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| Section | Objectives |
|---|---|
| Performance and Optimization | - Query performance tuning
|
| Data Architecture and Design | - Data modeling and schema design
|
| Data Pipelines and Data Engineering | - Data ingestion and transformation
|
| Security, Governance, and Compliance | - Data governance
|
| Data Sharing and Collaboration | - Secure data sharing
|
>> New ARA-C01 Exam Practice <<
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NEW QUESTION # 58
What is a key consideration when setting up search optimization service for a table?
Answer: D
Explanation:
A: The Search Optimization Service is designed to accelerate the performance of queries that use filters on large tables. One of the key considerations for its effectiveness is using it with tables where the columns used in the filter conditions have a high number of distinct values, typically in the hundreds of thousands or more.
This is because the service creates a map-reduce-like index on the column to speed up queries that use point lookups or range scans on that column. The more unique values there are, the more effective the index is at narrowing down the search space.References: Snowflake documentation and best practices on the Search Optimization Service, which would be covered under the SnowPro Advanced: Architect certification materials.
NEW QUESTION # 59
A company has an external vendor who puts data into Google Cloud Storage. The company's Snowflake account is set up in Azure.
What would be the MOST efficient way to load data from the vendor into Snowflake?
Answer: C
Explanation:
The most efficient way to load data from the vendor into Snowflake is to create an external stage on Google Cloud Storage and use the external table to load the data into Snowflake (Option B). This way, you can avoid copying or moving the data across different cloud platforms, which can incur additional costs and latency. You can also leverage the external table feature to query the data directly from Google Cloud Storage without loading it into Snowflake tables, which can save storage space and improve performance. Option A is not efficient because it requires the vendor to create a Snowflake account and a data share, which can be complicated and costly. Option C is not efficient because it involves copying the data from Google Cloud Storage to Azure Blob storage using external tools, which can be slow and expensive. Option D is not efficient because it requires creating a Snowflake account in the Google Cloud Platform (GCP), ingesting data into this account, and using data replication to move the data from GCP to Azure, which can be complex and time-consuming. References: The answer can be verified from Snowflake's official documentation on external stages and external tables available on their website. Here are some relevant links:
* Using External Stages | Snowflake Documentation
* Using External Tables | Snowflake Documentation
* Loading Data from a Stage | Snowflake Documentation
NEW QUESTION # 60
A table contains five columns and it has millions of records. The cardinality distribution of the columns is shown below:
Column C4 and C5 are mostly used by SELECT queries in the GROUP BY and ORDER BY clauses.
Whereas columns C1, C2 and C3 are heavily used in filter and join conditions of SELECT queries.
The Architect must design a clustering key for this table to improve the query performance.
Based on Snowflake recommendations, how should the clustering key columns be ordered while defining the multi-column clustering key?
Answer: B
Explanation:
Explanation
According to the Snowflake documentation, the following are some considerations for choosing clustering for a table1:
* Clustering is optimal when either:
* You require the fastest possible response times, regardless of cost.
* Your improved query performance offsets the credits required to cluster and maintain the table.
* Clustering is most effective when the clustering key is used in the following types of query predicates:
* Filter predicates (e.g. WHERE clauses)
* Join predicates (e.g. ON clauses)
* Grouping predicates (e.g. GROUP BY clauses)
* Sorting predicates (e.g. ORDER BY clauses)
* Clustering is less effective when the clustering key is not used in any of the above query predicates, or when the clustering key is used in a predicate that requires a function or expression to be applied to the key (e.g. DATE_TRUNC, TO_CHAR, etc.).
* For most tables, Snowflake recommends a maximum of 3 or 4 columns (or expressions) per key.
Adding more than 3-4 columns tends to increase costs more than benefits.
Based on these considerations, the best option for the clustering key columns is C. C1, C3, C2, because:
* These columns are heavily used in filter and join conditions of SELECT queries, which are the most effective types of predicates for clustering.
* These columns have high cardinality, which means they have many distinct values and can help reduce the clustering skew and improve the compression ratio.
* These columns are likely to be correlated with each other, which means they can help co-locate similar rows in the same micro-partitions and improve the scan efficiency.
* These columns do not require any functions or expressions to be applied to them, which means they can be directly used in the predicates without affecting the clustering.
References: 1: Considerations for Choosing Clustering for a Table | Snowflake Documentation
NEW QUESTION # 61
Company A has recently acquired company B. The Snowflake deployment for company B is located in the Azure West Europe region.
As part of the integration process, an Architect has been asked to consolidate company B's sales data into company A's Snowflake account which is located in the AWS us-east-1 region.
How can this requirement be met?
Answer: B
NEW QUESTION # 62
Which of the below commands will use warehouse credits?
Answer: A,C,D
Explanation:
* Warehouse credits are used to pay for the processing time used by each virtual warehouse in Snowflake.
A virtual warehouse is a cluster of compute resources that enables executing queries, loading data, and performing other DML operations. Warehouse credits are charged based on the number of virtual warehouses you use, how long they run, and their size1.
* Among the commands listed in the question, the following ones will use warehouse credits:
* SELECT MAX(FLAKE_ID) FROM SNOWFLAKE: This command will use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the maximum value of the FLAKE_ID column2. Therefore, option B is correct.
* SELECT COUNT(*) FROM SNOWFLAKE: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows in the table3. Therefore, option C is correct.
* SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows for each distinct value of the FLAKE_ID column4. Therefore, option D is correct.
* The command that will not use warehouse credits is:
* SHOW TABLES LIKE 'SNOWFL%': This command will not use warehouse credits because it is a metadata operation that does not require a virtual warehouse to execute. The command will return the names of the tables that match the pattern 'SNOWFL%' in the current database and schema5. Therefore, option A is incorrect.
References: : Understanding Compute Cost : MAX Function : COUNT Function : GROUP BY Clause : SHOW TABLES
NEW QUESTION # 63
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