USE REAL DAA-C01 DUMPS [2025] GUARANTEED SUCCESS

Use Real DAA-C01 Dumps [2025] Guaranteed Success

Use Real DAA-C01 Dumps [2025] Guaranteed Success

Blog Article

Tags: DAA-C01 Practice Questions, DAA-C01 Cheap Dumps, New DAA-C01 Test Discount, Download DAA-C01 Free Dumps, New DAA-C01 Test Cost

It is not hard to find that there are many different kinds of products in the education market now. It may be difficult for users to determine the best way to fit in the complex choices. We can tell you with confidence that the DAA-C01 study materials are superior in all respects to similar products. First, users can have a free trial of DAA-C01 Learning Materials, to help users better understand the DAA-C01 study materials. If the user discovers that the product is not appropriate for him, the user can choose another type of learning material.

This is a Snowflake DAA-C01 practice exam software for Windows computers. This DAA-C01 practice test will be similar to the actual SnowPro Advanced: Data Analyst Certification Exam (DAA-C01) exam. If user wish to test the Snowflake DAA-C01 study material before joining FreePdfDump, they may do so with a free sample trial. This DAA-C01 Exam simulation software can be readily installed on Windows-based computers and laptops. Since it is desktop-based Snowflake DAA-C01 practice exam software, it is not necessary to connect to the internet to use it.

>> DAA-C01 Practice Questions <<

DAA-C01 Cheap Dumps | New DAA-C01 Test Discount

Our DAA-C01 training quiz is the top selling products in the market. You will save a lot of preparation troubles if you purchase our DAA-C01 study materials. Our DAA-C01 exam braindumps are highly similar to the real test. Almost all questions of the real exam will be predicated accurately in our DAA-C01 Practice Questions, which can add you passing rate of the exam. And you will find that our prices for the exam products are quite favorable.

Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q68-Q73):

NEW QUESTION # 68
You're developing a data quality process in Snowflake that relies on identifying duplicate records within a large table named 'TRANSACTIONS. You need to generate a hash value for each row based on several key columns ('transaction_id', 'customer_id' , amount', to efficiently compare rows and detect duplicates. However, some of these columns may contain NULL values, which you want to handle consistently during the hash generation. Which of the following approaches, utilizing Snowflake's system functions, will MOST reliably generate a consistent hash value for duplicate rows, even when some of the key columns contain NULLs? (Select TWO)

  • A. Use the 'SHA2(CONCAT(NVL(transaction_id, 'I), NVL(customer_id, "), NVL(amount, "), NVL(transaction_date, "))) function, replacing NULLs with empty strings using the NVL function before concatenation.
  • B. Use the customer_id, amount, transaction_date)' function directly, as Snowflake automatically handles NULLs in hashing functions.
  • C. Use the WS('I', transaction_id, customer_id, amount, function. This concatenates strings with a separator, handling NULLs implicitly by skipping them in concatenation, leading to inconsistencies.
  • D. Use the II customer_id II amount II transaction_date)' function. Snowflake implicitly converts NULL to a default value during string concatenation.
  • E. Use the AS VARCHAR), AS VARCHAR), AS VARCHAR), NVL(CAST(transaction_date AS VARCHAR), function, explicitly converting each value to a string and replacing NULLs with empty strings using NVL.

Answer: A,E

Explanation:
Options B and E are the most reliable. Option B concatenates the value of the columns as a string to create a seed for SHA2, ensuring to convert the NULL to empty string, which is necessary so that SHA2 does not return NULL in the face of NULL column values. Option E also uses SHA2 to encrypt after concatenating all the column values but it casts all those columns to varchar, which is necessary for the data preparation and data ingestion as they might be of different datatype. The first option is wrong because Snowflake's HASH function automatically returns NULL if any of the input are NULL. Option C uses the 'II' operator to concatenate values and Snowflake will return NULL in case any value is null. Option D concatenates strings with a separator, handling NULLs implicitly by skipping them in concatenation, leading to inconsistencies


NEW QUESTION # 69
A telecommunications company wants to segment its customers based on their usage patterns for targeted marketing campaigns. You have access to a table named 'CUSTOMER USAGE with the following columns: 'CUSTOMER ONT), 'DATA USAGE GB' (FLOAT), 'VOICE CALL MINUTES (INT), and (INT). Which of the following Snowflake features or techniques would be MOST appropriate for performing customer segmentation and determining distinct customer clusters?

