DAA-C01 RELIABLE TEST CRAM, DAA-C01 CHEAP DUMPS

DAA-C01 Reliable Test Cram, DAA-C01 Cheap Dumps

DAA-C01 Reliable Test Cram, DAA-C01 Cheap Dumps

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q67-Q72):

NEW QUESTION # 67
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. 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.
  • 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 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.
  • D. 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.
  • E. 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.

Answer: C

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 # 68
You are responsible for maintaining a dashboard displaying real-time website traffic data'. The data is ingested into a Snowflake table named 'WEB EVENTS using Snowpipe from cloud storage. The 'WEB EVENTS' table includes 'EVENT TIMESTAMP' , 'PAGE URL' , and 'USER ID columns. The dashboard requires near real-time updates, but you are noticing significant latency. Which of the following actions, performed in isolation, is LEAST likely to improve the dashboard update frequency?

  • A. Increase the size of the Snowflake virtual warehouse used for Snowpipe data loading.
  • B. Refactor dashboard queries to directly query the 'WEB EVENTS' table without any aggregations or transformations.
  • C. Reduce the frequency of micro-batch data loading by increasing the Snowpipe 'AUTO_INGEST schedule to reduce the number of pipe executions per minute.
  • D. Optimize the Snowpipe configuration by adjusting the 'COPY INTO' statement to use file format options appropriate for the source data (e.g., compression, field delimiters).
  • E. Create a materialized view that pre-aggregates the data needed for the dashboard and configure it for automatic refresh.

Answer: C

Explanation:
Reducing the frequency of micro-batch data loading (option E) is LEAST likely to improve dashboard update frequency; in fact, it will decrease it. The dashboard needs near real-time updates, so reducing how often data is loaded will directly conflict with that requirement. All the other options are designed to help with optimization. Optimizing Snowpipe configuration, creating materialized views for pre-aggregation, and increasing warehouse size for Snowpipe can help, too. Reducing the amount of aggregations that dashboard needs will help since less processing is necessary.


NEW QUESTION # 69
A Snowflake table 'SALES_DATA' contains a 'TRANSACTION_ID' (VARCHAR), 'AMOUNT (VARCHAR), and 'TRANSACTION DATE (VARCHAR) column. Some 'TRANSACTION_ID' values are alphanumeric, others are purely numeric. The 'AMOUNT' column sometimes contains currency symbols ('$', ' ') or commas, and 'TRANSACTION DATE' is in 'MM/DD/YYYY' format. You need to perform the following transformations: 1. Extract only numeric 'TRANSACTION ID's. 2. Convert "AMOUNT' to a numeric type for calculations, removing currency symbols and commas. 3. Convert 'TRANSACTION DATE to a DATE type. Which of the following SQL queries effectively accomplishes these data type transformations in Snowflake?

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

Answer: D

Explanation:
Option E is the most comprehensive and robust solutiom - It uses 'REGEXP LIKE to filter out non-numeric and the CASE statement, which is important because 'TRANSACTION_lD's will have both numeric and alphanumeric values. - The 'AMOUNT' column correctly uses 'REGEXP_REPLACE and 'TRY_CAST to handle multiple currency symbols and converts the values to DECIMAL. -is used which handles incorrect data in DATE conversion and return NULL in case of invalid 'TRANSACTION_DATE. Option A is incorrect because IS_INTEGER is not a standard built-in Snowflake function. Option B can cause errors if the TRANSACTION_ID cannot be converted to INTEGER after being checked with REGEXP LIKE. Option C's CAST statements can cause errors if there's any data that cannot be correctly CAST.


NEW QUESTION # 70
You have a Snowpipe configured to load CSV files from an AWS S3 bucket into a Snowflake table. The CSV files are compressed using GZIP. You've noticed that Snowpipe is occasionally failing with the error 'Incorrect number of columns in file'. This issue is intermittent and affects different files. Your team has confirmed that the source data schema should be consistent. What combination of actions provides the most likely and efficient solution to address this intermittent column count mismatch issue?

