COMPLETE DAA-C01 EXAM DUMPS | EXAM VCE DAA-C01 FREE

Complete DAA-C01 Exam Dumps | Exam Vce DAA-C01 Free

Complete DAA-C01 Exam Dumps | Exam Vce DAA-C01 Free

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q83-Q88):

NEW QUESTION # 83
You are building a sales performance dashboard in Snowflake for a retail company. The data includes sales transactions, product information, and customer demographics. You need to enable users to drill down from regional sales summaries to individual store sales and then to customer-level details within the dashboard. Which of the following Snowflake features and dashboard design principles are CRUCIAL for achieving this interactive drill-down capability with optimal performance?

  • A. Exporting the data to an external BI tool and leveraging its drill-down features. Data can be exported to the external tool daily.
  • B. Creating multiple dashboards, one for each level of granularity (region, store, customer), and linking them together with navigation buttons.
  • C. Using parameterized views in Snowflake and configuring the dashboard to pass parameters dynamically based on user selections. Ensuring proper clustering keys are defined on relevant tables.
  • D. Relying solely on the dashboard's built-in filtering capabilities and avoiding any pre-aggregation or optimization in Snowflake.
  • E. Creating a stored procedure in Snowflake that dynamically generates SQL queries based on user interactions within the dashboard.

Answer: C

Explanation:
Parameterized views allow you to create flexible queries that adapt to user selections. Clustering keys ensure efficient filtering and data retrieval for drill-down operations. Creating multiple dashboards (B) is less efficient and user-friendly. Relying solely on dashboard filtering (C) can lead to performance issues. Exporting data to an external BI tool (D) introduces latency. Dynamic SQL generation (E) can be complex and prone to errors.


NEW QUESTION # 84
You are designing a data ingestion pipeline for a financial institution. The pipeline loads transaction data from various sources into a Snowflake table named 'TRANSACTIONS. The 'TRANSACTIONS table includes columns such as TRANSACTION , 'ACCOUNT ID', 'TRANSACTION DATE, 'TRANSACTION AMOUNT, and 'TRANSACTION TYPE. The data is loaded in micro- batches using Snowpipe. Due to potential source system errors and network issues, duplicate records with the same 'TRANSACTION ID' are occasionally ingested. You need to ensure data integrity by preventing duplicate 'TRANSACTION_ID' values in the 'TRANSACTIONS' table while minimizing the impact on ingestion performance. Which of the following approaches is the MOST efficient and reliable way to handle this deduplication requirement in Snowflake, considering data integrity and performance?

  • A. Define as the primary key on the 'TRANSACTIONS' table. Snowflake will automatically reject any duplicate inserts during Snowpipe ingestion.
  • B. Create a stream on the 'TRANSACTIONS' table and use it to identify newly inserted rows. Then, use a merge statement to insert new, distinct transactions into a separate staging table. Finally, periodically truncate the original 'TRANSACTIONS table and load the deduped data from the staging table.
  • C. Use a materialized view built on top of the TRANSACTIONS table that selects distinct transaction ids. This ensures that querying through the materialized view returns no duplicates.
  • D. Create a staging table with the same schema as 'TRANSACTIONS'. Use a 'MERGE' statement within the Snowpipe load process to insert new records from the incoming data into the 'TRANSACTIONS' table, only if the 'TRANSACTION ID does not already exist. Define 'TRANSACTION ID' as the primary key in the staging table. Use clustering on 'TRANSACTION_ID on the target 'TRANSACTIONS' table.
  • E. Create a scheduled task that runs every hour to identify and delete duplicate records based on 'TRANSACTION ID. The task will use a SQL query to find duplicate ' TRANSACTION ID values and remove the older entries.

Answer: D

Explanation:
Option E provides the most performant and robust solution. Although Snowflake doesn't enforce primary key constraints, defining them on the staging table and leveraging a 'MERGE' statement during the Snowpipe load process allows for efficient deduplication. Clustering on TRANSACTION_I[Y on the target table also helps with performance. A regular task would be less efficient and introduce latency. Snowflake does not automatically reject duplicate inserts based on defined primary keys (option A). Materialized views don't prevent duplicate data from entering the base table. Option C is possible but more complex to implement than a MERGE statement.


