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NEW QUESTION # 226
Which of the following are characteristics of Snowflake's parameter hierarchy?
Answer: B
Explanation:
This is the correct answer because it reflects the characteristics of Snowflake's parameter hierarchy. Snowflake provides three types of parameters that can be set for an account: account parameters, session parameters, and object parameters. All parameters have default values, which can be set and then overridden at different levels depending on the parameter type. The following diagram illustrates the hierarchical relationship between the different parameter types and how individual parameters can be overridden at each level1:
As shown in the diagram, schema parameters are a type of object parameters that can be set for schemas. Schema parameters can override the account parameters that are set at the account level. For example, the LOG_LEVEL parameter can be set at the account level to control the logging level for all objects in the account, but it can also be overridden at the schema level to control the logging level for specific stored procedures and UDFs in that schema2.
The other options listed are not correct because they do not reflect the characteristics of Snowflake's parameter hierarchy. Session parameters do not override virtual warehouse parameters, because virtual warehouse parameters are a type of session parameters that can be set for virtual warehouses. Virtual warehouse parameters do not override user parameters, because user parameters are a type of session parameters that can be set for users. Table parameters do not override virtual warehouse parameters, because table parameters are a type of object parameters that can be set for tables, and object parameters do not affect session parameters1.
Reference:
Snowflake Documentation: Parameters
Snowflake Documentation: Setting Log Level
NEW QUESTION # 227
How does a standard virtual warehouse policy work in Snowflake?
Answer: C
Explanation:
A standard virtual warehouse policy is one of the two scaling policies available for multi-cluster warehouses in Snowflake. The other policy is economic. A standard policy aims to prevent or minimize queuing by starting additional clusters as soon as the current cluster is fully loaded, regardless of the number of queries in the queue. This policy can improve query performance and concurrency, but it may also consume more credits than an economic policy, which tries to conserve credits by keeping the running clusters fully loaded before starting additional clusters. The scaling policy can be set when creating or modifying a warehouse, and it can be changed at any time.
Snowflake Documentation: Multi-cluster Warehouses
Snowflake Documentation: Scaling Policy for Multi-cluster Warehouses
NEW QUESTION # 228
A DevOps team has a requirement for recovery of staging tables used in a complex set of data pipelines. The staging tables are all located in the same staging schema. One of the requirements is to have online recovery of data on a rolling 7-day basis.
After setting up the DATA_RETENTION_TIME_IN_DAYS at the database level, certain tables remain unrecoverable past 1 day.
What would cause this to occur? (Choose two.)
Answer: A,E
Explanation:
* The DATA_RETENTION_TIME_IN_DAYS parameter controls the Time Travel retention period for an object (database, schema, or table) in Snowflake. This parameter specifies the number of days for
* which historical data is preserved and can be accessed using Time Travel operations (SELECT, CREATE ... CLONE, UNDROP)1.
* The requirement for recovery of staging tables on a rolling 7-day basis means that the DATA_RETENTION_TIME_IN_DAYS parameter should be set to 7 at the database level. However, this parameter can be overridden at the lower levels (schema or table) if they have a different value1.
* Therefore, one possible cause for certain tables to remain unrecoverable past 1 day is that the DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day. This would override the database level setting and limit the Time Travel retention period for all the tables in the schema to 1 day. To fix this, the parameter should be unset or set to 7 at the schema level1. Therefore, option B is correct.
* Another possible cause for certain tables to remain unrecoverable past 1 day is that the staging tables are of the TRANSIENT type. Transient tables are tables that do not have a Fail-safe period and can have a Time Travel retention period of either 0 or 1 day. Transient tables are suitable for temporary or intermediate data that can be easily reproduced or replicated2. To fix this, the tables should be created as permanent tables, which can have a Time Travel retention period of up to 90 days1. Therefore, option D is correct.
* Option A is incorrect because the MANAGED ACCESS feature is not related to the data recovery requirement. MANAGED ACCESS is a feature that allows granting access privileges to objects without explicitly granting the privileges to roles. It does not affect the Time Travel retention period or the data availability3.
* Option C is incorrect because there is no 1 TB limit for data recovery in Snowflake. The data storage size does not affect the Time Travel retention period or the data availability4.
* Option E is incorrect because there is no ALLOW_RECOVERY privilege in Snowflake. The privilege required to perform Time Travel operations is SELECT, which allows querying historical data in tables5.
References: : Understanding & Using Time Travel : Transient Tables : Managed Access : Understanding Storage Cost : Table Privileges
NEW QUESTION # 229
How do you validate the data that is unloaded using COPY INTO command
Answer: B
NEW QUESTION # 230
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: C
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.
1: Considerations for Choosing Clustering for a Table | Snowflake Documentation
NEW QUESTION # 231
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