Vce DAA-C01 Questions Latest

In Snowflake, the performance of a metadata-based query (like MAX, MIN, or COUNT) is typically near-instantaneous because Snowflake maintains constant-time statistics in its Cloud Services layer. For a standard table, SELECT MAX(num) does not even require a virtual warehouse to be active; it simply reads the value from the table's metadata.

However, when a Row Access Policy (RAP) is applied to a table, the query's behavior changes fundamentally. A row access policy is a security feature that restricts which rows are visible to a user based on their role or other attributes. To enforce this policy, Snowflake can no longer rely on the high-level metadata of the entire table because it must first determine which specific rows the user is authorized to see. Consequently, the query engine must scan the individual micro-partitions and evaluate the policy logic for every row (or block of rows) to filter out unauthorized data before calculating the maximum value. This turns a "metadata-only" operation into a data-scanning operation, which requires a running warehouse and significantly more time.

Evaluating the Options:

Option A is incorrect because the prompt states the tables are "similarly-sized." Even if it were slightly larger, a metadata lookup for a standard table would still be nearly instant.

Option C is incorrect because a multi-cluster warehouse helps with concurrency (multiple users), not the raw execution speed of a single simple aggregate query.

Option D is incorrect because USE_CACHED_RESULT refers to the Query Result Cache. While turning it off would prevent a "0ms" response from a previous run, it wouldn't explain a "significant" slowdown compared to a standard metadata fetch.

Option B is the 100% correct answer. The presence of a Row Access Policy forces a full data scan and policy evaluation, which is the most common reason for performance degradation in otherwise simple metadata queries.

To determine the correct result of the query, a Data Analyst must evaluate the nested functions according to standard SQL order of operations while correctly identifying how Snowflake handles NULL values in aggregate calculations.

Step 1: Evaluating AVG(GRADE)

The AVG() function calculates the arithmetic mean of the non-null values in a column. Based on the provided table data, the grades are: 4, 1, 2, 1, and 2. The sixth row contains a NULL grade. In Snowflake, as per ANSI SQL standards, aggregate functions ignore NULL values entirely. They are not treated as zeros, nor do they cause the function to return NULL (unless the entire column is null).

Sum of non-null values: $4 + 1 + 2 + 1 + 2 = 10$

Count of non-null values: $5$

Result: $10 / 5 = 2.0$

Step 2: Evaluating CEIL(..., 2)

The CEIL (or CEILING) function is typically used to return the smallest integer value that is greater than or equal to the input. However, Snowflake's CEIL function also supports an optional second argument for scale (precision).

The input is the result from the previous step: 2.0.

The scale argument is 2.

The function CEIL(2.0, 2) attempts to round the value up to the nearest value with two decimal places. Since 2.0 is already a whole number and has no fractional component beyond the specified scale, the ceiling remains 2.0.

Evaluating the Options:

Option A is incorrect as it represents a massive scaling error.

Option C and D are incorrect because they result from mathematical errors or incorrectly including the NULL row in the denominator (e.g., $10 / 6 = 1.66$, then rounded up).

Option B is the 100% correct answer. It accurately reflects the result of the AVG function ($2.0$) followed by the CEIL operation, which preserves the value as it is already at the target ceiling. This question tests precision in both mathematical logic and the technical nuances of Snowflake-specific SQL functions.

In Snowsight, worksheets and dashboards are tightly integrated to facilitate a smooth transition from data exploration to formal reporting. When a Data Analyst has already done the "heavy lifting"—cleaning and preparing data within standalone worksheets—Snowflake provides a direct way to convert those worksheets into dashboard tiles.

The "least operational overhead" method is to Move the existing worksheets into the dashboard. When you are in the dashboard view, you can select the option to add a tile and then choose an existing worksheet from your folders. This "Move" or "Add from Worksheet" action preserves the entire SQL statement, any formatting applied to the results, and the visualization settings (charts) that were already configured in the worksheet.

Evaluating the Options:

Option B and C are technically possible but require significantly more effort (manual copy-pasting, switching windows, re-naming tiles). This increases the risk of human error and is not considered a high-efficiency workflow in Snowsight.

Option D is the most complex and overhead-intensive option, requiring the analyst to sift through system logs to recover code they already have access to in their UI.

Option A is the 100% correct answer. By moving or importing the worksheet directly into the dashboard as a tile, the analyst maintains the link between the development work and the final presentation with just a few clicks. Note that once a worksheet is moved into a dashboard, it essentially becomes a "tile" and is managed as part of the dashboard object.

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) $\rightarrow$ 2 rows.

Student 2: 2 tokens (ALICE, SMITH) $\rightarrow$ 2 rows.

Student 3: 2 tokens (BOB, JOHNSON) $\rightarrow$ 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.