Snowflake ARA-C01 Practice Test - Questions Answers, Page 2

The SQL statement is a command for creating a pipe in Snowflake, which is an object that defines the COPY INTO <table> statement used by Snowpipe to load data from an ingestion queue into tables1.The statement uses a subquery in the FROM clause to transform the data from the staged files before loading it into the table2.

The transformations supported in the subquery are as follows2:

Data can be filtered by an optional WHERE clause, which specifies a condition that must be satisfied by the rows returned by the subquery. For example:

SQLAI-generated code. Review and use carefully.More info on FAQ.

create pipe mypipe as

copy into mytable

from (

select * from @mystage

where col1 = 'A' and col2 > 10

);

Columns can be reordered, which means changing the order of the columns in the subquery to match the order of the columns in the target table. For example:

SQLAI-generated code. Review and use carefully.More info on FAQ.

create pipe mypipe as

copy into mytable (col1, col2, col3)

from (

select col3, col1, col2 from @mystage

);

Columns can be omitted, which means excluding some columns from the subquery that are not needed in the target table. For example:

SQLAI-generated code. Review and use carefully.More info on FAQ.

create pipe mypipe as

copy into mytable (col1, col2)

from (

select col1, col2 from @mystage

);

The other options are not supported in the subquery because2:

Type casts are not supported, which means changing the data type of a column in the subquery. For example, the following statement will cause an error:

SQLAI-generated code. Review and use carefully.More info on FAQ.

create pipe mypipe as

copy into mytable (col1, col2)

from (

select col1::date, col2 from @mystage

);

Incoming data can not be joined with other tables, which means combining the data from the staged files with the data from another table in the subquery. For example, the following statement will cause an error:

SQLAI-generated code. Review and use carefully.More info on FAQ.

create pipe mypipe as

copy into mytable (col1, col2, col3)

from (

select s.col1, s.col2, t.col3 from @mystage s

join othertable t on s.col1 = t.col1

);

The ON ERROR - ABORT statement command can not be used, which means aborting the entire load operation if any error occurs. This command can only be used in the COPY INTO <table> statement, not in the subquery. For example, the following statement will cause an error:

SQLAI-generated code. Review and use carefully.More info on FAQ.

create pipe mypipe as

copy into mytable

from (

select * from @mystage

on error abort

);

1: CREATE PIPE | Snowflake Documentation

2: Transforming Data During a Load | Snowflake Documentation

Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported. This could be caused by the following factors:

The order of the keys in the JSON was changed. Snowflake stores semi-structured data internally in a column-like structure for the most common elements, and the remainder in a leftovers-like column. The order of the keys in the JSON affects how Snowflake determines the common elements and how it optimizes the query performance. If the order of the keys in the JSON was changed, Snowflake might have to re-parse the data and re-organize the internal storage, which could result in slower query performance.

There were variations in string lengths for the JSON values in the recent data imports. Non-native values, such as dates and timestamps, are stored as strings when loaded into a VARIANT column. Operations on these values could be slower and also consume more space than when stored in a relational column with the corresponding data type. If there were variations in string lengths for the JSON values in the recent data imports, Snowflake might have to allocate more space and perform more conversions, which could also result in slower query performance.

The other options are not valid causes for poor query performance:

There were JSON nulls in the recent data imports. Snowflake supports two types of null values in semi-structured data: SQL NULL and JSON null. SQL NULL means the value is missing or unknown, while JSON null means the value is explicitly set to null. Snowflake can distinguish between these two types of null values and handle them accordingly. Having JSON nulls in the recent data imports should not affect the query performance significantly.

The recent data imports contained fewer fields than usual. Snowflake can handle semi-structured data with varying schemas and fields. Having fewer fields than usual in the recent data imports should not affect the query performance significantly, as Snowflake can still optimize the data ingestion and query execution based on the existing fields.

Considerations for Semi-structured Data Stored in VARIANT

Snowflake Architect Training

Snowflake query performance on unique element in variant column

Snowflake variant performance

Partitioning an external table is a technique that improves the performance of queries executed against the table by reducing the amount of data scanned. Partitioning an external table involves creating one or more partition columns that define how the table is logically divided into subsets of data based on the values in those columns. The partition columns can be derived from the file metadata (such as file name, path, size, or modification time) or from the file content (such as a column value or a JSON attribute).Partitioning an external table allows the query optimizer to prune the files that do not match the query predicates, thus avoiding unnecessary data scanning and processing2

The other options are not effective steps for improving the performance of queries executed against an external table:

Shorten the names of the source files. This option does not have any impact on the query performance, as the file names are not used for query processing.The file names are only used for creating the external table and displaying the query results3

Convert the source files' character encoding to UTF-8. This option does not affect the query performance, as Snowflake supports various character encodings for external table files, such as UTF-8, UTF-16, UTF-32, ISO-8859-1, and Windows-1252.Snowflake automatically detects the character encoding of the files and converts them to UTF-8 internally for query processing4

Use an internal stage instead of an external stage to store the source files. This option is not applicable, as external tables can only reference files stored in external stages, such as Amazon S3, Google Cloud Storage, or Azure Blob Storage.Internal stages are used for loading data into internal tables, not external tables5Reference:

