FREE DOWNLOAD DEA-C02 TEST SCORE REPORT | VALID DEA-C02 RELIABLE STUDY PLAN: SNOWPRO ADVANCED: DATA ENGINEER (DEA-C02)

Free Download DEA-C02 Test Score Report | Valid DEA-C02 Reliable Study Plan: SnowPro Advanced: Data Engineer (DEA-C02)

Free Download DEA-C02 Test Score Report | Valid DEA-C02 Reliable Study Plan: SnowPro Advanced: Data Engineer (DEA-C02)

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q69-Q74):

NEW QUESTION # 69
You are responsible for monitoring the performance of a Snowflake data pipeline that loads data from S3 into a Snowflake table named 'SALES DATA. You notice that the COPY INTO command consistently takes longer than expected. You want to implement telemetry to proactively identify the root cause of the performance degradation. Which of the following methods, used together, provide the MOST comprehensive telemetry data for troubleshooting the COPY INTO performance?

  • A. Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and enable Snowflake's query profiling for the COPY INTO statement.
  • B. Use Snowflake's partner connect integrations to monitor the virtual warehouse resource consumption and query the 'VALIDATE function to ensure data quality before loading.
  • C. Query the 'COPY_HISTORY view and the view in 'ACCOUNT_USAG Also, check the S3 bucket for throttling errors.
  • D. Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and monitor CPU utilization of the virtual warehouse using the Snowflake web I-Jl.
  • E. Query the ' LOAD_HISTORY function and monitor the network latency between S3 and Snowflake using an external monitoring tool.

Answer: A,C

Explanation:
To comprehensively troubleshoot COPY INTO performance, you need data on the copy operation itself (COPY HISTORY), overall account and data validation. The COPY_HISTORY view provides details about each COPY INTO execution, including the file size, load time, and any errors encountered. Query profiling offers detailed insight into the internal operations of the COPY INTO command, revealing bottlenecks. Monitoring S3 for throttling ensures that the data source isn't limiting performance. Using helps correlate storage growth with load times. LOAD_HISTORY doesn't exist, 'VALIDATE function is for data validation not performance. While warehouse CPU utilization is useful, it doesn't provide the specific details needed to diagnose COPY INTO issues. External network monitoring is also less relevant than checking for S3 throttling and analyzing Snowflake's internal telemetry data.


NEW QUESTION # 70
A data engineering team is implementing a change data capture (CDC) process using Snowflake Streams on a table 'CUSTOMER DATA'. After several days, they observe that some records are missing from the target table after the stream is consumed. The stream 'CUSTOMER DATA STREAM' is defined as follows: 'CREATE STREAM CUSTOMER DATA STREAM ON TABLE CUSTOMER DATA;' and the transformation code to process the data is shown below. What could be the possible reasons for the missing records, considering the interaction between Time Travel and Streams? Assume all table sizes are significantly larger than micro-partitions, making full table scans inefficient.

  • A. The stream's 'AT' or 'BEFORE clause in the consumer query is incorrectly configured, causing it to skip some historical changes.
  • B. The stream's offset persistence is reliant on Time Travel, If the data being ingested is older than the set Time Travel duration, the change may not be seen by the stream.
  • C. The underlying table 'CUSTOMER DATA' was dropped and recreated with the same name, invalidating the stream's tracking capabilities.
  • D. The parameter for the database containing 'CUSTOMER_DATR is set to a value lower than the stream's offset persistence, causing some changes to be purged before the stream could consume them.
  • E. DML operations (e.g., DELETE, UPDATE) performed directly against the target table are interfering with the stream's ability to track changes consistently.

Answer: B,D

Explanation:
Option A is correct: If the 'DATA RETENTION_TIME IN DAYS' is less than the time it takes to consume the stream, Time Travel will not be able to retrieve the changes, leading to missing records. Option E is also correct, as time travel duration plays a significant role.


NEW QUESTION # 71
You are tasked with ingesting a large volume of CSV files from an external stage into a Snowflake table. Some of these CSV files contain corrupted records with inconsistent delimiters or missing values. You need to ensure that only valid records are loaded into the table, and the corrupted records are captured for further analysis. Which of the following COPY INTO options would BEST address this requirement?

