EXAM DAA-C01 QUIZZES - EXAM DAA-C01 ANSWERS

Exam DAA-C01 Quizzes - Exam DAA-C01 Answers

Exam DAA-C01 Quizzes - Exam DAA-C01 Answers

Blog Article

Tags: Exam DAA-C01 Quizzes, Exam DAA-C01 Answers, Practice Test DAA-C01 Fee, DAA-C01 Valid Exam Camp, Top DAA-C01 Dumps

Our DAA-C01 study materials are the accumulation of professional knowledge worthy practicing and remembering. There are so many specialists who join together and contribute to the success of our DAA-C01 guide quiz just for your needs. As well as responsible and patient staff who has being trained strictly before get down to business and interact with customers on our DAA-C01 Exam Questions. You can contact with our service, and they will give you the most professional guide.

Our brand has marched into the international market and many overseas clients purchase our DAA-C01 exam dump online. As the saying goes, Rome is not build in a day. The achievements we get hinge on the constant improvement on the quality of our DAA-C01 latest study question and the belief we hold that we should provide the best service for the clients. The great efforts we devote to the Snowflake exam dump and the experiences we accumulate for decades are incalculable. All of these lead to our success of DAA-C01 learning file and high prestige.

>> Exam DAA-C01 Quizzes <<

DAA-C01 valid prep cram & DAA-C01 sure pass download

A full Snowflake DAA-C01 package is required to take each Success in Life. If you want to be successful, you need to prepare well for the SnowPro Advanced: Data Analyst Certification Exam DAA-C01 exam. Buying the right Snowflake DAA-C01 Exam Preparation Materials is one way to prepare for it. With the right study tools, you can easily prepare for the SnowPro Advanced: Data Analyst Certification Exam. Whether you want to study Snowflake DAA-C01 Exam or pass other SnowPro Advanced: Data Analyst Certification Exam exam, if you want to prepare for Snowflake DAA-C01 exam, you can choose Snowflake DAA-C01 Valid Exam Questions exam.

Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q206-Q211):

NEW QUESTION # 206
You are working with a table named 'PRODUCT DESCRIPTIONS that contains product descriptions in a 'description' (VARCHAR) column. You need to implement a solution to identify potentially sensitive information within these descriptions, specifically looking for mentions of credit card numbers or social security numbers (SSNs). You want to flag any description that contains either of these patterns. Which of the following Snowflake SQL snippets, leveraging scalar string functions and regular expressions, provides the most efficient and accurate way to achieve this? (Assume that valid credit card numbers are 16 digits and valid SSNs are in the format 'XXX-XX-XXXX'). Select all correct options.

  • A. Option E
  • B. Option D
  • C. Option B
  • D. Option C
  • E. Option A

Answer: D,E

Explanation:
Options A and C are both correct. Option A uses 'REGEXP_LIKE with two separate regular expressions to search for a 16-digit number (credit card) and a 'XXX-XX-XXXX' pattern (SSN). Option C uses a single 'REGEXP LIKE' function with an alternation to combine both patterns into one regular expression , which is generally more efficient. Option B relies on 'CONTAINS' which performs a simple substring search, not regular expression matching; this will not accurately identify the patterns. Option D uses 'STARTS WITH' which will only identify descriptions that begin with the specified keywords, missing most cases. Option E uses 'LIKE operator for the mentioned words in the sentence that can be 'Credit Card' or 'Social Security Number', but this isn't a search for Credit card numbers or SSNs.


NEW QUESTION # 207
You have identified a valuable dataset on the Snowflake Marketplace related to weather patterns. To consume this data, you perform the following actions: 1. You request and receive the data share from the provider. 2. You create a database named 'WEATHER DB' from the share. Now you want to create a secure view named 'DAILY WEATHER SUMMARY in your own database 'ANALYTICS DB.PUBLIC', which joins your internal sales data C ANALYTICS DB.PUBLIC.SALES) with the weather data from the provider's 'WEATHER DB.WEATHER SCHEMA.DAILY WEATHER' table. You only want to expose specific columns from both tables in your view to minimize data exposure. Which of the following steps are required to ensure this secure and functional integration?

