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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q62-Q67):

NEW QUESTION # 62
How do row access policies and Dynamic Data Masking impact the creation of dashboards in terms of data visibility and security?

Answer: A

Explanation:
Row access policies restrict data visibility based on user privileges, ensuring better security in dashboard creation.


NEW QUESTION # 63
You're working with a large dataset containing website user activity, including 'session_id', 'user_id', 'timestamp', and 'page_view'. You suspect bot activity is skewing your engagement metrics. Bots tend to have very short session durations and high page view counts within those sessions. Which of the following Snowflake SQL queries, used in combination, would be MOST effective in identifying and flagging potential bot sessions, considering performance on a large dataset? Assume you have access to Snowflake's statistical functions.

Answer: D

Explanation:
Option E is most effective because it identifies bot sessions based on two distinct criteria: unusually high page view counts AND unusually short session durations, which are characteristic of bots. By combining these two conditions with standard deviations, it reduces the likelihood of false positives. All other answer choices only check rate and not the count or session duration .


NEW QUESTION # 64
A telecommunications company wants to identify customers whose service addresses fall within a specific service area polygon defined as a Well-Known Text (WKT) string. The customer addresses are stored in a table 'CUSTOMER ADDRESSES' with a 'ADDRESS POINT column of type GEOGRAPHY. You have the WKT representation of the service area polygon stored in a variable '@service area_wkt'. Which of the following statements will correctly identify the customers within the service area? (Select all that apply)

Answer: C,D

Explanation:
TO_GEOGRAPHY(@service_area_wkt))' correctly identifies points that fall within the service area polygon. converts the WKT string into a GEOGRAPHY object. 'ST_COVERS(TO_GEOGRAPHY(@service_area_wkt), ADDRESS_POINT)' checks if the polygon covers the point.


NEW QUESTION # 65
A table named STUDENT is created:

What will be the output?

Answer: D

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 # 66
A data analyst is tasked with optimizing a daily ETL pipeline that loads data from several external sources into a Snowflake data warehouse. One of the key transformations involves joining two large tables, 'ORDERS' (millions of rows) and 'CUSTOMERS' (hundreds of thousands of rows), based on 'CUSTOMER ID. The pipeline currently uses a standard 'JOIN' operation, but the transformation step is taking longer than expected. The analyst has explored various optimization techniques, including increasing virtual warehouse size, but the performance improvement is minimal. Assuming that the 'CUSTOMER ID column is appropriately indexed (or clustered, if applicable) in both tables, and you want to minimise data movement. Which of the following approaches would yield the MOST significant performance improvement for this transformation step, considering metadata caching and data distribution?

Answer: A

Explanation:
Ensuring consistent data types and collations (D) is crucial for optimal join performance in Snowflake. When data types and collations match, Snowflake can leverage its internal optimizations more effectively, including metadata caching and efficient data access patterns. Using a 'BROADCAST JOIN' hint without considering data distribution (A) might lead to unnecessary data movement and performance degradation. 'MAP JOIN' is unavailable in Snowflake (B). Redistributing data using 'DISTRIBUTION_TYPE = HASH' (C) is generally less efficient than leveraging Snowflake's automatic optimizations. Pre-sorting data (E) is unnecessary in Snowflake as it does not guarantee data colocation in the same way that other distributed systems might.


NEW QUESTION # 67
......

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