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| Section | Objectives |
|---|---|
| Topic 1: Performance and Optimization | - Cost optimization
|
| Topic 2: Data Sharing and Collaboration | - Secure data sharing
|
| Topic 3: Data Pipelines and Data Engineering | - Data orchestration
|
| Topic 4: Data Architecture and Design | - Data modeling and schema design
|
| Topic 5: Security, Governance, and Compliance | - Data governance
|
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NEW QUESTION # 90
Which SQL alter command will MAXIMIZE memory and compute resources for a Snowpark stored procedure when executed on the snowpark_opt_wh warehouse?
Answer: B
Explanation:
To maximize memory and compute resources for a Snowpark stored procedure, you need to set the MAX_CONCURRENCY_LEVEL parameter for the warehouse that executes the stored procedure. This parameter determines the maximum number of concurrent queries that can run on a single warehouse. By setting it to 16, you ensure that the warehouse can use all the available CPU cores and memory on a single node, which is the optimal configuration for Snowpark-optimized warehouses. This will improve the performance and efficiency of the stored procedure, as it will not have to share resources with other queries or nodes. The other options are incorrect because they either do not change the MAX_CONCURRENCY_LEVEL parameter, or they set it to a lower value than 16, which will reduce the memory and compute resources for the stored procedure. Reference:
[Snowpark-optimized Warehouses] 1
[Training Machine Learning Models with Snowpark Python] 2
[Snowflake Shorts: Snowpark Optimized Warehouses] 3
NEW QUESTION # 91
The following DDL command was used to create a task based on a stream:
Assuming MY_WH is set to auto_suspend - 60 and used exclusively for this task, which statement is true?
Answer: B
NEW QUESTION # 92
Database DB1 has schema S1 which has one table, T1.
DB1 --> S1 --> T1
The retention period of EG1 is set to 10 days.
The retention period of s: is set to 20 days.
The retention period of t: Is set to 30 days.
The user runs the following command:
Drop Database DB1;
What will the Time Travel retention period be for T1?
Answer: C
Explanation:
The Time Travel retention period for T1 will be 30 days, which is the retention period set at the table level.
The Time Travel retention period determines how long the historical data is preserved and accessible for an object after it is modified or dropped. The Time Travel retention period can be set at the account level, the database level, the schema level, or the table level. The retention period set at the lowest level of the hierarchy takes precedence over the higher levels. Therefore, the retention period set at the table level overrides the retention periods set at the schema level, the database level, or the account level. When the user drops the database DB1, the table T1 is also dropped, but the historical data is still preserved for 30 days, which is the retention period set at the table level. The user can use the UNDROP command to restore the table T1 within the 30-day period. The other options are incorrect because:
* 10 days is the retention period set at the database level, which is overridden by the table level.
* 20 days is the retention period set at the schema level, which is also overridden by the table level.
* 37 days is not a valid option, as it is not the retention period set at any level.
Understanding & Using Time Travel
AT | BEFORE
Snowflake Time Travel & Fail-safe
NEW QUESTION # 93
What built-in Snowflake features make use of the change tracking metadata for a table? (Choose two.)
Answer: A,D
Explanation:
In Snowflake, the change tracking metadata for a table is utilized by the MERGE command and the STREAM object. The MERGE command uses change tracking to determine how to apply updates and inserts efficiently based on differences between source and target tables. STREAM objects, on the other hand, specifically capture and store change data, enabling incremental processing based on changes made to a table since the last stream offset was committed.References: Snowflake Documentation on MERGE and STREAM Objects.
NEW QUESTION # 94
What step will im the performance of queries executed against an external table?
Answer: C
Explanation:
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 tables5 Reference:
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
6: SnowPro Advanced: Architect | Study Guide
7: Partitioning External Tables
8: Creating External Tables
9: Supported File Formats and Compression for Staged Data Files
10: Overview of Stages
NEW QUESTION # 95
......
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