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Home Snowflake SnowPro Advanced Data Engineer

Snowflake SnowPro Advanced Data Engineer Exam Practice Questions (P. 1)

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Question #1
A Data Engineer is investigating a query that is taking a long time to return. The Query Profile shows the following:

What step should the Engineer take to increase the query performance?
  1. A
    Add additional virtual warehouses.
  2. B
    Increase the size of the virtual warehouse.
  3. C
    Rewrite the query using Common Table Expressions (CTEs).
  4. D
    Change the order of the joins and start with smaller tables first.

Correct Answer:
B

Question #2
How can the following relational data be transformed into semi-structured data using the LEAST amount of operational overhead?
  1. A
    Use the TO_JSON function.
  2. B
    Use the PARSE_JSON function to produce a VARIANT value.
  3. C
    Use the OBJECT_CONSTRUCT function to return a Snowflake object.
  4. D
    Use the TO_VARIANT function to convert each of the relational columns to VARIANT.

Correct Answer:
C

Question #3
A Data Engineer executes a complex query and wants to make use of Snowflake’s query results caching capabilities to reuse the results.
Which conditions must be met? (Choose three.)
  1. A
    The results must be reused within 72 hours.
  2. B
    The query must be executed using the same virtual warehouse.
  3. C
    The USED_CACHED_RESULT parameter must be included in the query.
  4. D
    The table structure contributing to the query result cannot have changed.
  5. E
    The new query must have the same syntax as the previously executed query.
  6. F
    The micro-partitions cannot have changed due to changes to other data in the table.

Correct Answer:
EDF
chatbot-img GPT-4o - Answer
Snowflake's query results caching enables efficiencies by reusing previous query outcomes, but only under specific conditions to ensure data integrity and relevance. Importantly, the original query's structure and the micro-partitions involved must remain unaltered; any changes in these aspects invalidate the cached results. Moreover, the exact syntactic repetition of the query is crucial for the cache to recognize and deliver the previously computed results. This approach helps maintain both speed and consistency in data handling across various queries and sessions without confinement to a single virtual warehouse.

Question #4
A Data Engineer needs to load JSON output from some software into Snowflake using Snowpipe.
Which recommendations apply to this scenario? (Choose three.)
  1. A
    Load large files (1 GB or larger).
  2. B
    Ensure that data files are 100-250 MB (or larger) in size, compressed.
  3. C
    Load a single huge array containing multiple records into a single table row.
  4. D
    Verify each value of each unique element stores a single native data type (string or number).
  5. E
    Extract semi-structured data elements containing null values into relational columns before loading.
  6. F
    Create data files that are less than 100 MB and stage them in cloud storage at a sequence greater than once each minute.

Correct Answer:
BDE
chatbot-img GPT-4o - Answer
For optimal performance and cost-efficiency when using Snowpipe to load JSON into Snowflake, it's recommended to manage file sizes appropriately. Files should ideally be between 100-250 MB in size, compressed. This helps minimize overhead costs per file, making the process more cost-effective. Moreover, ensuring that each unique data element within the file carries a consistent data type is crucial for maintaining data integrity. Also, handle null values effectively by extracting them into relational columns beforehand, which helps in preserving the structure and quality of your data.

Question #5
Given the table SALES which has a clustering key of column CLOSED_DATE, which table function will return the average clustering depth for the SALES_REPRESENTATIVE column for the North American region?
  1. A
    select system$clustering_information('Sales', 'sales_representative', 'region = ''North America''');
  2. B
    select system$clustering_depth('Sales', 'sales_representative', 'region = ''North America''');
  3. C
    select system$clustering_depth('Sales', 'sales_representative') where region = 'North America';
  4. D
    select system$clustering_information('Sales', 'sales_representative') where region = 'North America’;

Correct Answer:
B
chatbot-img GPT-4o - Answer
When analyzing the clustering in Snowflake for a specific column within a table that has a condition, the function system$clustering_depth proves to be an efficient choice. It directly returns the average clustering depth linked to the specified column, here 'sales_representative', tailored to a specific filtering condition, 'region = ''North America'''. This function is designed to center its output strictly on the depth aspect, avoiding unnecessary details about other clustering statistics, making it both straightforward and focused for performance evaluation tasks.

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