Most Recent Pure Storage FAAA_004 Exam Dumps

If a customer is unsatisfied with the level of data reduction on their FlashArray, the SE should make the following two statements:

FlashArray's deduplication effectiveness will usually increase as the data quantity grows:

Deduplication relies on identifying and eliminating duplicate data blocks. As more data is written to the array, the likelihood of finding duplicates increases, improving the overall deduplication ratio.

Customers should expect better data reduction results over time as their dataset grows.

The Right-Size Guarantee means that the customer can work with their SE if necessary:

Pure Storage's Right-Size Guarantee ensures that customers receive the expected effective capacity based on their workload's data reduction profile. If the actual data reduction does not meet expectations, the customer can collaborate with their SE to address the issue and potentially adjust their subscription or configuration.

Why Not the Other Options?

A . A FlashArray's compression and deduplication will need to be tuned for data subsets:

FlashArray's data reduction techniques (compression and deduplication) are automatic and do not require manual tuning. This statement is misleading.

C . FlashArray data reduction needs to be tuned to increase its effectiveness:

Similar to Option A, FlashArray's data reduction mechanisms are fully automated and do not require manual intervention.

Key Points:

Data Growth: Deduplication effectiveness improves as more data is written to the array.

Right-Size Guarantee: Provides assurance that customers can work with their SE to address data reduction concerns.

Automatic Optimization: FlashArray's data reduction features are self-optimizing and do not require manual tuning.

Pure Storage FlashArray Documentation: 'Understanding Data Reduction and Capacity Planning'

Pure Storage Whitepaper: 'Maximizing Data Reduction with FlashArray'

Pure Storage Knowledge Base: 'Right-Size Guarantee Terms and Conditions'

If a customer notices a low data reduction ratio upon initial data ingest, the Purity data reduction technique that will help increase the data reduction ratio over time is deep deduplication and deep compression .

Why This Matters:

Deep Deduplication and Deep Compression:

Purity//FA (the operating system for FlashArray) applies deduplication to eliminate duplicate data blocks and compression to reduce the size of unique data blocks.

These techniques are applied continuously as new data is written to the array. Over time, as more data is ingested and patterns emerge, the effectiveness of deduplication and compression increases, leading to a higher data reduction ratio.

For example, deduplication becomes more effective as the dataset grows and more duplicates are identified. Similarly, compression benefits from identifying repetitive patterns in larger datasets.

Why Not the Other Options?

B . Snapshot cleanup and garbage collection:

Snapshot cleanup and garbage collection are maintenance processes that reclaim space from deleted snapshots or unused data blocks. While these processes free up space, they do not directly contribute to increasing the data reduction ratio.

C . Capacity consolidation and cloning:

Capacity consolidation refers to combining workloads onto fewer arrays, and cloning creates space-efficient copies of volumes. While cloning leverages data reduction techniques, it does not inherently improve the overall data reduction ratio for existing data.

D . RAID-HA protection and AES-256 encryption:

RAID-HA (high availability) ensures data redundancy, and AES-256 encryption secures data. Neither of these features impacts the data reduction ratio.

Key Points:

Deep Deduplication and Compression: Continuously optimize storage efficiency as more data is ingested.

Data Reduction Ratio: Improves over time as deduplication identifies duplicates and compression reduces unique data.

Purity//FA Automation: These techniques are fully automated and do not require manual intervention.

Pure Storage FlashArray Documentation: 'Understanding Data Reduction in Purity//FA'

Pure Storage Whitepaper: 'Maximizing Data Reduction with FlashArray'

Pure Storage Knowledge Base: 'How Deduplication and Compression Work in FlashArray'

The customer has a cloud-first strategy and does not wish to own additional capital assets, meaning they are looking for a solution that operates entirely within the public cloud without requiring on-premises hardware. Additionally, their primary goal is to reduce cloud storage costs while managing a large volume of database data (100 TB).

Cloud Block Store (CBS) is the ideal solution for this requirement. CBS is a software-defined block storage solution that runs natively in the public cloud (e.g., AWS or Azure). It provides enterprise-grade storage features like deduplication, compression, and thin provisioning, which help optimize storage usage and reduce costs. By leveraging CBS, the customer can efficiently manage their database workloads in the cloud while minimizing storage expenses.

Why Not the Other Options?

A . Evergreen//Flex: This is a subscription-based model for on-premises FlashArray hardware. Since the customer does not want to own any additional capital assets, this option does not align with their cloud-first strategy.