  • A. Create the view using fully qualified names, selecting only the necessary columns from both the 'SALES' table and the 'DAILY WEATHER table.
  • B. Create an outbound share and grant usage on the ANALYTICS DB database
  • C. No additional steps are required; you can directly query in your view definition.
  • D. Grant 'SELECT privileges on 'ANALYTICS DB.PUBLIC.SALES' to the share provider.
  • E. Create a warehouse with access control policies enabled.

Answer: A

Explanation:
Option D is the correct answer. When consuming data from a Snowflake Marketplace data share, you do not need to grant privileges to the provider on your internal data (Option A is incorrect). Option B is incorrect because creating views and acessing tables requires proper schema names. Option C is incorrect because outbound shares are not needed to consume marketplace data and access control is managed within the consumer account. Option E is incorrect because enabling access control policies on a warehouse is irrelevant to the task of creating a secure view that joins internal data with shared data.


NEW QUESTION # 208
You are tasked with analyzing website clickstream data stored in a Snowflake table called 'clickstream_events'. Each row represents a click event and contains a 'session_id' , and a 'properties' column of type VARIANT that stores key-value pairs related to the event (e.g., '(page': '[product/123', 'element': You need to extract the 'page' and 'element' values from the 'properties' column and identify the most common 'page'-'element' combinations for each 'session_id'. Furthermore you need to limit the results of your data to the top 5 pages element pair. How can this task be accomplished using Snowflake table functions and analytical functions?

  • A. Create a UDF to parse the 'properties' VARIANT and return a table with 'page' and 'element columns, then JOIN this UDF's output with the original table and use QUALIFY ROW NUMBER() OVER (PARTITION BY 'session_id' ORDER BY COUNT( ) DESC) 5.
  • B. Use LATERAL FLATTEN to extract the keys and values from the 'properties' column, then use GROUP BY 'session_id', 'key', 'value' and COUNT( ) to find the most frequent combinations.
  • C. Use multiple LATERAL FLATTEN calls, one for 'page' and one for 'element', then JOIN the results on and use QUALIFY ROW_NUMBER() OVER (PARTITION BY 'session_id' ORDER BY COUNT( ) DESC) 5.
  • D. First create a view that flattens the JSON column using LATERAL FLATTEN, then select from this view to perform the group by and ranking operations.
  • E. Extract 'page' and 'element' using 'properties:page' and 'properties:element' directly in the SELECT statement, then use GROUP BY 'session_id', 'page', 'element' and QUALIFY ROW NUMBER() OVER (PARTITION BY 'session_id' ORDER BY COUNT( ) DESC) 5.

Answer: E

Explanation:
Option C is the most efficient and Snowflake-idiomatic way to achieve this. Directly accessing 'properties:page' and properties:element' is more performant than using LATERAL FLATTEN when you know the specific keys you need. The QUALIFY clause, combined with ROW NUMBER(), efficiently filters the results to the top 5 combinations per session. LATERAL FLATTEN is generally used when you need to iterate over an array within the VARIANT, not when you're extracting specific key-value pairs. UDF introduces extra overhead.


NEW QUESTION # 209
You have a Snowflake table 'CUSTOMER ORDERS with columns 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. You need to calculate the cumulative sum of 'ORDER AMOUNT' for each customer, ordered by 'ORDER DATE. However, due to potential late-arriving data, you also need to implement a windowing function that resets the cumulative sum if there's a gap of more than 30 days between consecutive orders for a customer. Which SQL query best accomplishes this?

  • A. Option E
  • B. Option D
  • C. Option B
  • D. Option A
  • E. Option C

Answer: E

Explanation:
Option C correctly uses a conditional partitioning approach. UG(ORDER DATE, 1, ORDER DATE) OVER (PARTITION BY CUSTOMER_ID ORDER BY ORDER_DATE)' calculates the previous order date for each customer. (ORDER_DATE - 1, ORDER DATE) OVER (PARTITION BY CUSTOMER ID ORDER BY ORDER DATE) > 30)' creates a boolean expression that is true when the difference between consecutive order dates exceeds 30 days. This boolean expression is then used as a secondary partition key, effectively restarting the cumulative sum whenever a gap of more than 30 days occurs. The primary partition is still 'CUSTOMER ID' , ensuring sums are calculated within each customer's order history. The ordering of 'ORDER_DATE is essential for the cumulative sum to be calculated chronologically.


