DAA-C01 Exam Actual Tests | New DAA-C01 Braindumps Questions

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q20-Q25):

NEW QUESTION # 20
A financial institution needs to collect stock ticker data for intraday trading analysis. The data source provides updates every second. They need to maintain a 5-minute rolling average of stock prices for each ticker. The system needs to be highly available and resilient to data source interruptions. Considering the need for near real-time analysis and potential data source instability, which combination of technologies and approaches would be MOST effective?

Answer: B

Explanation:
A stream processing framework like Kafka is ideal for handling high-velocity data streams. Kafka provides fault tolerance and the ability to perform real-time aggregations (rolling average with tumbling window). While Snowpipe can ingest the raw data quickly, calculating the rolling average on-demand (using a VIEW) may not meet the near real-time requirement and can be inefficient. A scheduled task might not be able to handle the volume and frequency of data. The key to answering this question is understanding the need for real-time aggregation AND resilience to potential data source outages, both of which Kafka elegantly addresses.


NEW QUESTION # 21
A data analyst accidentally dropped a crucial table, 'SALES DATA', containing historical sales information. The table was dropped 5 days ago. The data retention period for the Snowflake account is set to the default value. The analyst needs to recover the table with all its data'. What is the MOST efficient and reliable method to recover the 'SALES DATA" table in Snowflake?

Answer: D

Explanation:
The 'UNDROP TABLE command is the most efficient and direct method to recover a dropped table, as long as it's within the data retention period. Since the table was dropped 5 days ago and the default retention period is typically sufficient (can be up to 90 days in Enterprise Edition), 'UNDROP TABLE should work. While options B and E are valid uses of cloning and time travel, they involve creating a new table and are less direct. Requesting Snowflake support (C) is unnecessary for a simple table recovery. Option D will require data extraction and recreating the table structure, which is tedious and time-consuming compared to 'UNDROP'


NEW QUESTION # 22
How do secure views contribute to data analysis practices in terms of access control and data security?

Answer: C

Explanation:
Secure views enhance data security while allowing selective data access.


NEW QUESTION # 23
A table named STUDENT is created:

What will be the output?

Answer: B

Explanation:
To determine the correct output, a Data Analyst must understand the behavior of the STRTOK_SPLIT_TO_TABLE table function and how it interacts with different delimiters.
1. Functionality of STRTOK_SPLIT_TO_TABLE:
The STRTOK_SPLIT_TO_TABLE function tokenizes a string based on a set of specified delimiters. Unlike simple SPLIT functions that often look for a literal string as a delimiter, STRTOK treats the delimiter string as a set of individual characters. Any character present in the delimiter string will trigger a split. Crucially, it skips empty tokens-if multiple delimiters appear consecutively, it does not produce a row for the space between them.
2. Evaluating the Data and Delimiters ('*-'):
* 'JOHN*DOE': The * character is a delimiter. This results in two tokens: JOHN and DOE.
* 'ALICE-SMITH': The - character is a delimiter. This results in two tokens: ALICE and SMITH.
* 'BOB*-JOHNSON': Both * and - are delimiters. Because they appear consecutively (*-), the function splits at each but ignores the empty space between them. This results in two tokens: BOB and JOHNSON.
3. Analyzing the Resulting Rows:
The LATERAL join produces a row for every token generated. The columns SEQ (identifying the source row sequence) and INDEX (position of the token within that sequence) are used for sorting.
* Student 1: 2 tokens (JOHN, DOE) $ ightarrow$ 2 rows.
* Student 2: 2 tokens (ALICE, SMITH) $ ightarrow$ 2 rows.
* Student 3: 2 tokens (BOB, JOHNSON) $ ightarrow$ 2 rows.
Evaluating the Options (image_8233d8.png):
* Option A incorrectly only shows results for Student 3.
* Option C incorrectly implies Student 1 and 2 produce three tokens each.
* Option D incorrectly interprets the consecutive delimiters in Student 3's name as creating a different sequence count.
* Option B is the 100% correct result set. It accurately shows that each student's name is split into exactly two parts, properly handling the varying delimiters and the consecutive characters in the third record.


NEW QUESTION # 24
You are building a Snowsight dashboard to monitor the performance of various SQL queries. You have a table 'QUERY HISTORY with columns 'QUERY ID', 'START TIME, 'END TIME, 'USER NAME, 'DATABASE NAME, and 'EXECUTION_TIME (in seconds). You want to create a bar chart that shows the average execution time for each user, but only for queries executed against a specific database (e.g., 'SALES DB') within the last week. Furthermore, you need to allow users to filter the data by username via a Snowsight dashboard variable. What is the most efficient SQL query and Snowsight configuration to achieve this?

Answer: E

Explanation:
Option B provides the most efficient solution by using a Snowsight dashboard variable ('USERNAME) with a dropdown populated from the distinct usernames in the 'QUERY HISTORY table. This allows users to easily filter the data by selecting a username from the dropdown, and the SQL query directly incorporates the variable using '$USERNAME in the WHERE clause for optimal performance. Option A lacks the dynamic filtering capability. Option C will not work as 'HAVING' clause should use aggregate functions, and even if it worked is not efficient. Option D would be difficult to maintain and less performant. Option E introduces unnecessary complexity with a stored procedure for this specific requirement.


NEW QUESTION # 25
......

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