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[General] Start Snowflake DAA-C01 Exam Preparation Today And Get Success

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【General】 Start Snowflake DAA-C01 Exam Preparation Today And Get Success

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q73-Q78):NEW QUESTION # 73
You are using Snowpipe to continuously load data from an external stage (AWS S3) into a Snowflake table named 'RAW DATA. You notice that the pipe is frequently encountering errors due to invalid data formats in the incoming files. You need to implement a robust error handling mechanism that captures the problematic records for further analysis without halting the pipe's operation. Which of the following approaches is the MOST effective and Snowflake-recommended method to achieve this?
  • A. Implement a custom error logging table and modify the Snowpipe's COPY INTO statement to insert error records into this table using a stored procedure called upon failure.
  • B. Utilize Snowpipe's 'VALIDATION_MODE' parameter set to to identify and handle invalid records. This requires modification of the COPY INTO statement to redirect errors to an error table.
  • C. Implement Snowpipe's 'ERROR _ INTEGRATION' object, configuring it to automatically log error records to a designated stage location in JSON format for later analysis. This requires updating the pipe definition.
  • D. Configure Snowpipe's 'ON_ERROR parameter to 'CONTINUE' and rely on the 'SYSTEM$PIPE_STATUS' function to identify files with errors. Then, manually query those files for problematic records.
  • E. Disable the Snowpipe and manually load data using a COPY INTO statement with the 'ON_ERROR = 'SKIP_FILE" option, then manually inspect the skipped files.
Answer: C
Explanation:
Snowflake's 'ERROR INTEGRATION' feature, when configured with a pipe, automatically logs details of records that fail during ingestion to a specified stage. This provides a structured and readily accessible log of errors without interrupting the data loading process. Option A is not a native feature. Option B, while potentially usable, doesn't directly integrate with pipes as the PRIMARY mechanism. Option C involves more manual intervention and doesn't offer structured error logging. Option E defeats the purpose of automated loading via Snowpipe.

NEW QUESTION # 74
Consider a scenario where you have a table 'CUSTOMER ORDERS' with columns 'CUSTOMER ID', 'ORDER DATE' , 'ORDER TOTAL' , and 'PRODUCT CATEGORY'. You want to create a materialized view that calculates the sum of order totals for each customer, grouped by product category, and refreshed automatically on a daily basis. However, you are also concerned about minimizing the cost of materialized view maintenance. Which of the following strategies would be MOST cost-effective while still providing reasonably up-to-date data?
  • A. Create a materialized view and set the refresh schedule to 'ON CHANGE'.
  • B. Create a materialized view without specifying a refresh schedule, and manually refresh it whenever the report is run.
  • C. Create a materialized view and schedule a daily refresh at a time of low system activity.
  • D. Create a materialized view and set the refresh schedule to 'ON CHANGE' with a clustering key on
  • E. Create a standard view with the same aggregation logic and optimize the underlying table using clustering.
Answer: C
Explanation:
Scheduling a daily refresh allows the materialized view to be updated regularly without incurring the overhead of 'ON CHANGE' refreshes, which can be very costly if the underlying table is frequently updated. Manual refreshes would not provide up-to-date data automatically. 'ON CHANGE' without further optimization can be extremely expensive. A standard view wouldn't provide the performance benefits of a materialized view. Clustering on CUSTOMER ID might improve performance but would not address the refresh cost directly.

NEW QUESTION # 75
You have a Snowflake table called 'PRODUCT SALES' with columns 'PRODUCT ID (INT), 'SALE DATE' (DATE), and 'SALES AMOUNT' You want to implement a data integrity rule to prevent duplicate records based on 'PRODUCT ID and 'SALE DATE. Which of the following methods provides the most effective way to achieve this in Snowflake, and why?
  • A. Add a composite UNIQUE constraint on 'PRODUCT ID' and 'SALE DATE. Snowflake will automatically prevent the insertion of duplicate rows during data loading or insertion.
  • B. Implement data validation within your ETL pipeline before loading data into Snowflake to prevent duplicates from entering the table. This approach keeps the table clean from the start.
  • C. Create a view that filters out duplicate records using 'ROW NUMBER()' and partitioning by 'PRODUCT ID and 'SALE DATE. This guarantees data integrity during query execution.
  • D. Create a user defined function (UDF) that checks for the existance of data before insert. If data already exist, don't insert it.
  • E. Create a stored procedure that runs periodically to identify and delete duplicate records based on 'PRODUCT and 'SALE DATE'. This approach fixes integrity issues reactively.
Answer: A,B
Explanation:
Options C and D provide the most effective methods. A composite UNIQUE constraint directly prevents duplicate insertions at the table level. Validating in the ETL pipeline (D) prevents duplicates before they even reach the database. A view (A) only masks the issue, and a stored procedure (B) is reactive and doesn't prevent duplicates from being inserted in the first place. A UDF could be helpful but is not the BEST option for this scenario.

NEW QUESTION # 76
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. 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.
  • B. Define as the primary key on the 'TRANSACTIONS' table. Snowflake will automatically reject any duplicate inserts during Snowpipe ingestion.
  • 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 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.
  • 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: A
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 # 77
When working with Snowsight dashboards to summarize large data sets, what key advantage do they offer in exploratory analyses?
  • A. They only support basic data summarization.
  • B. Snowsight dashboards can't handle large data sets efficiently.
  • C. They are limited to presenting static data sets.
  • D. Snowsight dashboards facilitate quick, visual comprehension of complex data.
Answer: D
Explanation:
Snowsight dashboards aid in exploratory analysis by providing visually accessible insights into complex data, aiding quick comprehension.

NEW QUESTION # 78
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