NEW QUESTION # 85
You are using Snowpipe to continuously load JSON data from an external stage. Occasionally, some JSON records are malformed and cause the pipe to fail. You want to configure the pipe to skip these invalid records and continue loading valid data, while also capturing the error details for later analysis. Which approach provides the most efficient and appropriate solution for this scenario?

  • A. Implement custom error handling in your application code to pre-validate JSON records before uploading them to the stage.
  • B. Use the = 'SKIP_FILE" option in the 'COPY INTO' statement used by the Snowpipe definition.
  • C. Configure the Snowpipe definition to use the 'VALIDATE(O)' function within the 'COPY INTO' statement.
  • D. Use the 'ON ERROR = 'CONTINUE'' option in the 'COPY INTO' statement used by the Snowpipe definition in conjunction with the 'VALIDATE function to capture error details.
  • E. Use the 'VALIDATION MODE = RETURN ALL ERRORS parameter in the 'COPY INTO' statement and then filter the data based on the errors returned.

Answer: D

Explanation:
Option E is the most efficient and complete solution. SON ERROR = 'CONTINUE" allows Snowpipe to skip bad records and continue processing. Using it in conjunction with the 'VALIDATE function within the 'COPY INTO' statement enables capturing error information for analysis. This combines error skipping with error logging. Options A, B, C, and D are either less efficient (requiring pre-processing or post- processing of data), or do not provide a comprehensive solution for both skipping and capturing error information. Using (option B) is too coarse-grained as it skips entire files, even with only a few errors. Using 'VALIDATION MODE without ERROR=CONTINUE will still stop the pipe on errors.


NEW QUESTION # 86
You have a Snowflake table 'EMPLOYEES' with columns 'EMPLOYEE ID' ONT, PRIMARY KEY), 'SALARY' and 'DEPARTMENT' (VARCHAR). You need to enforce the following business rules: 1. 'SALARY' must be a positive value. 2. 'DEPARTMENT' must be one of the following values: 'SALES', 'MARKETING', 'ENGINEERING'. 3. If the Employee is in 'SALES' Department, Salary should be between 50000 and 100000. Which of the following is the most appropriate and efficient approach using Snowflake constraints and features?

  • A. Use CHECK constraints for both rules and a third CHECK constraint to combine rules 2 and 3 to apply on each record.
  • B. Enforce rules using stored procedure at the time of insertion and updation.
  • C. Use a CHECK constraint for 'SALARY > , a CHECK constraint 'DEPARTMENT IN ('SALES', 'MARKETING', 'ENGINEERING') , and a third CHECK constraint 'CASE WHEN DEPARTMENT = 'SALES' THEN SALARY BETWEEN 50000 AND 100000 ELSE TRUE END'.
  • D. Use CHECK constraint for SALARY, Create a Lookup table for Departments, and apply a Foreign key relationship for DEPARTMENT field in EMPLOYEE table.
  • E. Use a CHECK constraint for 'SALARY > , an ENUM type for 'DEPARTMENT' , and a TRIGGER to enforce the salary range rule for the 'SALES' department.

Answer: C

Explanation:
Option D is the most appropriate and efficient. CHECK constraints are designed for these types of validations. The 'CASE' statement within the third CHECK constraint allows conditional validation based on the 'DEPARTMENT value. CHECK constriants are enforced at the time of record insert or update. Stored procedures could be an option but are not the most appropriate. Snowflake does not directly support ENUM types for column definitions. Creating Lookup table with Foreign key is another option.


NEW QUESTION # 87
A data analyst is performing an exploratory analysis on sales data and observes a highly skewed distribution for the 'sales_amount' column. Which of the following data transformation techniques are appropriate to mitigate the impact of the skewness and make the data more suitable for modeling and analysis? (Select all that apply)

  • A. Standardization
  • B. Min-Max Scaling
  • C. Box-Cox Transformation
  • D. Winsorization
  • E. Log Transformation

Answer: C,D,E

Explanation:
Log transformation and Box-Cox transformation are common techniques used to reduce skewness in data. Winsorization helps to reduce the impact of outliers, which can contribute to skewness. Standardization and Min-Max scaling are primarily used for feature scaling and do not directly address skewness.


NEW QUESTION # 88
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