fix links

Author: b-ahibbard

articles/azure-netapp-files/testing-methodology.md

@@ -75,7 +75,7 @@ For official benchmark results for how FIO performs in Azure NetApp Files, see [
 
 FIO can be run with specific options to control how a performance benchmark reads and writes files. In the benchmarks tests with caching excluded, the FIO flag `randrepeat=0` was used to avoid caching by running a true random workload rather than a repeated pattern. 
 
-**[`randrepeat`](https://fio.readthedocs.io/latest/fio_doc.html#cmdoption-arg-randrepeat)**
+**[`randrepeat`]https://fio.readthedocs.io/en/latest/fio_doc.html#i-o-type)**
 
 By default, when `randrepeat` isn't defined, the FIO tool sets the value to "true," meaning that the data produced in the files isn't truly random. Thus, filesystem caches aren't utilized to improve overall performance of the workload.  
 

articles/azure-netapp-files/workload-types.md

@@ -21,7 +21,7 @@ For more information, see [Understand large volumes in Azure NetApp Files](large
 
 * **Specific offset, streaming random read/write workloads:** Online transaction processing (OLTP) databases are typical here. A signature of an OLTP workload is a dependence on random read to find the desired file offset (such as a database table row) and write performance against a small number of files. With this type of workload, tens of thousands to hundreds of thousands of I/O operations are common. Application vendors and database administrators typically have specific latency targets for these workloads. In most cases, Azure NetApp Files regular volumes are best suited for this workload. 
 
-* **Whole file streaming workloads:** Examples include post-production media rendering of media repositories, high-performance computing suites such as those seen in computer-aided engineering/design suites (for example, computational fluid dynamics), oil and gas suites, and machine learning fine-tuning frameworks. A hallmark of this type of workload is larger files read or written in a continuous manner. For these workloads, storage throughput is the most critical attribute as it has the biggest impact on time to completion. Latency sensitivity is common here as workloads typically use a fixed amount of concurrency, thus throughput is determined by latency. Workloads typical of post-production are latency sensitive to the degree that framerate is only achieved when specific latency values are met. Both Azure NetApp Files regular volumes and Azure NetApp Files large volumes are appropriate for these workloads, with large volumes providing [more capacity](azure-netapp-files-resource-limits) and [higher file count possibilities](maxfiles-concept.md). 
+* **Whole file streaming workloads:** Examples include post-production media rendering of media repositories, high-performance computing suites such as those seen in computer-aided engineering/design suites (for example, computational fluid dynamics), oil and gas suites, and machine learning fine-tuning frameworks. A hallmark of this type of workload is larger files read or written in a continuous manner. For these workloads, storage throughput is the most critical attribute as it has the biggest impact on time to completion. Latency sensitivity is common here as workloads typically use a fixed amount of concurrency, thus throughput is determined by latency. Workloads typical of post-production are latency sensitive to the degree that framerate is only achieved when specific latency values are met. Both Azure NetApp Files regular volumes and Azure NetApp Files large volumes are appropriate for these workloads, with large volumes providing [more capacity](azure-netapp-files-resource-limits.md) and [higher file count possibilities](maxfiles-concept.md). 
 
 
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