B . Evergreen//Forever: Similar to Evergreen//Flex, this is an on-premises solution that involves hardware ownership, which does not meet the customer's requirements.

D . Portworx DBaaS: While Portworx is a containerized storage solution for databases, it is primarily designed for Kubernetes environments and does not directly address the need to reduce cloud storage costs for traditional database workloads.

Key Points:

Cloud Block Store: A cloud-native block storage solution that reduces storage costs through advanced data reduction techniques.

Cloud-First Strategy: CBS aligns perfectly with the customer's desire to avoid capital expenditures and operate entirely within the public cloud.

Pure Storage Cloud Block Store Documentation: 'Deploying and Managing Cloud Block Store in Azure'

Pure Storage Whitepaper: 'Optimizing Cloud Costs with Cloud Block Store'

Pure Storage Best Practices Guide: 'Database Workloads in the Public Cloud'

The customer is looking for a storage system that supports 20 TB of file shares , 800 TB of workloads , has a low cost per GB , and can utilize CloudSnap in the future. The best recommendation is FlashArray//C .

Why This Matters:

FlashArray//C:

FlashArray//C is designed for capacity-optimized workloads , making it ideal for use cases requiring large amounts of storage at a lower cost per GB compared to higher-performance arrays like FlashArray//X.

It supports QLC flash technology , which provides high density and cost efficiency for less performance-intensive workloads.

CloudSnap is fully supported on FlashArray//C, enabling snapshots to be offloaded to public cloud storage for disaster recovery or archival purposes.

Why Not the Other Options?

A . FlashArray//X:

FlashArray//X is optimized for high-performance workloads, such as databases and mission-critical applications. While it supports CloudSnap, it is more expensive and not the most cost-effective solution for large-scale capacity needs.

C . Cloud Block Store:

Cloud Block Store is a cloud-native block storage solution that runs in public clouds (e.g., AWS, Azure). It does not meet the requirement for on-premises storage with file shares and CloudSnap utilization.

D . FlashBlade//S:

FlashBlade//S is designed for file and object storage but is typically used for high-performance, unstructured data workloads. It is more expensive than FlashArray//C and not necessary for this use case.

Key Points:

FlashArray//C: Provides high-density storage at a low cost per GB, ideal for large-scale workloads.

CloudSnap Support: Enables offloading snapshots to the cloud for disaster recovery or archival purposes.

Cost Efficiency: Balances performance and cost, making it suitable for file shares and large datasets.

Pure Storage FlashArray//C Documentation: 'Use Cases for FlashArray//C'

Pure Storage Whitepaper: 'Optimizing Storage Costs with FlashArray//C'

Pure Storage Knowledge Base: 'Choosing the Right FlashArray Model for Your Workload'

The customer currently has a FlashArray//X with 40 TB of available block storage and needs to add 10 TB of file workloads while minimizing infrastructure costs. Let's analyze the options:

Analysis of Options:

A . Run both workloads on the current FlashArray :

Pure Storage FlashArray supports both block and file workloads using the Purity File Services feature, which allows customers to run file workloads directly on their FlashArray.

Since the FlashArray already has 40 TB of available storage, adding 10 TB of file workloads is feasible without requiring additional hardware. This is the most cost-effective solution.

B . Add another disk pool for file storage to their current FlashArray :

Adding a separate disk pool for file storage is unnecessary because Purity File Services can handle both block and file workloads on the same array.

C . Purchase an entry-level FlashBlade for the file workload :

While FlashBlade is designed for file and object workloads, purchasing a new FlashBlade would be significantly more expensive than leveraging the existing FlashArray. This option does not align with the customer's goal of minimizing costs.

D . NDU the FlashArray //X to a //XL and run both workloads there :

Upgrading the FlashArray//X to a FlashArray//XL via a Non-Disruptive Upgrade (NDU) is unnecessary for this use case. The current FlashArray//X has sufficient capacity to handle both workloads, and upgrading to a higher-tier array would increase costs unnecessarily.

Recommendation:

The most cost-effective solution is A. Run both workloads on the current FlashArray , leveraging Purity File Services to support the file workload.

Purity File Services Documentation :

Purity File Services

Explains how to configure and use file services on FlashArray.

FlashArray Use Cases :

FlashArray Use Cases

Highlights the versatility of FlashArray for both block and file workloads.