  • A. Using Snowflake's built-in 'QUALIFY' clause combined with window functions to rank customers based on individual usage metrics and categorize them based on rank percentiles.
  • B. Creating a series of complex SQL queries with multiple 'CASE statements to manually define customer segments based on predefined thresholds for data usage, voice calls, and SMS count.
  • C. Using the 'APPROX COUNT DISTINCT function to estimate the number of distinct usage patterns without performing actual clustering.
  • D. Implementing a K-Means clustering algorithm using Snowflake's Python User-Defined Functions (UDFs) and storing the cluster assignments in a new column within the 'CUSTOMER USAGE table.
  • E. Utilizing Snowflake's external functions to call a machine learning model hosted on a platform like AWS SageMaker or Azure Machine Learning to perform the clustering and return the segment assignments.

Answer: D,E

Explanation:
Options B and E are the most appropriate. Option B leverages Snowflake's UDF capabilities for in-database processing, allowing for potentially complex custom clustering algorithms. Option E allows integration with external machine learning platforms to take advantage of pre- built, optimized machine learning models. Option A is not appropriate because it just provides a count of distinct patterns, not the clustering itself. Option C is not scalable or maintainable for complex segmentation. Option D provides ranking, but not clustering or segmentation in the sense intended by the question.


NEW QUESTION # 70
You are tasked with creating a data access strategy for a marketing analytics team. They need access to customer purchase data, but only aggregated by region and product category. They should not be able to see individual customer details due to PII compliance. You decide to use a Secure View. Which of the following are the MOST appropriate steps to ensure data security and minimize performance impact?

  • A. Create a Secure View directly on the base tables with the aggregation logic. Grant SELECT privilege on the view to the marketing analytics role.
  • B. Create a Materialized View that aggregates the data. Create a Secure View on top of the Materialized View and grant SELECT privilege on the secure view to the marketing analytics role.
  • C. Create a Secure View that aggregates the data and grant SELECT privilege on the view to the marketing analytics role.
  • D. Create a regular view that aggregates the data and grant SELECT privilege on the view to the marketing analytics role.
  • E. Create a Materialized View directly on the base tables with the aggregation logic. Grant SELECT privilege on the Materialized View to the marketing analytics role.

Answer: C

Explanation:
Secure Views prevent access to the underlying tables and data lineage. Creating the Secure View directly with the aggregation prevents unauthorized access to detailed data and avoids exposing intermediate tables or views. Materialized views while improving performance, when used along with a Secure View adds unnecessary complexity as aggregation should happen in secure view only.


NEW QUESTION # 71
A company stores sensor data, including timestamps (ts), sensor ID (sensor_id), and readings (reading_value), in a Snowflake table named 'sensor_data'. Due to sensor malfunctions, some readings are significantly higher or lower than expected (outliers). Which of the following approaches are suitable in Snowflake to calculate the average reading value for each sensor, EXCLUDING readings that fall outside of two standard deviations from the mean for that sensor?

  • A. Using a HAVING clause after grouping by sensor_id to filter out groups where the range of reading_value exceeds a certain threshold.
  • B. Using a LATERAL FLATTEN function to transform reading values into an array, calculate the mean and standard deviation in a JavaScript UDF, then use ARRAY SLICE to remove outliers before calculating the average.
  • C. Using a QUALIFY clause with window functions to filter out the outlier readings based on their distance from the mean, prior to calculating the final average.
  • D. Calculating the mean and standard deviation for each sensor in a subquery, then joining the results with the original data and filtering based on the calculated values.
  • E. Using window functions to calculate the mean and standard deviation for each sensor, then filtering the results to exclude outliers using a WHERE clause.

