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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q30-Q35):

NEW QUESTION # 30
If a multi-cluster warehouse is resized, the new size applies to

A. All of the above
B. Clusters that are started after the warehouse is resized
C. Clusters that are currently running

Answer: A

NEW QUESTION # 31
An Architect is troubleshooting a query with poor performance using the QUERY_HIST0RY function. The Architect observes that the COMPILATIONJHME is greater than the EXECUTIONJTIME.
What is the reason for this?

A. The query is processing a very large dataset.
B. The query has overly complex logic.
C. The query is queued for execution.
D. The query is reading from remote storage.

Answer: B

Explanation:
Compilation time is the time it takes for the optimizer to create an optimal query plan for the efficient execution of the query. It also involves some pruning of partition files, making the query execution efficient2 If the compilation time is greater than the execution time, it means that the optimizer spent more time analyzing the query than actually running it. This could indicate that the query has overly complex logic, such as multiple joins, subqueries, aggregations, or expressions. The complexity of the query could also affect the size and quality of the query plan, which could impact the performance of the query3 To reduce the compilation time, the Architect can try to simplify the query logic, use views or common table expressions (CTEs) to break down the query into smaller parts, or use hints to guide the optimizer. The Architect can also use the EXPLAIN command to examine the query plan and identify potential bottlenecks or inefficiencies4 Reference:
1: SnowPro Advanced: Architect | Study Guide 5
2: Snowflake Documentation | Query Profile Overview 6
3: Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time 7
4: Snowflake Documentation | Optimizing Query Performance 8
: SnowPro Advanced: Architect | Study Guide
: Query Profile Overview
: Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time
: Optimizing Query Performance

NEW QUESTION # 32
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?

A. Ask the vendor to create a Snowflake account, load the data into Snowflake and create a data share.
B. Create an external stage on Google Cloud Storage and use the external table to load the data into Snowflake.
C. Copy the data from Google Cloud Storage to Azure Blob storage using external tools and load data from Blob storage to Snowflake.
D. Create a Snowflake Account in the Google Cloud Platform (GCP), ingest data into this account and use data replication to move the data from GCP to Azure.

Answer: B

NEW QUESTION # 33
Which steps are recommended best practices for prioritizing cluster keys in Snowflake? (Choose two.)

A. Choose lower cardinality columns to support clustering keys and cost effectiveness.
B. Choose cluster columns that are most actively used in selective filters.
C. Choose columns that are frequently used in join predicates.
D. Choose cluster columns that are actively used in the GROUP BY clauses.
E. Choose TIMESTAMP columns with nanoseconds for the highest number of unique rows.

Answer: B,C

Explanation:
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 # 34
Which copy options are not supported by CREATE PIPE...AS COPY FROM command?

A. VALIDATION_MODE = RETURN_n_ROWS | RETURN_ERRORS | RETURN_ALL_ERRORS
B. FILES = ( 'file_name1' [ , 'file_name2', ... ] )
C. ON_ERROR = ABORT_STATEMENT
D. FORCE = TRUE | FALSE
E. MATCH_BY_COLUMN_NAME = CASE_SENSITIVE | CASE_INSENSITIVE | NONE

Answer: A,B,C,D,E

NEW QUESTION # 35
......

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