NEW QUESTION # 210
You are responsible for loading data into a Snowflake table named 'CUSTOMER DATA' from a series of compressed JSON files located in a Google Cloud Storage (GCS) bucket. The data volume is significant, and the loading process needs to be as efficient as possible. The JSON files are compressed using GZIP, and they contain a field called 'registration date' that should be loaded as a DATE type in Snowflake. However, some files contain records where the 'registration_date' is missing or has an invalid format. Your goal is to load all valid data while skipping any files that contain invalid dates, and log any files that contain invalid records. You want to choose the most efficient approach. Which of the following options represents the best strategy to achieve this?

  • A. create a file format object specifying 'TYPE = JSON' and 'COMPRESSION = GZIP'. Use a COPY INTO command with a transformation function 'TRY Configure the 'CUSTOMER DATA' table with a default value for 'registration_date' and use 'ON ERROR = CONTINUE'.
  • B. create a file format object specifying 'TYPE = JSON' and 'COMPRESSION = GZIP. Use a COPY INTO command with 'ON_ERROR = SKIP_FILE. Implement a scheduled task to query the COPY HISTORY view to identify any skipped files and manually investigate the errors.
  • C. Create a file format object specifying 'TYPE = JSON' and 'COMPRESSION = GZIP. Use a COPY INTO command with a transformation function 'TO DATE(registration_datey and SON ERROR = CONTINUE. Use a validation table to store rejected records.
  • D. Create a file format object specifying 'TYPE = JSON' and 'COMPRESSION = GZIP. Use a COPY INTO command with a transformation function 'TRY TO DATE(registration_date)' and 'ON ERROR = SKIP FILE. Implement a separate process to validate the loaded data for NULL 'registration_date' values.
  • E. Use Snowpipe with a file format object specifying 'TYPE = JSON' and 'COMPRESSION = GZIP'. Configure error notifications for the pipe and handle errors manually.

Answer: A

Explanation:
The correct answer is E. Using gracefully handles invalid dates by returning NULL, which can be managed using a default value on the target table. 'ON ERROR = CONTINUE' ensures the loading process doesn't halt. Combining this with a default value provides for a fast, efficient load. Option A skips the entire file, which is not desired if only some records are invalid. Option B will halt the load process if the target field cannot accept a value. Option C is valid but requires a separate process. Option D makes the manual handling more complex since Snowpipe is designed for near real time instead of batch. E is the best option as all invalid fields will be populated with the default value and load will be unaffected.


NEW QUESTION # 211
......

Because they are immensely useful and help you gain success in a DAA-C01 certification exam. More than ever, the professionals are now facing a highly competitive world to get their talent recognized enhancing their positions in their work environment. Such a milieu demands them to enrich their candidature more seriously. So the professionals work hard to maintain their quality and never fail in doing so. 2Pass4sure DAA-C01 Certification exams are the best option for any ambitious and ardent professional to make his continuation in his area of work intact.

Exam DAA-C01 Answers: https://www.2pass4sure.com/SnowPro-Advanced/DAA-C01-actual-exam-braindumps.html

Snowflake Exam DAA-C01 Quizzes Finally the clients will receive the mails successfully, If you want to choose this certification training resources, 2Pass4sure's Snowflake DAA-C01 exam training materials will be the best choice, You have no need to doubt your abilities, our DAA-C01 exam has included all relevant IT knowledge that you should grasp, Snowflake Exam DAA-C01 Quizzes This pdf contains test questions compiled by experts.

Therefore, for any desired behavior, an object either knows DAA-C01 it personally, inherits it, or knows another object who knows it, These features greatly simplify Wi-Fi connectivity.

Finally the clients will receive the mails successfully, If you want to choose this certification training resources, 2Pass4sure's Snowflake DAA-C01 Exam Training materials will be the best choice.

Pass Guaranteed Quiz Snowflake - Unparalleled DAA-C01 - Exam SnowPro Advanced: Data Analyst Certification Exam Quizzes

You have no need to doubt your abilities, our DAA-C01 exam has included all relevant IT knowledge that you should grasp, This pdf contains test questions compiled by experts.

Snowflake provides latest SnowPro Advanced: Data Analyst Certification Exam DAA-C01 test.

Report this page