New TestKingIT DAA-C01 Exam Questions Real DAA-C01 Dumps Updated on Aug 24, 2026 [Q23-Q39]

NO.23 When maintaining reports and dashboards, why is it crucial to configure subscriptions and updates?

They complicate dashboard management without any added benefits.

They limit data accessibility for effective dashboard usage.

Subscriptions and updates ensure timely information delivery.

Configuring these features hampers dashboard usability.

NO.24 What role does operationalizing data play in maintaining reports and dashboards for business requirements?

It limits the usability of reports by narrowing down access.

Operationalizing data ensures consistent and efficient usage.

Operationalizing data complicates dashboard management.

It restricts data updates, affecting dashboard accuracy.

NO.25 You observe that a Snowflake query, intended to perform aggregations on a ‘SALES table (partitioned by ‘SALE DATE), exhibits unexpectedly poor performance despite the data being relatively well clustered. Further investigation reveals that a user recently modified the ‘SESSION’ parameter NTE OUTPUT FORMAT to ‘YYYY-MM’. The aggregation query filters the ‘SALES’ table using a ‘WHERE clause on ‘SALE DATE. Which of the following explains the performance degradation, and what actions can be taken to remediate?

The change in alters the internal storage format of ‘SALE_DATE, invalidating existing clustering metadata. Re-clustering the ‘SALES’ table is required.

The modified causes Snowflake to perform implicit conversions on ‘SALE_DATE in the ‘WHERE clause, preventing partition pruning. Modify the query to use a consistent date format or reset the session parameter.

The change in increases the size of the query’s result set, leading to network bottlenecks. Reduce the number of columns returned by the query.

The parameter is irrelevant to query performance as it only affects the output representation of dates. The performance issue is due to a different factor, such as insufficient warehouse size.

The change in impacts the cost-based optimizer and impacts the explain plan, causing a full table scan, use ‘ALTER SESSION SET DATE OUTPUT FORMAT = ‘AUTO”.

NO.26 A Snowflake data warehouse contains a table ‘CUSTOMER TRANSACTIONS with columns ‘CUSTOMER ID, ‘TRANSACTION DATE’, ‘AMOUNT’, and ‘PRODUCT CATEGORY’. Analysts frequently run queries that aggregate transaction amounts by product category for specific customer segments. The following query pattern is common:

Which of the following strategies, when implemented together, would BEST optimize the performance of this query pattern, considering both result caching and data access patterns?

Create a view that encapsulates the ‘WHERE’ clause conditions (filtering by ‘CUSTOMER ID and ‘TRANSACTION DATE). Enable automatic query rewrite.

create a materialized view that pre-aggregates ‘SUM(AMOUNT)’ by ‘PRODUCT_CATEGORY, ‘CUSTOMER_ID , and ‘TRANSACTION_DATE. Regularly refresh the materialized view.

Tune the virtual warehouse size to be as small as possible while still meeting performance requirements. Ensure the statistics on the table are up to date.

Cluster the ‘CUSTOMER TRANSACTIONS’ table by ‘CUSTOMER and then ‘TRANSACTION DATE. Create a materialized view preaggregating ‘SUM(AMOUNT)’ by PRODUCT_CATEGORY’, and ‘ TRANSACTION_DATE

Create indexes on ‘CUSTOMER and ‘TRANSACTION DATE columns of the CUSTOMER TRANSACTIONS table.

NO.27 You are preparing to load a large dataset from Parquet files stored in an Azure Blob Storage container into Snowflake using Snowsight. The dataset contains personally identifiable information (PII) and you need to ensure that only authorized users can access this data,. You want to use Snowflake’s data masking policies to protect the PII. Which of the following options represents the correct sequence of steps and considerations for achieving this, specifically using Snowsight for the loading and initial policy application phases?

1. Create an Azure external stage pointing to the Blob Storage container. 2. Create a file format object specifying PARQUET as the file type. 3. Load the Parquet files into a staging table with all columns as VARCHAR using Snowsight. 4. Define masking policies on the staging table columns. 5. Create the final table and transfer data from staging, applying the masking policies on the target table as the data moves.

