy.

Amazon FSx for NetApp ONTAP (B) and Google Cloud VMware Engine (C) are not relevant in this Azure-specific scenario.

To automatically scan, analyze, and categorize data and take necessary actions, the customer should use BlueXP Classification. This tool helps organizations gain insights into their data by scanning and classifying it, allowing them to manage sensitive data, meet compliance requirements, and take appropriate actions based on the analysis.

BlueXP Observability (A) focuses on monitoring and performance tracking, BlueXP Digital Advisor (C) provides system recommendations, and Harvest (D) is a monitoring tool for ONTAP environments but not focused on data classification and action automation.

The company is migrating data from a 6-node, on-premises NetApp FAS cluster to NetApp Cloud Volumes ONTAP and determined that they can consolidate to two HA pairs. This means that the minimum number of Cloud Volumes ONTAP instances required is 2, as each HA pair corresponds to one instance.

1 instance (A) would not provide the required redundancy and performance, and 6 instances (B) or 4 instances (D) are unnecessary given the performance sizing results.

When deploying NetApp Cloud Volumes ONTAP for an Amazon Elastic Container Service (Amazon ECS) workload that involves large sequential I/O, the sizing characteristic to consider is MBps (megabytes per second). Large sequential I/O workloads, such as video streaming or backup operations, typically rely on high throughput rather than random IOPS.

Capacity (B) and IOPS (C) are important for other workload types, while CPU (D) is less relevant for storage throughput requirements in this context.

To add personal data identifiers from an Oracle database to NetApp BlueXP classification scans, the customer should use custom categories. Custom categories allow the user to define specific types of data (such as personal identifiers) for classification, helping BlueXP to scan and detect those specific data types within the environment.

RegEx (A) can be used for pattern matching but would require the user to manually define regular expressions, while custom keywords (D) and Data Fusion (C) are not the appropriate mechanisms for this specific use case of adding personal data identifiers to the scans. Custom categories are specifically designed for managing such identifiers.

The customer is using 30TB of capacity in NetApp Cloud Volumes ONTAP but only 10TB of this capacity is actively in use. The most cost-efficient solution in this case is to implement data tiering and optimization. Data tiering moves inactive or cold data to lower-cost storage (such as object storage in Azure), while keeping frequently accessed data on higher-performance storage. This strategy allows the customer to reduce costs by only paying for premium storage for the data that is actively in use, while moving less frequently accessed data to a cheaper storage tier.

Storing all data in the premium storage tier (A) would increase costs rather than reduce them. BlueXP backup and recovery (B) is for data protection, not cost optimization. Deploying an additional single-node Cloud Volumes ONTAP instance (D) would increase storage costs rather than optimize them.

When deploying NetApp BlueXP in a Virtual Network (VNet) with no outbound Internet access, the solution must be deployed in restricted mode. Restricted mode ensures that BlueXP can operate within a closed environment where outbound connectivity is blocked, allowing it to function without requiring access to external services on the Internet. This mode is ideal for environments with strict security and isolation requirements.

Dark mode (B) typically refers to a fully isolated environment without connectivity, but restricted mode is the correct term for BlueXP in this specific context. Private mode (D) and AJ standard mode (A) are not relevant in this scenario.