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

Answer: A

Explanation:
Option E is the most comprehensive solution. ERROR = CONTINUE allows the COPY INTO statement to proceed despite errors. 'SKIP HEADER = 1' handles potential header issues in corrupted files. MISMATCH = FALSE' allows for varying column counts. 'VALIDATION _ MODE' and 'RESULT provide a mechanism to capture and analyze the rejected records, satisfying the requirement to analyze corrupted records. Options A, B, C, and D are insufficient for capturing corrupted records for analysis or may lead to data loss.


NEW QUESTION # 72
Consider a scenario where you have a large dataset of sensor readings stored in a Snowflake table called 'SENSOR DATA'. You need to build an external function to perform complex calculations on these readings using a custom Python library hosted on AWS Lambda'. The calculation requires significant computational resources, and you want to optimize the data transfer between Snowflake and the Lambda function. The following SQL is provided: CREATE OR REPLACE EXTERNAL FUNCTION ARRAY) RETURNS ARRAY VOLATILE MAX BATCH ROWS = 2000 RETURNS NULL ON NULL INPUT API INTEGRATION = aws_lambda_integration AS 'arn:aws:lambda:us-east-1:123456789012:function:sensorProcessor'; Which of the following options would further optimize the performance and reduce data transfer costs, assuming the underlying Lambda function is correctly configured and functional?

  • A. Increase the 'MAX BATCH ROWS' parameter to the maximum allowed value to send larger batches of data to the external function. Ensure Lambda function memory is increased appropriately.
  • B. Reduce the number of columns passed to the external function by performing pre-aggregation or filtering on the data within Snowflake before calling the function.
  • C. Convert the input data to a binary format (e.g., using 'TO_BINARY and FROM_BINARY' functions in Snowflake) before sending it to the Lambda function, and decode it in Lambda to reduce the size of the data being transmitted.
  • D. Rewrite the custom Python library in Java and create a Snowflake User-Defined Function (UDF) instead of using an external function.
  • E. Compress the data before sending it to the external function and decompress it within the Lambda function. Update the Lambda function to compress the array of results before sending it back to Snowflake and use Snowflake+s functions to decompress it.

Answer: A,B,E

Explanation:
The correct answers are A, B, and C. Option A reduces the amount of data transferred over the network, improving performance and reducing costs. Option B minimizes data transfer by sending only necessary data. Option C improves throughput by processing more rows per Lambda invocation, potentially reducing overall execution time. Option D requires a binary format compatible with both Snowflake and Lambda, which can be complex to implement and may not always provide significant benefits. Option E could improve performance by executing directly within Snowflake, but requires re-writing the code and may not be feasible if the Python library relies on specific dependencies not available in the Snowflake Java UDF environment.


NEW QUESTION # 73
You are using Snowflake Iceberg tables to manage a large dataset stored in AWS S3. Your team needs to perform several operations on this data, including updating existing records, deleting records, and performing time travel queries to analyze data at different points in time. Which of the following statements regarding the capabilities and limitations of Snowflake Iceberg tables are TRUE? (Select all that apply)

  • A. Snowflake Iceberg tables support both row-level and column-level security policies, allowing you to control access to sensitive data at a granular level.
  • B. Snowflake Iceberg tables support 'UPDATE, ' DELETE, and 'MERGE operations, allowing you to modify existing data directly in the data lake.
  • C. Snowflake automatically manages the Iceberg metadata, including snapshots and manifests, eliminating the need for manual metadata management tasks.
  • D. Snowflake Iceberg tables do not support transaction isolation levels, so concurrent write operations may lead to data inconsistencies.
  • E. Snowflake Iceberg tables support time travel queries using the 'AT(timestamp => ...y syntax, allowing you to query the state of the data at a specific point in time.

Answer: B,C,E

Explanation:
Snowflake Iceberg tables do support 'UPDATE' , 'DELETE' , and 'MERGE operations to modify data directly in the data lake (A). They do support time travel using the 'AT(timestamp => ...y syntax (B). Snowflake does automatically manage the Iceberg metadata (D). Snowflake Iceberg tables provide ACID guarantees and transaction isolation, so concurrent writes are handled safely. Row and column level security can be applied using Snowflake's masking policies and row access policies, but it is not a feature directly built into the Iceberg specification; rather it is a feature of the Snowflake platform. Thus, choice E is incorrect.


NEW QUESTION # 74
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