Exam DP-203 topic 4 question 39 discussion

Correct answer is A. We are just copying files between folders. Selecting binary copy, ADF will not check schema. With D we would discard data With C we would change file contents

It's tricky. Not D, because you don't just throw away data. Likely not C, because it doesn't solve for future schema variability. (Avro formats are usually chosen in situations where the schema may evolve over time, because they store both the data and schema in the file itself.) A makes most sense, since you're just trying to move files over. Binary preserves everything as-is, and you can read/interpret them as ASCII/UTF-8/whatever later.

The goal is to copy from source to destination. Period. Binary is not analysed (images, videos, etc). Treat text as binary and you'll ve fine. https://learn.microsoft.com/en-us/azure/data-factory/pipeline-trigger-troubleshoot-guide#you-see-a-delimitedtextmorecolumnsthandefined-error-when-copying-a-pipeline

chatgpt: The best solution to resolve the error is: Add an explicit mapping. Adding an explicit mapping will ensure that the data from the source file is correctly mapped to the destination columns, thus resolving the error related to the column count mismatch. Changing the copy activity setting to binary copy may not directly address the root cause of the error, which is a column count mismatch. Binary copy may still encounter the same issue if the source data does not match the expected column count in the destination. Therefore, while changing the copy activity setting to binary copy might be beneficial in some scenarios, it may not effectively resolve the specific error mentioned.

Error explicitly points error to a AVRO file, not CSV. I don't know if it could be possible to have schema mismatch on a AVRO file (I don't think it resembles a CSV at all), but setting up a binary copy activity colves the error if the requirement is just to copy files from A to B. Obviously, it is not the best solution, but it depends on the variety of file formats inside the folder. In case all the files are AVRO formatted, there's a beautiful dataset enabling to properly handle such a format --> https://learn.microsoft.com/en-us/azure/data-factory/format-avro

Resolution https://learn.microsoft.com/en-us/azure/data-factory/pipeline-trigger-troubleshoot-guide#you-see-a-delimitedtextmorecolumnsthandefined-error-when-copying-a-pipeline Select the Binary Copy option while creating the Copy activity. This way, for bulk copies or migrating your data from one data lake to another, Data Factory won't open the files to read the schema. Instead, Data Factory will treat each file as binary and copy it to the other location.

Adding an explicit mapping allows you to define the exact structure of the data being copied, including the number and names of columns. This ensures that the Copy activity can handle any inconsistencies in the source data and prevent this error from occurring.

Correct answer is A https://learn.microsoft.com/en-us/azure/data-factory/pipeline-trigger-troubleshoot-guide#you-see-a-delimitedtextmorecolumnsthandefined-error-when-copying-a-pipeline

Switch the Copy activity to Binary Copy Binary Copy can help to resolve this error by copying the data from the source file without any data conversion. This means that the data will be copied as-is, even if it contains more columns than the sink table expects. However, it does not support data transformation. To use Binary Copy for this task, you will need to configure the source and sink connections to point to the respective folders. For the source connection, you can use the Delimited Text connector in ADF. For the sink connection, you can also use the Delimited Text connector or another connector that supports the file format in the target folder. Use an explicit mapping If you want to copy the data from the source file and transform it to match the schema of the sink table, you can use an explicit mapping. This will allow you to map the source columns to the corresponding sink columns.

C. Add an explicit mapping. Explicit mapping involves specifying the mapping between source and destination columns explicitly. By doing this, you can ensure that each column in the source file is correctly mapped to its corresponding column in the destination file, which helps to address issues related to column count mismatches. While other options may have their use cases, such as changing the copy activity setting to Binary Copy or enabling fault tolerance to skip incompatible rows, adding an explicit mapping (Option C) is specifically designed to handle issues where the source and destination structures do not match in terms of column count or order. Therefore, in the context of resolving a "DelimitedTextMoreColumnsThanDefined" error, adding an explicit mapping is the most appropriate action.

Vote for C we have a schema mismatch -) Also • Option A: Binary Copy is used for copying non-parseable files like images or videos, not for structured data like CSV.

https://sqlwithmanoj.com/2020/07/29/azure-data-factory-adf-pipeline-failure-found-more-columns-than-expected-column-count-delimitedtextmorecolumnsthandefined/

A. Change the Copy activity setting to Binary Copy: This would bypass the error by copying the files as-is without interpreting the contents. This method might be suitable if the files are not strictly delimited text files or if you plan to handle the data inconsistency at a later stage or in a different part of the pipeline.

A is correct

I would go with Option C- Add an explicit mapping. Laying out possible derivations from the question 1. the actual error says - column mismatch . 2. Even though the filename is "filename.avro" , it could just be a filename, the source file type is CSV/TSV. Possible answers 1. Add an explicit mapping 2. Enabling Fault tolerance to skip incompatible rows I think both would be a possible solution, but to me, skipping incompatible rows is more of a temporary solution and explicit mapping would be more permanent for this error. I'm also excluding future schema issues that arise after this as there is no information about it.

It says CSV/tsv source but file is avro so A is the answer

I was pondering a bit about this one, and decided to go with D. Reasoning behind this is because the question was "how to resolve this error?" and 100% preservation of source data hasn't been a condition, hence D is the most straightforward.