Snowflake SnowPro Advanced Data Engineer Exam Dumps 2024
Snowflake SnowPro Advanced Data Engineer Exam Dumps 2024
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Question 1:
In your SQL Query you have the below predicate LIKE ‘%SERCH%IS%OPTIMIZED%‘For the above predicate, the search optimization service can optimize this query, using search access paths to search for the substrings? (Select all that apply)
A. SEARCH
B. OPTIMIZED
C. IS
D. All of them
Answer: A, B
Explanation:
The search optimization service can improve performance when searching for substrings that are 5 or more characters long.Search access paths are not used for IS because the substring is shorter than 5 characters.
Question 2:
Search Optimization works with Secured Data Sharing
A. TRUE
B. FALSE
Answer: A
Explanation:
TRUE. Here‘s why:
Compatibility with Secured Data Sharing:
Snowflake‘s Secured Data Sharing allows sharing data and objects (including tables) securely across accounts and organizations.
Search Optimization, implemented through Search Access Paths (SAPs), operates on the underlying data structure, not directly on the shared object.
This means that as long as the shared table structure and data remain consistent, Search Optimization applied to the original table will also effectively work for the shared data within the recipient‘s account.
Considerations:
While Search Optimization itself works with Secured Data Sharing, there are a few things to keep in mind:
The recipient account needs to have sufficient privileges to access and utilize the SAPs associated with the shared table.
Any potential performance benefits of Search Optimization might be affected by factors like network latency and data movement between accounts.
Overall:
Search Optimization offers its performance advantages even when applied to shared data through Secured Data Sharing. However, some additional considerations regarding access and network factors might come into play.
Question 3:
Select the statements that are TRUE regarding Search Optimization Service and Database Replication
A. If a table in the primary database has the SEARCH OPTIMIZATION property enabled, the property is replicated to the corresponding table in the secondary database.
B. Search access paths in the secondary database are replicated.
C. If a table in the primary database has the SEARCH OPTIMIZATION property enabled, the property is NOT replicated to the corresponding table in the secondary database.
D. Search access paths in the secondary database are not replicated but are instead rebuilt automatically.
Answer: A, D
Explanation:
If a table in the primary database has the SEARCH OPTIMIZATION property enabled, the property is replicated to the corresponding table in the secondary database. Search access paths in the secondary database are not replicated but are instead rebuilt automatically
Question 4:
You have a table named MY_TABLE and you have added EQUALITY search optimization on the column DOB_TXT ( which is a varchar field). If you execute the below SQL will the query use Search Optimization Service? SELECT * FROM MY_TABLE WHERE TO_DATE(DOB_TXT) = ‘1999-01-01‘;
A. Yes. Since you have added search optimization on EQUALITY for the column DOB_TXT
B. No. Search Optimization does not work on VARCHAR fields
C. No. Search Optimization doesn‘t support casting on values in the table column.
Answer: C
Explanation:
Although search optimization supports predicates with implicit and explicit casts on constant values, it does not support predicates that cast values in the actual table column (except for casts from INTEGER and NUMBER to VARCHAR).In this case explicit casting is done on table column DOB_TXT which is a varchar field
Question 5:
Search Optimization can be added to External tables and Materialized views.
A. TRUE
B. FALSE
Answer: B
Explanation:
FALSE. Here‘s why:
Search Optimization and Applicable Objects:
Search Optimization service functions by building and leveraging additional persistent storage structures called Search Access Paths (SAPs) alongside the original data. These SAPs can take the form of materialized views or clustered tables depending on the chosen optimization technique.
External Tables: These tables reside outside Snowflake, pointing to data stored in external clouds like AWS S3, Azure Blob Storage, or Google Cloud Storage. Snowflake cannot directly create SAPs within these external systems, making Search Optimization unavailable for external tables.
Materialized Views: As pre-computed data structures within Snowflake, materialized views can benefit from Search Optimization. Specific materialized views can be designated as SAPs, enabling optimized data access for queries targeting the materialized view data.
Therefore, while Search Optimization can not be directly applied to external tables due to their external location, it can be implemented on materialized views to enhance query performance for relevant queries.
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Question 6:
In your query you have the following condition – WHERE AND . Select the statement which is TRUE regarding Search Optimization service for this condition.