1. Create an Azure external stage pointing to the Blob Storage container. 2. Create a file format object specifying PARQUET as the file type. 3. Create the target table with the desired schema, including data types. 4. Load the Parquet files directly into the target table using Snowsight. 5. Define masking policies on the target table columns after loading.

1. Create an Azure external stage pointing to the Blob Storage container. 2. Create a file format object specifying PARQUET as the file type. 3. Define masking policies on the target table columns. 4. Create the target table with the desired schema, referring to masking policies. 5. Load the Parquet files directly into the target table using Snowsight.

1. Create an Azure external stage pointing to the Blob Storage container. 2. Load the Parquet files into a staging table with all columns as VARCHAR using Snowsight. 3. Create a file format object specifying PARQUET as the file type. 4. Define masking policies on the staging table columns. 5. Create the final table and transfer data from staging, applying the masking policies on the target table as the data moves.

1. Create an Azure external stage pointing to the Blob Storage container. 2. Create a file format object specifying PARQUET as the file type. 3. Create the target table with the desired schema, including data types. 4. Define masking policies on the target table columns. 5. Load the Parquet files directly into the target table using Snowsight.

NO.28 A company is looking for new headquarters and wants to minimize the distances employees have to commute.
The company has geographic data on employees’ residences. Through the Snowflake Marketplace, the company obtained geographic data for possible locations of the new headquarters. How can the distance between an employee’s residence and potential headquarters locations be calculated in meters with the LEAST operational overhead?

ST_HAUSDORFFDISTANCE

HAVERSINE

ST_LENGTH

ST_DISTANCE

NO.29 A Data Analyst has a Parquet file stored in an Amazon S3 staging area. Which query will copy the data from the staged Parquet file into separate columns in the target table?

Option A

Option B

Option C

Option D

NO.30 A Data Analyst created a SQL statement that updates a table used for reporting. The Analyst now wants to automate the execution of that SQL.
The Analyst decides to use a task for this along with a stream called MYSTREAM on the source table so only they can update the table if there is new data.
Which statement will create a task that will execute when there is new data in MYSTREAM without having to be executed manually?

To automate a data pipeline in Snowflake using Tasks and Streams, an analyst must understand the relationship between scheduling and conditional execution. A task is an object used to execute a single SQL statement (or a call to a stored procedure) on a recurring basis. A stream, on the other hand, tracks Change Data Capture (CDC) metadata for a table, indicating whether new rows have been added, updated, or deleted.
The core requirement of this question is to ensure the task runs automatically but only if there is new data. In Snowflake, a task requires a SCHEDULE parameter to run automatically without manual intervention. The SCHEDULE defines the frequency (e.g., ‘5 MINUTE’ or a CRON expression) at which the task “attempts” to run. However, to prevent wasting credits by running the task when no data has changed, the WHEN clause is used in conjunction with the system function SYSTEM$STREAM_HAS_DATA(‘stream_name’).
Evaluating the Options based on the exhibit (image_8c9a13.png):
Option A is incorrect because it lacks a SCHEDULE. Without a schedule, the task is considered “standalone” and will never execute unless manually triggered by an EXECUTE TASK command.
Option B is incorrect because it incorrectly attempts to place the SYSTEM$STREAM_HAS_DATA function within the SCHEDULE parameter. The schedule must be a time-based interval or a cron string.
Option C is incorrect because ‘STREAM’ is not a valid schedule interval.
Option D is the 100% correct answer. It provides a valid time-based SCHEDULE (‘2 minute’) which enables the task to heart-beat automatically. It then correctly utilizes the WHEN clause with SYSTEM$STREAM_HAS_DATA(‘MYSTREAM’). This configuration ensures that every two minutes, Snowflake checks the stream; if the stream contains data, the task executes the SQL; if the stream is empty, the task skips the execution, saving compute resources. This pattern is the industry standard for building efficient, automated Data Transformation pipelines in the Snowflake Data Cloud.

NO.31 You have a large dataset of IoT sensor readings stored in compressed JSON files within an AWS S3 bucket. Each JSON file contains an array of sensor readings with the following structure:
You need to load this data into a Snowflake table named ‘sensor data’ with columns ‘sensor id’, ‘timestamp’, ‘temperature’, and ‘humidity’. Which of the following Snowflake commands would be the MOST efficient and appropriate to ingest this data, assuming you have already created the table and a named stage pointing to the S3 bucket?

