Snowflake DEA-C01 SnowPro Advanced: Data Engineer Certification Exam Practice Test

Page: 1 / 14
Total 65 questions
Question 1

A secure function returns data coming through an inbound share

What will happen if a Data Engineer tries to assign usage privileges on this function to an outbound share?



Answer : A

An error will be returned because the Engineer cannot share data that has already been shared. A secure function is a Snowflake function that can access data from an inbound share, which is a share that is created by another account and consumed by the current account. A secure function can only be shared with an inbound share, not an outbound share, which is a share that is created by the current account and shared with other accounts. This is to prevent data leakage or unauthorized access to the data from the inbound share.


Question 2

What is a characteristic of the use of external tokenization?



Answer : D

External tokenization is a feature in Snowflake that allows users to replace sensitive data values with tokens that are generated and managed by an external service. External tokenization allows the preservation of analytical values after de-identification, such as preserving the format, length, or range of the original values. This way, users can perform analytics on the tokenized data without compromising the security or privacy of the sensitive data.


Question 3

Which functions will compute a 'fingerprint' over an entire table, query result, or window to quickly detect changes to table contents or query results? (Select TWO).



Answer : B, C

The functions that will compute a 'fingerprint' over an entire table, query result, or window to quickly detect changes to table contents or query results are:

HASH_AGG(*): This function computes a hash value over all columns and rows in a table, query result, or window. The function returns a single value for each group defined by a GROUP BY clause, or a single value for the entire input if no GROUP BY clause is specified.

HASH_AGG(<expr>, <expr>): This function computes a hash value over two expressions in a table, query result, or window. The function returns a single value for each group defined by a GROUP BY clause, or a single value for the entire input if no GROUP BY clause is specified. The other functions are not correct because:

HASH (*): This function computes a hash value over all columns in a single row. The function returns one value per row, not one value per table, query result, or window.

HASH_AGG_COMPARE (): This function compares two hash values computed by HASH_AGG() over two tables or query results and returns true if they are equal or false if they are different. The function does not compute a hash value itself, but rather compares two existing hash values.

HASH COMPARE(): This function compares two hash values computed by HASH() over two rows and returns true if they are equal or false if they are different. The function does not compute a hash value itself, but rather compares two existing hash values.


Question 4

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

Which recommendations apply to this scenario? (Select THREE)



Answer : B, D, F

The recommendations that apply to this scenario are:

Ensure that data files are 100-250 MB (or larger) in size compressed: This recommendation will improve Snowpipe performance by reducing the number of files that need to be loaded and increasing the parallelism of loading. Smaller files can cause performance degradation or errors due to excessive metadata operations or network latency.

Verify each value of each unique element stores a single native data type (string or number): This recommendation will improve Snowpipe performance by avoiding data type conversions or errors when loading JSON data into variant columns. Snowflake supports two native data types for JSON elements: string and number. If an element has mixed data types across different files or records, such as string and boolean, Snowflake will either convert them to string or raise an error, depending on the FILE_FORMAT option.

Create data files that are less than 100 MB and stage them in cloud storage at a sequence greater than once each minute: This recommendation will minimize Snowpipe costs by reducing the number of notifications that need to be sent to Snowpipe for auto-ingestion. Snowpipe charges for notifications based on the number of files per notification and the frequency of notifications. By creating smaller files and staging them at a lower frequency, fewer notifications will be needed.


Question 5

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



Answer : C

This option is the best way to transform relational data into semi-structured data using the least amount of operational overhead. The OBJECT_CONSTRUCT function takes a variable number of key-value pairs as arguments and returns a Snowflake object, which is a variant type that can store JSON data. The function can be used to convert each row of relational data into a JSON object with the column names as keys and the column values as values.


Question 6

A Data Engineer has created table t1 with datatype VARIANT:

create or replace table t1 (cl variant);

The Engineer has loaded the following JSON data set. which has information about 4 laptop models into the table:

The Engineer now wants to query that data set so that results are shown as normal structured dat

a. The result should be 4 rows and 4 columns without the double quotes surrounding the data elements in the JSON data.

The result should be similar to the use case where the data was selected from a normal relational table z2 where t2 has string data type columns model__id. model, manufacturer, and =iccisi_r.an=. and is queried with the SQL clause select * from t2;

Which select command will produce the correct results?

A)

B)

C)

D)



Answer : B


Question 7

Assuming that the session parameter USE_CACHED_RESULT is set to false, what are characteristics of Snowflake virtual warehouses in terms of the use of Snowpark?



Answer : A

Creating a DataFrame from a table will start a virtual warehouse because it requires reading data from Snowflake. The other options will not start a virtual warehouse because they either operate on local data or use an existing session to query Snowflake.


Page:    1 / 14   
Total 65 questions