Free Access to Snowflake.ARA-C01.v2026-04-11.q236 with Valid Practice Test (Page 13)

Question 56

An Architect uses COPY INTO with the ON_ERROR=SKIP_FILE option to bulk load CSV files into a table called TABLEA, using its table stage. One file named file5.csv fails to load. The Architect fixes the file and re-loads it to the stage with the exact same file name it had previously.
Which commands should the Architect use to load only file5.csv file from the stage? (Choose two.)

A.COPY INTO tablea FROM @%tablea RETURN_FAILED_ONLY = TRUE;
B.COPY INTO tablea FROM @%tablea;
C.COPY INTO tablea FROM @%tablea FILES = ('file5.csv');
D.COPY INTO tablea FROM @%tablea FORCE = TRUE;
E.COPY INTO tablea FROM @%tablea NEW_FILES_ONLY = TRUE;
F.COPY INTO tablea FROM @%tablea MERGE = TRUE;

Question 57

How can the Snowpipe REST API be used to keep a log of data load history?

A.Call insertReport every 20 minutes, fetching the last 10,000 entries.
B.Call loadHistoryScan every minute for the maximum time range.
C.Call insertReport every 8 minutes for a 10-minute time range.
D.Call loadHistoryScan every 10 minutes for a 15-minutes range.

Correct Answer: D

The Snowpipe REST API provides two endpoints for retrieving the data load history: insertReport and loadHistoryScan. The insertReport endpoint returns the status of the files that were submitted to the insertFiles endpoint, while the loadHistoryScan endpoint returns the history of the files that were actually loaded into the table by Snowpipe. To keep a log of data load history, it is recommended to use the loadHistoryScan endpoint, which provides more accurate and complete information about the data ingestion process. The loadHistoryScan endpoint accepts a start time and an end time as parameters, and returns the files that were loaded within that time range. The maximum time range that can be specified is 15 minutes, and the maximum number of files that can be returned is 10,000. Therefore, to keep a log of data load history, the best option is to call the loadHistoryScan endpoint every 10 minutes for a 15-minute time range, and store the results in a log file or a table. This way, the log will capture all the files that were loaded by Snowpipe, and avoid any gaps or overlaps in the time range. The other options are incorrect because:
Calling insertReport every 20 minutes, fetching the last 10,000 entries, will not provide a complete log of data load history, as some files may be missed or duplicated due to the asynchronous nature of Snowpipe. Moreover, insertReport only returns the status of the files that were submitted, not the files that were loaded.
Calling loadHistoryScan every minute for the maximum time range will result in too many API calls and unnecessary overhead, as the same files will be returned multiple times. Moreover, the maximum time range is 15 minutes, not 1 minute.
Calling insertReport every 8 minutes for a 10-minute time range will suffer from the same problems as option A, and also create gaps or overlaps in the time range.
Reference:
Snowpipe REST API
Option 1: Loading Data Using the Snowpipe REST API
PIPE_USAGE_HISTORY

Question 58

An Architect wants to use clustering keys to improve table performance.
What should the Architect consider? (Select THREE).

A.Clustering has a storage and compute cost.
B.Snowflake recommends a maximum of five clustering keys.
C.Clustering keys are designed to co-locate data within micro-partitions.
D.Clustering keys are only recommended for tables larger than 1 GB.
E.Snowflake can cluster a materialized view to tailor for specific queries.
F.A high value returned by SYSTEM$CLUSTERING_DEPTH indicates good clustering.

Question 59

Is it possible for a data provider account with a Snowflake Business Critical edition to share data with an Enterprise edition data consumer account?

A.A Business Critical account cannot be a data sharing provider to an Enterprise consumer. Any consumer accounts must also be Business Critical.
B.If a user in the provider account with role authority to create or alter share adds an Enterprise account as a consumer, it can import the share.
C.If a user in the provider account with a share owning role sets share_restrictions to False when adding an Enterprise consumer account, it can import the share.
D.If a user in the provider account with a share owning role which also has override share restrictions privilege share_restrictions set to False when adding an Enterprise consumer account, it can import the share.

Question 60

When using the copy into <table> command with the CSV file format, how does the match_by_column_name parameter behave?

A.It expects a header to be present in the CSV file, which is matched to a case-sensitive table column name.
B.The parameter will be ignored.
C.The command will return an error.
D.The command will return a warning stating that the file has unmatched columns.

Correct Answer: B

Option B is the best design to meet the requirements because it uses Snowpipe to ingest the data continuously and efficiently as new records arrive in the object storage, leveraging event notifications. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables1. It also uses streams and tasks to orchestrate transformations on the ingested data. Streams are objects that store the change history of a table, and tasks are objects that execute SQL statements on a schedule or when triggered by another task2.
Option B also uses an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. An external function is a user-defined function that calls an external API, such as Amazon Comprehend, to perform computations that are not natively supported by Snowflake3. Finally, option B uses the Snowflake Marketplace to make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions. The Snowflake Marketplace is a platform that enables data providers to list and share their data sets with data consumers, regardless of the cloud platform or region they use4.
Option A is not the best design because it uses copy into to ingest the data, which is not as efficient and continuous as Snowpipe. Copy into is a SQL command that loads data from files into a table in a single transaction. It also exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
Option C is not the best design because it uses Amazon EMR and PySpark to ingest and transform the data, which also increases the operational complexity and maintenance of the infrastructure. Amazon EMR is a cloud service that provides a managed Hadoop framework to process and analyze large-scale data sets.
PySpark is a Python API for Spark, a distributed computing framework that can run on Hadoop. Option C also develops a python program to do model inference by leveraging the Amazon Comprehend text analysis API, which increases the development effort.
Option D is not the best design because it is identical to option A, except for the ingestion method. It still exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
References: 1: Snowpipe Overview 2: Using Streams and Tasks to Automate Data Pipelines 3: External Functions Overview 4: Snowflake Data Marketplace Overview : [Loading Data Using COPY INTO] : [What is Amazon EMR?] : [PySpark Overview]
* The copy into <table> command is used to load data from staged files into an existing table in Snowflake. The command supports various file formats, such as CSV, JSON, AVRO, ORC, PARQUET, and XML1.
* The match_by_column_name parameter is a copy option that enables loading semi-structured data into separate columns in the target table that match corresponding columns represented in the source data. The parameter can have one of the following values2:
* CASE_SENSITIVE: The column names in the source data must match the column names in the target table exactly, including the case. This is the default value.
* CASE_INSENSITIVE: The column names in the source data must match the column names in the target table, but the case is ignored.
* NONE: The column names in the source data are ignored, and the data is loaded based on the order of the columns in the target table.
* The match_by_column_name parameter only applies to semi-structured data, such as JSON, AVRO, ORC, PARQUET, and XML. It does not apply to CSV data, which is considered structured data2.
* When using the copy into <table> command with the CSV file format, the match_by_column_name parameter behaves as follows2:
* It expects a header to be present in the CSV file, which is matched to a case-sensitive table column name. This means that the first row of the CSV file must contain the column names, and they must match the column names in the target table exactly, including the case. If the header is missing or does not match, the command will return an error.
* The parameter will not be ignored, even if it is set to NONE. The command will still try to match the column names in the CSV file with the column names in the target table, and will return an error if they do not match.
* The command will not return a warning stating that the file has unmatched columns. It will either load the data successfully if the column names match, or return an error if they do not match.
References:
* 1: COPY INTO <table> | Snowflake Documentation
* 2: MATCH_BY_COLUMN_NAME | Snowflake Documentation

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