1: SnowPro Advanced: Architect | Study Guide

2: Snowflake Documentation | Partitioning External Tables

3: Snowflake Documentation | Creating External Tables

4: Snowflake Documentation | Supported File Formats and Compression for Staged Data Files

5: Snowflake Documentation | Overview of Stages

:SnowPro Advanced: Architect | Study Guide

:Partitioning External Tables

:Creating External Tables

:Supported File Formats and Compression for Staged Data Files

:Overview of Stages

The image shows a SQL query that can be used to identify which queries are spilled to remote storage and suggests changing the warehouse parameters to address this issue. Spilling to remote storage occurs when the memory allocated to a warehouse is insufficient to process a query, and Snowflake uses disk or cloud storage as a temporary cache. This can significantly slow down the query performance and increase the cost. To troubleshoot this issue, a Snowflake Architect can run the query shown in the image to find out which queries are spilling, how much data they are spilling, and which warehouses they are using.Then, the architect can adjust the warehouse size, type, or scaling policy to provide enough memory for the queries and avoid spilling12.Reference:

Recognizing Disk Spilling

Managing the Kafka Connector

Search optimization service is a feature of Snowflake that can significantly improve the performance of certain types of lookup and analytical queries on tables.Search optimization service creates and maintains a persistent data structure called a search access path, which keeps track of which values of the table's columns might be found in each of its micro-partitions, allowing some micro-partitions to be skipped when scanning the table1.

Search optimization service can significantly improve query performance on partitioned external tables, which are tables that store data in external locations such as Amazon S3 or Google Cloud Storage.Partitioned external tables can leverage the search access path to prune the partitions that do not contain the relevant data, reducing the amount of data that needs to be scanned and transferred from the external location2.

The other options are not correct because:

A) Search optimization service works best with a column that has a high cardinality, which means that the column has many distinct values. However, there is no specific minimum number of distinct values required for search optimization service to work effectively.The actual performance improvement depends on the selectivity of the queries and the distribution of the data1.

C) Search optimization service does not help to optimize storage usage by compressing the data into a GZIP format. Search optimization service does not affect the storage format or compression of the data, which is determined by the file format options of the table.Search optimization service only creates an additional data structure that is stored separately from the table data1.

D) The table does not need to be clustered with a key having multiple columns for effective search optimization. Clustering is a feature of Snowflake that allows ordering the data in a table or a partitioned external table based on one or more clustering keys. Clustering can improve the performance of queries that filter on the clustering keys, as it reduces the number of micro-partitions that need to be scanned.However, clustering is not required for search optimization service to work, as search optimization service can skip micro-partitions based on any column that has a search access path, regardless of the clustering key3.

1:Search Optimization Service | Snowflake Documentation

2: Partitioned External Tables | Snowflake Documentation

3: Clustering Keys | Snowflake Documentation

The best way to address the performance issues and time-outs faced by the Store Manager team is to configure a dedicated virtual warehouse for them and make it multi-clustered. This will allow them to run their reports independently from other workloads and scale up or down the compute resources as needed. A dedicated virtual warehouse will also enable them to apply specific security and access policies for their data. A multi-clustered virtual warehouse will provide high availability and concurrency for their queries and avoid queuing or throttling.

Using a Business Intelligence tool for in-memory computation may improve performance, but it will not solve the underlying issue of insufficient compute resources in the shared virtual warehouse. It will also introduce additional costs and complexity for the data architecture.

Configuring the virtual warehouse to size 4-XL may increase the performance, but it will also increase the cost and may not be optimal for the workload. It will also not address the concurrency and availability issues that may arise from sharing the virtual warehouse with other workloads.

Advising the Store Manager team to defer report execution to off-business hours may reduce the load on the shared virtual warehouse, but it will also reduce the timeliness and usefulness of the reports for the business. It will also not guarantee that the performance issues and time-outs will not occur at other times.

Snowflake Architect Training

Snowflake SnowPro Advanced Architect Certification - Preparation Guide

SnowPro Advanced: Architect Exam Study Guide

According to the Snowflake documentation1, the Business Critical edition offers the following features that are relevant to the question:

Support for Multi-Factor Authentication (MFA): This is a standard feature available in all Snowflake editions

Support for Multi-Factor Authentication (MFA): This is a standard feature available in all Snowflake editions1.

A minimum of 2 months of Time Travel availability: This is an enterprise feature that allows users to access historical data for up to 90 days1.

Database replication in between different regions: This is an enterprise feature that enables users to replicate databases across different regions or cloud platforms1.

Native support for JDBC and ODBC: This is a standard feature available in all Snowflake editions1.

Customer-managed encryption keys using Tri-Secret Secure: This is a business critical feature that provides enhanced security and data protection by allowing customers to manage their own encryption keys1.

Support for Payment Card Industry Data Security Standards (PCI DSS): This is a business critical feature that ensures compliance with PCI DSS regulations for handling sensitive cardholder data1.

Therefore, the minimum Snowflake edition that should be selected during account creation to provide all the listed services is the Business Critical edition.

Snowflake Editions | Snowflake Documentation

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.

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.

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.

1: COPY INTO <table> | Snowflake Documentation

2: MATCH_BY_COLUMN_NAME | Snowflake Documentation