Option A

Option B

Option C

Option D

Option E

NO.32 When maintaining reports and dashboards, why is it crucial to build automated and repeatable tasks?

Automated tasks reduce manual efforts, ensuring consistency.

Automated tasks increase the complexity of dashboard management.

Repeatable tasks hinder data updates in dashboards.

They ensure inconsistency in reports and dashboards.

NO.33 How do exploratory ad-hoc analyses differ from routine analysis?

They involve querying known patterns without exploring further.

Ad-hoc analyses deviate from established routines, exploring patterns and anomalies in data.

Ad-hoc analyses focus on anomalies and established trends.

Ad-hoc analyses rely heavily on predefined queries.

NO.34 In what ways do materialized views enhance performance in data analysis compared to regular views?

Materialized views offer real-time updates, unlike regular views.

Regular views simplify complex data structures more effectively.

Materialized views provide precomputed snapshots, reducing query computation.

They restrict data accessibility for improved security.

NO.35 How do partitioning strategies impact query performance and data storage efficiency in Snowflake?

Partitioning improves query planning only

Enhances query performance and reduces storage requirements

Limits data access for specific user roles

Reduces query performance and increases storage requirements

NO.36 You are tasked with cleaning and transforming a dataset containing customer information in Snowflake. This dataset includes columns such as ‘CUSTOMER ID, ‘NAME’, ‘EMAIL’, ‘PHONE NUMBER’, and ‘ADDRESS’. Your goal is to implement several data quality rules: 1) Ensure all phone numbers are in a consistent ‘+1-XXX-XXX-XXXX’ format (where X is a digit). 2) Remove any leading or trailing whitespace from the ‘NAME and ‘ADDRESS’ columns. 3) Replace invalid email addresses (identified by failing a specific regex pattern) with NULL. Which combination of Snowflake SQL statements would efficiently and accurately address these requirements?

NO.37 A Data Analyst is working with a table that has 1 record per day, with sales information. Which window function would calculate a 7-day moving average of sales, where SALES_DATE represents the date column?

SUM(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 6 PRECEDING AND
CURRENT ROW)

SUM(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 7 PRECEDING AND
CURRENT ROW)

AVG(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 6 PRECEDING AND
CURRENT ROW)

AVG(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 7 PRECEDING AND
CURRENT ROW)

NO.38 In Snowflake, how does partition pruning contribute to optimizing query performance?

Impacts query planning but not execution

Limits data accessibility across warehouses

Filters unnecessary partitions during query execution

Increases query complexity and optimization

NO.39 You are analyzing customer order data in Snowflake and need to determine if there is a statistically significant correlation between the number of items in an order (‘ITEM COUNT) and the total order value CORDER VALUE’). You have a table named ‘ORDERS’ with columns ‘ORDER ID’, ‘ITEM COUNT’, and ‘ORDER VALUE’. Which of the following Snowflake functions or methods, used in combination, would be the MOST appropriate and statistically sound for calculating the correlation coefficient between these two variables, taking into account the need to handle potential NULL values appropriately?

Use the ‘CORR function directly on the ‘ITEM_COUNT and ‘ORDER_VALUE columns. No special handling is needed for NULL values as ‘CORR automatically ignores them.

First, replace NULL values in both ‘ITEM COUNT and ‘ORDER _ VALUE with 0 using ‘COALESCE, then apply the ‘CORR function.

Use ‘AVG’ and ‘STDDEV_POP functions to calculate the mean and standard deviation for both and Then, manually compute the correlation coefficient using these statistics.

Use ‘QUALIFY Clause with ‘CORR function to determine the coefficient values, and then apply statistical significance tests manually by calculating p-values.

Use a combination of ‘WHERE clause to filter out rows where either ‘ITEM COUNT or ‘ORDER VALUE is NULL, and then apply the ‘CORR function to the filtered data.

New TestKingIT DAA-C01 Exam Questions Real DAA-C01 Dumps Updated on Aug 24, 2026 [Q23-Q39]

Leave a Reply Cancel reply