Answer: C,D,E

Explanation:
Options A, B and C provides valid ways to determine outliers. A is based on direct filtering based on standard deviation on the original table using window function. B uses Sub query approach and filtering. C allows to use QUALIFY clause with window functions for filtering before aggregation. D attempts to filter groups based on range which is not the intent of the original question to filter on a per reading basis if its an outlier or not. Option E, although technically possible, introduces significant complexity and performance overhead with the use of UDF and array manipulation for a task achievable with standard SQL.


NEW QUESTION # 72
You have a large CSV file containing customer transaction data that you need to load into Snowflake using Snowsight. The CSV file is located in an AWS S3 bucket. The file contains fields like 'transaction id', 'customer id', 'transaction date', and 'transaction amount. However, the 'transaction_date' column is in the format 'YYYYMMDD' and you need to convert it to Snowflake's DATE format ('YYYY-MM-DD') during the load process. Which of the following steps should you take in Snowsight to accomplish this efficiently and correctly?

  • A. Load the CSV file into Snowflake without any transformation. Write a stored procedure to transform the 'transaction_date' column and schedule the stored procedure to run periodically.
  • B. Use Snowsight's 'Load Data' wizard to load the CSV file directly into a table with the required schema. After loading, execute an "UPDATE statement to convert the 'transaction_date' column using 'TO DATE(transaction_date, YYYYMMDD'V.
  • C. Create an external table pointing to the S3 bucket. Then, create a view on top of the external table with the 'TO_DATE(transaction_date, 'YYYYMMDD')' transformation applied. Finally, create a new table using 'CREATE TABLE AS SELECT from the view.
  • D. Load the data into a staging table with all columns as VARCHAR. Then, create a new table with the desired schema. Finally, use a 'CREATE TABLE AS SELECT (CTAS) statement with 'TO DATE(transaction_date, to transform and load the data from the staging table to the final table.
  • E. Create a new table in Snowflake with the desired schema (including DATE data type for 'transaction_date'). Use Snowsight's 'Load Data' wizard to load the CSV file, selecting the appropriate file format options and using a computed column expression 'TO_DATE(transaction_date, 'YYYYMMDD')' for the 'transaction date' column.

Answer: E

Explanation:
Option A is the most efficient and correct approach. Snowsight's 'Load Data' wizard allows you to specify transformations during the load process using computed columns, which is more performant than loading into a staging table or updating after loading. Options B, C, D and E are functional but less efficient due to the extra steps involved. Using external tables for initial loading then CTAS can be good for exploration but not as direct as option A. Updates should generally be avoided on large datasets after loading when you have a chance to transform during load.


NEW QUESTION # 73
......

With the rapid market development, there are more and more companies and websites to sell DAA-C01 guide torrent for learners to help them prepare for exam. If you have known before, it is not hard to find that the study materials of our company are very popular with candidates, no matter students or businessman. Welcome your purchase for our DAA-C01 Exam Torrent. As is an old saying goes: Client is god! Service is first! It is our tenet, and our goal we are working at!

DAA-C01 Cheap Dumps: https://www.freepdfdump.top/DAA-C01-valid-torrent.html

Our excellent professionals are furnishing exam candidates with highly effective DAA-C01 study materials, you can even get the desirable outcomes within one week, Snowflake DAA-C01 Practice Questions After the candidates buy our products, we can offer our new updated materials for your downloading one year for free, Our DAA-C01 study materials allow you to improve your competitiveness.

How to keep your moral compass pointed toward true North"even DAA-C01 when those around you are compromising their ethics, Click OK to return to the Symbol dialog box, Our excellent professionals are furnishing exam candidates with highly effective DAA-C01 Study Materials, you can even get the desirable outcomes within one week.

Highly Rated Snowflake SnowPro Advanced: Data Analyst Certification Exam DAA-C01 PDF Dumps

After the candidates buy our products, we can offer our new updated materials for your downloading one year for free, Our DAA-C01 study materials allow you to improve your competitiveness.

For one thing, it is convenient and easy for you to read exam questions and answers of our DAA-C01 origination questions, DAA-C01 certifications help aspiring young men in finding the right the various positions.

Report this page