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| Section | Weight | Objectives |
|---|---|---|
| Accounts and Security | 25% | - Access Control and Authentication
|
| Data Engineering | 25% | - Data Loading and Pipelines
|
| Performance Optimization | 20% | - Query Performance
|
| Snowflake Architecture | 30% | - Platform Architecture Design
|
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NEW QUESTION # 58
An Architect uses COPY INTO with the ON_ERROR=SKIP_FILE option to bulk load CSV files into a table called TABLEA, using its table stage. One file named file5.csv fails to load. The Architect fixes the file and re-loads it to the stage with the exact same file name it had previously.
Which commands should the Architect use to load only file5.csv file from the stage? (Choose two.)
Answer: B,D
Explanation:
* Option A (RETURN_FAILED_ONLY) will only load files that previously failed to load. Since file5.csv already exists in the stage with the same name, it will not be considered a new file and will not be loaded.
* Option D (FORCE) will overwrite any existing data in the table. This is not desired as we only want to load the data from file5.csv.
* Option E (NEW_FILES_ONLY) will only load files that have been added to the stage since the last COPY command. This will not work because file5.csv was already in the stage before it was fixed.
* Option F (MERGE) is used to merge data from a stage into an existing table, creating new rows for any data not already present. This is not needed in this case as we simply want to load the data from file5.csv.
Therefore, the architect can use either COPY INTO tablea FROM @%tablea or COPY INTO tablea FROM
@%tablea FILES = ('file5.csv') to load only file5.csv from the stage. Both options will load the data from the specified file without overwriting any existing data or requiring additional configuration
NEW QUESTION # 59
When loading data from stage using COPY INTO, what options can you specify for the ON_ERROR clause?
Answer: B,C,D
Explanation:
Explanation
* The ON_ERROR clause is an optional parameter for the COPY INTO command that specifies the behavior of the command when it encounters errors in the files. The ON_ERROR clause can have one of the following values1:
* CONTINUE: This value instructs the command to continue loading the file and return an error message for a maximum of one error encountered per data file. The difference between the ROWS_PARSED and ROWS_LOADED column values represents the number of rows that include detected errors. To view all errors in the data files, use the VALIDATION_MODE parameter or query the VALIDATE function1.
* SKIP_FILE: This value instructs the command to skip the file when it encounters a data error on any of the records in the file. The command moves on to the next file in the stage and continues loading. The skipped file is not loaded and no error message is returned for the file1.
* ABORT_STATEMENT: This value instructs the command to stop loading data when the first error is encountered. The command returns an error message for the file and aborts the load operation. This is the default value for the ON_ERROR clause1.
* Therefore, options A, B, and C are correct.
References: : COPY INTO <table>
NEW QUESTION # 60
An Architect plans to stream data using the Snowflake Connector for Kafka in Snowpipe.
What setting will optimize costs?
Answer: B
Explanation:
When using the Snowflake Connector for Kafka with Snowpipe, cost efficiency depends on batching data into appropriately sized files before ingestion. Snowflake charges Snowpipe costs based on the number of files ingested and compute used for loading. Very small files significantly increase overhead and cost.
Setting buffer.size.bytes = 10 MB allows the connector to batch records into reasonably sized files before flushing them to Snowflake (Answer C). This strikes a balance between ingestion latency and cost efficiency and aligns with Snowflake best practices for streaming ingestion. Extremely small buffer sizes or very frequent flush intervals (such as 1 record or 1 second) lead to excessive file creation and higher Snowpipe costs.
Maximizing micro-partitions is not configurable directly and is counterproductive for cost and performance.
For SnowPro Architect candidates, this question emphasizes the importance of batching and file sizing strategies when designing streaming ingestion pipelines with Kafka and Snowpipe.
=========
NEW QUESTION # 61
What will the below query return
SELECT TOP 10 GRADES FROM STUDENT;
Answer: A
NEW QUESTION # 62
A query shows a high number of bytes spilled to remote storage. The query includes a WHERE clause that filters out a large percentage of rows.
What actions can reduce spillage and improve performance? (Select TWO).
Answer: B,E
Explanation:
Bytes spilled to remote storage indicate memory pressure during query execution. Increasing the warehouse size provides more memory and compute resources, reducing the likelihood of spilling (Answer A).
Additionally, defining clustering keys on highly selective filter columns improves micro-partition pruning, which reduces the volume of data scanned and processed in memory (Answer C). Together, these actions address both the root cause (too much data scanned) and the symptom (insufficient memory).
Increasing clusters improves concurrency, not per-query memory. Primary and foreign keys are informational only in Snowflake and do not affect execution plans. Retention settings have no impact on query execution.
This question reinforces SnowPro Architect performance diagnostics: understand both physical execution (memory, spill) and logical data access (pruning).
NEW QUESTION # 63
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