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Snowflake SnowPro Advanced: Data Analyst Certification Exam DAA-C01 Prüfungsfragen mit Lösungen (Q181-Q186):

181. Frage
Your team is building a data pipeline to ingest data from a REST API that returns JSON payloads. Due to API rate limits, you need to implement a backoff strategy to avoid overwhelming the API. You are using Python and the 'requests' library for data ingestion. Which code snippet BEST demonstrates a robust backoff strategy with exponential backoff and jitter?

Antwort: B

Begründung:
Option B provides the most robust backoff strategy. It implements exponential backoff (wait time increases exponentially with each retry) and adds jitter (a random amount of time) to the wait time to avoid synchronized retries from multiple clients. also handles error codes correctly. Option A doesn't have a backoff strategy. Option C has a simple retry mechanism but lacks exponential backoff and a limit on retries. Option D lacks error handling and retry logic. Option E uses recursion, which is generally not recommended and does not feature exponential backoff.

182. Frage
You have a Snowflake table containing order data'. You need to calculate the shipping cost for each order based on the order amount and the destination country. You decide to use a Java UDF for this calculation, as the logic is complex and involves external APIs (simulated here). The UDF should take the order amount (FLOAT) and destination country (VARCHAR) as input and return the calculated shipping cost (FLOAT). The Java code requires external JAR files to be imported. Which of the following options correctly defines and calls the Java UDF in Snowflake, assuming the necessary JAR file has been uploaded to a stage named 'my_stage'?

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

Antwort: E

Begründung:
Option E is the most correct because the function definition does not require the definition of the class 'com.example.ShippingCalculator' within the function body. Since the jar file is defined within the imports section, snowflake does not need the explicit definition. Option A, C, and D requires the function and class definition which is already defined in the jar, and defining it again will lead to conflicts. Option B doesn't correctly define the class. All the rest of the options either try to define the Java code inline (which is incorrect when using IMPORTS) or have syntax errors in the UDF definition.

183. Frage
You are using Snowpipe to continuously load data from an AWS S3 bucket into a Snowflake table called 'ORDERS. The data is in JSON format. You observe that Snowpipe is occasionally missing records, even though the S3 event notifications are being correctly sent to the Snowflake-managed SQS queue. Upon investigation, you discover that some JSON records are larger than the maximum size supported by Snowpipe for a single record (16MB). You need to implement a solution to handle these oversized JSON records without losing data,. Which of the following approaches is the most efficient and reliable?

A. Increase the 'MAX FILE SIZE parameter of the Snowpipe configuration to accommodate the larger JSON records. Snowflake automatically handles oversized records by splitting them internally.
B. Configure the S3 bucket to automatically split oversized JSON files into smaller files before they are sent to the SQS queue. Snowpipe will then process these smaller files independently.
C. Disable Snowpipe and switch to a batch loading approach using the COPY INTO command with automatic data compression. The COPY INTO command handles larger files more efficiently than Snowpipe.
D. Use the 'VALIDATE function in Snowflake to identify oversized JSON records. Then, manually extract and split those records into smaller files and load them separately.
E. Implement a pre-processing step using an AWS Lambda function triggered by S3 events to split the oversized JSON records into smaller, valid-sized chunks before they are ingested by Snowpipe. Update the Snowpipe COPY statement to handle the new chunked data format.

Antwort: E

Begründung:
The correct answer is B. Snowpipe has a limitation of 16MB per record. The most reliable solution is to pre-process the oversized records before they reach Snowpipe. Using an AWS Lambda function is a serverless and scalable way to split these records. Option A is incorrect because 'MAX FILE_SIZE pertains to the size of the files, not individual records within those files. Option C is not feasible as S3 doesn't natively split JSON files. Option D is inefficient as it involves manual intervention. Option E defeats the purpose of continuous data loading with Snowpipe. By splitting oversized records before Snowpipe ingests them, you ensure that no data is lost, and Snowpipe can continue to operate as designed.

184. Frage
You are building a Snowsight dashboard to monitor the performance of various SQL queries. You have a table 'QUERY HISTORY with columns 'QUERY ID', 'START TIME, 'END TIME, 'USER NAME, 'DATABASE NAME, and 'EXECUTION_TIME (in seconds). You want to create a bar chart that shows the average execution time for each user, but only for queries executed against a specific database (e.g., 'SALES DB') within the last week. Furthermore, you need to allow users to filter the data by username via a Snowsight dashboard variable. What is the most efficient SQL query and Snowsight configuration to achieve this?

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

Antwort: B

Begründung:
Option B provides the most efficient solution by using a Snowsight dashboard variable ('USERNAME) with a dropdown populated from the distinct usernames in the 'QUERY HISTORY table. This allows users to easily filter the data by selecting a username from the dropdown, and the SQL query directly incorporates the variable using '$USERNAME in the WHERE clause for optimal performance. Option A lacks the dynamic filtering capability. Option C will not work as 'HAVING' clause should use aggregate functions, and even if it worked is not efficient. Option D would be difficult to maintain and less performant. Option E introduces unnecessary complexity with a stored procedure for this specific requirement.

185. Frage
You have a CSV file loaded into a Snowflake table named 'raw data'. The file contains customer order data, but some rows have missing values in the 'order date' column. You need to create a new table, 'cleaned data' , that contains only valid records and handles missing 'order date' values by substituting them with the date '1900-01-01'. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowflake features?

Antwort: E

Begründung:
Option E is the most efficient and correct. 'COALESCE' efficiently handles NULL replacement, and ensures the replacement value is the correct data type (DATE). It also explicitly selects all other columns. Option A only filters out rows with null order_date. Options B, C and D creates a new column, . It does not also implicitly take all columns, which would make this more appropriate.

186. Frage
......

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Lee Gray Lee Gray

Biography

DAA-C01 Schulungsmaterialien & DAA-C01 Dumps Prüfung & DAA-C01 Studienguide

DAA-C01 Online Praxisprüfung, DAA-C01 Testking

Snowflake SnowPro Advanced: Data Analyst Certification Exam DAA-C01 Prüfungsfragen mit Lösungen (Q181-Q186):

DAA-C01 Übungsmaterialien & DAA-C01 Lernführung: SnowPro Advanced: Data Analyst Certification Exam & DAA-C01 Lernguide