Instant Access Snowflake.DAA-C01.v2026-08-26.q67 Actual Practice Test Engine for Free (Page 13)

Question 56

In what ways do materialized views enhance performance in data analysis compared to regular views?

A.Materialized views offer real-time updates, unlike regular views.
B.Regular views simplify complex data structures more effectively.
C.Materialized views provide precomputed snapshots, reducing query computation.
D.They restrict data accessibility for improved security.

Question 57

The following code is run:

Then this statement is executed:

What will be the output of this statement?

A.
B.
C.
D.

Correct Answer: C

To determine the correct output, a Data Analyst must understand the behavior of the ILIKE operator, wildcard characters, and the ESCAPE clause in Snowflake.
1. Pattern Matching with ILIKE: The ILIKE operator performs case-insensitive pattern matching. The provided pattern is 'p%^_j%'.
* p: The string must start with the letter 'P' or 'p'.
* %: This wildcard matches any sequence of zero or more characters.
* ^_: The ESCAPE '^' clause identifies the caret symbol as an escape character. This means the underscore (_) immediately following it is treated as a literal character rather than its usual wildcard function (which matches any single character).
* j: The letter 'J' or 'j' must follow the literal underscore.
* %: Matches any trailing sequence of characters.
2. Evaluating the Data: Applying this logic to the values in the cert_dem table:
* 'Peter*John': While it starts with 'P' and contains 'J', it lacks the required literal underscore. No match.
* 'Peter John': Contains a space instead of an underscore. No match.
* 'Peter_John': Starts with 'P', contains a literal underscore, and is followed by 'J'. Match.
* 'Peter_john': Because the operator is ILIKE (case-insensitive), the lowercase 'j' is accepted. Match.
* null: Comparisons with NULL using LIKE or ILIKE always return NULL (unknown), so it is excluded from the results.
3. Ordering the Results: The query includes ORDER BY 1, which sorts the results alphabetically based on the first column. Between 'Peter_John' and 'Peter_john', the standard sort order typically places uppercase before lowercase in most Snowflake collations.
Evaluating the Options (image_829269.png):
* Option A incorrectly excludes the case-insensitive match.
* Option B incorrectly identifies values that lack the literal underscore.
* Option D incorrectly includes values that do not meet the escaped underscore requirement.
* Option C is the 100% correct result set, displaying both versions of 'Peter_John' that contain the literal underscore.

Question 58

What will the following query return?
SELECT * FROM testtable SAMPLE BLOCK (0.012) REPEATABLE (99992);

A.A sample of a table in which each block of rows has a 1.2% probability of being included in the sample where repeated elements are allowed.
B.A sample of a table in which each block of rows has a 0.012% probability of being included in the sample, with the seed set to 99992.
C.A sample of a table in which each block of rows has a 1.2% probability of being included in the sample, with the seed set to 99992.
D.A sample containing 99992 records of a table in which each block of rows has a 0.012% probability of being included in the sample.

Question 59

You are loading data from a series of CSV files into Snowflake using Snowsight. The files have varying column orders and some files are missing certain columns. You need to ensure that all data is loaded into a consistent table schema, handling missing columns gracefully.
Which of the following strategies is MOST effective in Snowsight to achieve this?

A.Define a file format with 'SKIP_HEADER = 1 ' and load all CSV files into a single table with all possible columns defined as VARCHAR. After loading, use SQL queries with and to convert the data to the appropriate data types.
B.Create multiple file formats, one for each unique CSV file structure. Use Snowsight's 'Load Data' wizard to load each set of files with the corresponding file format. Use UNION ALL to combine the data from multiple tables into a single view.
C.Load the data into a staging table with a single VARIANT column. Then, use SQL with 'CASE statements and 'GET ' function to extract data from the VARIANT column into the target table with the desired schema.
D.Define a file format with 'SKIP HEADER = 1', "FIELD OPTIONALLY ENCLOSED BY = and "NULL _ IF = (", 'NULL')'. Create a single table with all columns defined and use Snowsight's 'Load Data' wizard to load the CSV files. Columns not present in a given CSV file will automatically be populated with NULL.
E.Pre-process the CSV files before loading using a scripting language (e.g., Python) to standardize the column order and add missing columns with NULL values. Then, load the pre-processed files into Snowflake using Snowsight.

Question 60

You are a data analyst for an e-commerce company. You need to create a dashboard visualizing sales performance. The dashboard requires two key features: 1) users should be able to filter the data by product category dynamically using a dropdown list. 2) The dashboard should efficiently handle large datasets (millions of rows) without performance degradation. Which Snowflake features and techniques would you use to achieve these requirements most effectively?

A.Use Snowflake's dynamic data masking to hide sensitive sales data based on user roles, and create a view with pre-aggregated data for each product category to improve dashboard performance.
B.Implement user-defined functions (IJDFs) in Python to perform custom filtering logic, and store the results in a temporary table for the dashboard to consume.
C.Utilize Snowflake's caching mechanisms and create materialized views to pre-compute aggregated data for the dashboard, along with using dashboard-level filtering widgets connected to the product category column.
D.Create a stream on the sales data and use a task to continuously update a summary table with aggregated data for each product category. Use the summary table as the data source for the dashboard.
E.Extract all data into a pandas DataFrame and create a Dash application for the front end, as Snowflake struggles to handle real-time filtering effectively.

Question 56

Question 57

Question 58

Question 59

Question 60

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