A. Search optimization can improve performance if each condition separately returns a few rows
B. Search optimization can improve performance if either condition separately returns a few rows
Answer: B
Explanation:
For queries that use conjunctions of predicates (i.e., AND), query performance can be improved by search optimization if any of the predicates adhere to the conditions above. For example, suppose that a query has: where condition_x and condition_y Search optimization can improve performance if either condition separately returns a few rows (i.e., condition_x returns a few rows or condition_y returns a few rows). If condition_x returns a few rows but condition_y returns many rows, the query performance can benefit from search optimization.
Question 7:
As a Data Architect you have designed a Datawarehouse based on Dimensional modeling ( Dimensions and Fact tables). To enable the search optimization service to improve the performance of joins between the dimension and fact table where do you add the search optimization?
A. Fact table
B. Dimension table
C. Create a new table with required data and add it there
D. Search Optimization Service can‘t improve the JOIN performance
Answer: A
Explanation:
The search optimization service does not directly improve the performance of joins. However, it can improve the performance of filtering rows from either table prior to the join, if the table has search optimization enabled and the predicate is selective.Snowflake recommends to add search optimization to larger table ( in this case Fact table) to improve the performance of JOIN .
Question 8:
Search optimization works best to improve the performance of a query in which at least one of the columns accessed through the query filter operation has at least —– distinct values
A. 1000-2000
B. 5000-10000
C. 100,000 - 200,000
D. 1,000,000-2,000,000
Answer: C
Explanation:
While there‘s no definitive threshold, the most accurate answer for when search optimization works best is C. 100,000 – 200,000 distinct values. Here‘s why:
Search optimization relies on pre-computing data structures like materialized views or clustered tables based on specific columns and query patterns. This pre-processing can be beneficial, but it also introduces additional overhead in terms of building and maintaining these structures. Therefore, the ideal scenario for using search optimization involves:
High Cardinality: Columns with a significant number of distinct values (ideally, in the range of 100,000 to 200,000) benefit greatly from search optimization. This allows the pre-computed structures to effectively filter and segment the data, significantly reducing the amount of data processed during query execution.
Frequently Used Filters: Search optimization shines when queries rely on filtering on the same high-cardinality columns repeatedly. This maximizes the value of the pre-computed structures, as they can be readily utilized for multiple queries.
Lower cardinality (less than 100,000 distinct values):
The benefits of search optimization might be less significant. Filtering on a small number of distinct values might not benefit as much from pre-computed structures compared to directly accessing the original data.
Higher cardinality (beyond 200,000 distinct values):
The effectiveness of search optimization can vary based on the specific query and chosen optimization technique. It‘s crucial to carefully analyze the cost-benefit trade-off, as building and maintaining complex structures for extremely high cardinality might not always be worthwhile.
Question 9:
Snowflake provides several ways to optimize query performance. Select all the correct answers
A. Clustering a table
B. Query Acceleration
C. Materialized Views
D. Search Optimization Service
E. All of the above
F. None
Answer: E
Explanation:
All the valid options for optimizing query performance in Snowflake:
Clustering a table: Reorganizing data based on frequently used query patterns can significantly improve query execution speed by minimizing data movement and facilitating faster access to relevant data segments.
Query Acceleration: This service offloads portions of complex queries to dedicated shared compute resources, potentially reducing query execution time for eligible queries.
Materialized Views: Pre-computed data structures based on frequently used queries can significantly reduce query execution times by providing readily available results instead of recomputing them each time the query is run.
Search Optimization Service: This service automatically identifies and implements Search Access Paths, optimized data structures like clustered tables or materialized views, to accelerate specific queries.
Question 10:
When you use Search Optimization Service , you will end up with
A. Only Compute Costs
B. Only Storage Costs
C. Both Compute and Storage Costs
D. No additional costs since it is a metadata operation
Answer: C
Explanation:
When you use the Search Optimization Service in Snowflake, you‘ll incur Both Compute and Storage Costs. Here‘s why:
Compute Costs:
Building and maintaining Search Access Paths (SAPs) involves processing data and creating additional data structures. This requires Snowflake to allocate compute resources, resulting in compute costs.
Queries utilizing SAPs might still require some processing, though likely significantly less compared to queries accessing the original data directly. This also translates to compute costs.
Storage Costs:
SAPs themselves represent additional persistent storage structures alongside the original data. This increases the overall storage footprint and leads to storage costs.
Depending on the specific optimization technique used, the storage footprint of SAPs can vary. For example, materialized views often store pre-computed data which can be larger than the original table, while clustered tables might have a similar or even slightly smaller footprint.
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SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
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