SnowPro Advanced Data Engineer

Here you have the best Snowflake SnowPro Advanced Data Engineer practice exam questions

You have 65 total questions to study from
Each page has 5 questions, making a total of 13 pages
You can navigate through the pages using the buttons at the bottom
This questions were last updated on November 20, 2024

Question 1 of 65

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?

Add additional virtual warehouses.

Increase the size of the virtual warehouse.

Rewrite the query using Common Table Expressions (CTEs).

Change the order of the joins and start with smaller tables first.

Correct Answer: B

The query profile indicates that a significant amount of data (37.59 GB) is being spilled to local storage. This is a clear sign that the virtual warehouse does not have enough memory to handle the query efficiently. Spilling to disk is much slower than processing data in memory. To address this, the engineer should increase the size of the virtual warehouse, which will provide more memory and reduce or eliminate the need for disk spilling, thus improving query performance.

Question 2 of 65

How can the following relational data be transformed into semi-structured data using the LEAST amount of operational overhead?

Use the TO_JSON function.

Use the PARSE_JSON function to produce a VARIANT value.

Use the OBJECT_CONSTRUCT function to return a Snowflake object.

Use the TO_VARIANT function to convert each of the relational columns to VARIANT.

Correct Answer: C

To transform relational data into semi-structured data with the least operational overhead, the OBJECT_CONSTRUCT function in Snowflake is an efficient choice. This function constructs a JSON-like output by taking column names and their corresponding values from a table. This operation is simple and direct, avoiding the need for intermediate steps or conversions, making it the least overhead solution among the options provided.

Question 3 of 65

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.)

The results must be reused within 72 hours.

The query must be executed using the same virtual warehouse.

The USED_CACHED_RESULT parameter must be included in the query.

The table structure contributing to the query result cannot have changed.

The new query must have the same syntax as the previously executed query.

The micro-partitions cannot have changed due to changes to other data in the table.

Correct Answer: A, D, E

To take advantage of Snowflake's query results caching capabilities, the results must be reused within 72 hours. Additionally, the table structure contributing to the query result cannot have changed, and the new query must have the same syntax as the previously executed query. These conditions ensure that the cached results are valid and can be reused efficiently.

Question 4 of 65

A Data Engineer needs to load JSON output from some software into Snowflake using Snowpipe.

Which recommendations apply to this scenario? (Choose three.)

Load large files (1 GB or larger).

Ensure that data files are 100-250 MB (or larger) in size, compressed.

Load a single huge array containing multiple records into a single table row.

Verify each value of each unique element stores a single native data type (string or number).

Extract semi-structured data elements containing null values into relational columns before loading.

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: B, D, F

When loading JSON data into Snowflake using Snowpipe, there are specific recommendations to follow for optimal performance and cost efficiency. First, it is recommended to ensure that data files are 100-250 MB or larger in size when compressed, helping to balance performance and cost (option B). Additionally, verifying each value of each unique element stores a single native data type (string or number) ensures data consistency and integrity (option D). Lastly, creating data files less than 100 MB and staging them in cloud storage at a frequency greater than once each minute might seem counterintuitive, but it helps to keep the overhead manageable and avoids issues with latency and queue management in Snowpipe (option F). This combination of practices ensures effective and efficient use of Snowpipe for loading JSON data.

Question 5 of 65

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?

select system$clustering_information('Sales', 'sales_representative', 'region = ''North America''');

select system$clustering_depth('Sales', 'sales_representative', 'region = ''North America''');

select system$clustering_depth('Sales', 'sales_representative') where region = 'North America';

select system$clustering_information('Sales', 'sales_representative') where region = 'North America’;

Correct Answer: B

The table function system$clustering_depth is used to return the average clustering depth for a specified set of columns, with an additional optional filter condition. Since the question specifies the need to calculate the average clustering depth for the sales_representative column in the North American region, this function perfectly fits the requirement.