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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowflake Architecture | 30% | - Data modeling and use cases - Object hierarchy and architectural impact - Data sharing architecture design - Development lifecycle and workload support - Data protection and recovery strategies |
| Topic 2: Performance Optimization | 20% | - Query performance tuning - Virtual warehouse configuration and sizing - Clustering and partitioning strategies - Cost optimization and resource management |
| Topic 3: Data Engineering | 25% | - Transformation and processing frameworks - Data pipeline and integration design - Data loading and unloading solutions |
| Topic 4: Accounts and Security | 25% | - Security principles and implementation scenarios - Account and database strategy design - Security, compliance and governance architecture |
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NEW QUESTION # 138
Which of the below query will you run to get the approximate number of distinct values in a table?
Answer: C
NEW QUESTION # 139
A company has a table with that has corrupted data, named Data. The company wants to recover the data as it was 5 minutes ago using cloning and Time Travel.
What command will accomplish this?
Answer: D
Explanation:
This is the correct command to create a clone of the table Data as it was 5 minutes ago using cloning and Time Travel. Cloning is a feature that allows creating a copy of a database, schema, table, or view without duplicating the data or metadata. Time Travel is a feature that enables accessing historical data (i.e. data that has been changed or deleted) at any point within a defined period. To create a clone of a table at a point in time in the past, the syntax is:
CREATE TABLE <clone_name> CLONE <source_table> AT (OFFSET => <offset_in_seconds>); The OFFSET parameter specifies the time difference in seconds from the present time. A negative value indicates a point in the past. For example, -60*5 means 5 minutes ago. Alternatively, the TIMESTAMP parameter can be used to specify an exact timestamp in the past. The clone will contain the data as it existed in the source table at the specified point in time12.
Reference:
Snowflake Documentation: Cloning Objects
Snowflake Documentation: Cloning Objects at a Point in Time in the Past
NEW QUESTION # 140
You will be using a multi cluster warehouse. You will statically control the available resources (i.e. servers) and you have large numbers of concurrent user sessions and/or queries and the numbers do not fluctuate significantly.
Which mode will you use for the warehouse?
Answer: A
NEW QUESTION # 141
You need to choose a high cardinality column for the clustering key
Answer: B
NEW QUESTION # 142
Which steps are recommended best practices for prioritizing cluster keys in Snowflake? (Choose two.)
Answer: A,B
Explanation:
According to the Snowflake documentation, the best practices for choosing clustering keys are:
* Choose columns that are frequently used in join predicates. This can improve the join performance by reducing the number of micro-partitions that need to be scanned and joined.
* Choose columns that are most actively used in selective filters. This can improve the scan efficiency by skipping micro-partitions that do not match the filter predicates.
* Avoid using low cardinality columns, such as gender or country, as clustering keys. This can result in poor clustering and high maintenance costs.
* Avoid using TIMESTAMP columns with nanoseconds, as they tend to have very high cardinality and low correlation with other columns. This can also result in poor clustering and high maintenance costs.
* Avoid using columns with duplicate values or NULLs, as they can cause skew in the clustering and reduce the benefits of pruning.
* Cluster on multiple columns if the queries use multiple filters or join predicates. This can increase the chances of pruning more micro-partitions and improve the compression ratio.
* Clustering is not always useful, especially for small or medium-sized tables, or tables that are not frequently queried or updated. Clustering can incur additional costs for initially clustering the data and maintaining the clustering over time.
References:
* Clustering Keys & Clustered Tables | Snowflake Documentation
* [Considerations for Choosing Clustering for a Table | Snowflake Documentation]
NEW QUESTION # 143
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