  • A. Set the 'SKIP_HEADER parameter in the file format to 1 and ensure that a header row is consistently present in all CSV files. Also implement a task that validates that the header of all CSV files are correct.
  • B. Investigate the compression level of the GZIP files. Some compression levels might lead to data corruption during decompression, causing incorrect column counts. Lowering the compression might help.
  • C. Adjust the parameter in the file format to FALSE. This will allow Snowpipe to load the data, skipping rows with incorrect column counts. Implement a separate process to identify and handle skipped rows.
  • D. Check for carriage return characters within the CSV data fields. These characters can be misinterpreted as row delimiters, leading to incorrect column counts. Use the and 'RECORD_DELIMITER parameters in the file format to correctly parse the CSV data.
  • E. Recreate the Snowflake table with a 'VARIANT column to store the entire CSV row as a single field. Then, use SQL to parse the 'VARIANT* data into the desired columns.

Answer: C,D

Explanation:
Setting *ERROR ON COLUMN COUNT MISMATCH' to FALSE allows the pipe to continue without halting on such errors. However, this approach will leave behind bad records. Carriage return issues can occur, which affect the column count when ingesting data. If there are carriage return characters inside the CSV fields, this will be misinterpreted as delimiters. Option A might help if headers are present and consistent, but is less likely the root cause of an intermittent column count mismatch. Option C is unlikely to be a primary cause of column count issues as GZIP decompression is generally reliable. Option E is a workaround, but less efficient than correctly configuring the CSV parsing.


NEW QUESTION # 71
You are tasked with creating a dashboard in Snowsight to visualize sales data'. You have a table 'SALES DATA' with columns 'ORDER_DATE (DATE), 'PRODUCT CATEGORY (VARCHAR), 'SALES_AMOUNT (NUMBER), and 'REGION' (VARCHAR). The business requirements include the following: 1. Display total sales amount by product category in a pie chart. 2. Display a table showing sales amount for each region for a user-selected date range. 3. Allow the user to filter both visualizations by a specific region.
Which of the following approaches would BEST satisfy these requirements using Snowsight dashboards and features?

  • A. Create two separate charts: a pie chart for product category sales and a table for regional sales. Use the same filter on the dashboard for region, and manually enter the date range in the SQL query for the table chart.
  • B. Create two separate dashboards: one for the pie chart and another for the table. Use a global session variable to store the selected region and date range, and access it in the SQL queries for both dashboards.
  • C. Create a view with all calculations of the total sale amount, grouping by product category and region. Then create the dashboard with charts based off of this view. This will allow for easier modification if the business requirements change.
  • D. Create a single Snowsight dashboard with two charts: a pie chart showing total sales by product category using the query 'SELECT PRODUCT_CATEGORY, SUM(SALES AMOUNT) FROM SALES DATA WHERE REGION = $REGION GROUP BY PRODUCT_CATEGORY, and a table showing regional sales using the query 'SELECT REGION, FROM SALES_DATA WHERE ORDER_DATE BETWEEN $START_DATE AND $END_DATE AND REGION = $REGION GROUP BY REGION'. Define three dashboard variables: 'REGION' (Dropdown), 'START DATE (Date), and 'END DATE (Date).
  • E. Create a single Snowsight dashboard with a Python chart for product category sales, querying data using Snowflake Connector, and a table showing regional sales using SQL query. No dashboard variables are needed, as the Python script handles all filtering.

Answer: D

Explanation:
Option B is the most effective because it utilizes dashboard variables for 'REGION' , 'START_DATE, and 'END_DATE' , allowing users to dynamically filter both the pie chart and the table. It also leverages SQL queries within Snowsight for data retrieval and aggregation, making it a straightforward and efficient solution. Option A is less flexible due to the manual date range entry. Option C unnecessarily introduces Python scripting, complicating the solution. Option D is inefficient as it creates separate dashboards. Option E creates a good base, but relies on option B to implement a dashboard with interactive filters.


NEW QUESTION # 72
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