DEA-C02 RELIABLE EXAM SIMS & LATEST DEA-C02 TEST SAMPLE

DEA-C02 Reliable Exam Sims & Latest DEA-C02 Test Sample

DEA-C02 Reliable Exam Sims & Latest DEA-C02 Test Sample

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Tags: DEA-C02 Reliable Exam Sims, Latest DEA-C02 Test Sample, Test DEA-C02 Online, DEA-C02 Valid Exam Answers, DEA-C02 Dumps Download

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Pass Guaranteed Quiz Marvelous Snowflake DEA-C02 - SnowPro Advanced: Data Engineer (DEA-C02) Reliable Exam Sims

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q303-Q308):

NEW QUESTION # 303
You have created a JavaScript UDF named 'calculate discount' in Snowflake that takes two arguments: 'product_price' (NUMBER) and 'discount_percentage' (NUMBER). The UDF calculates the discounted price using the formula: 'product_price (1 - discount_percentage / 100)'. However, when you call the UDF with certain input values, you are encountering unexpected results, specifically with very large or very small numbers due to JavaScript's number precision limitations. Which of the following strategies can you implement to mitigate this issue and ensure accurate calculations within your JavaScript UDF?

  • A. Convert the input numbers to strings within the JavaScript UDF before performing the calculation.
  • B. Use JavaScript's 'toFixed(V method to round the result to a fixed number of decimal places.
  • C. Avoid large or small number and stick to the limited range of input values.
  • D. Utilize a JavaScript library specifically designed for handling arbitrary-precision arithmetic, such as 'Big.js' or 'Decimal.jS , within the UDF.
  • E. Cast input arguments and the result to 'FLOAT within the UDF.

Answer: D

Explanation:
Option B is the most reliable solution. Using a dedicated arbitrary-precision arithmetic library like 'Big.js' or 'Decimal.js' allows you to perform calculations with a higher degree of accuracy, overcoming JavaScript's inherent limitations in handling very large or very small numbers. Option A might help with formatting the output, but it doesn't address the precision issue during calculation. Option C and D will not solve the problem. Option E is not practical.


NEW QUESTION # 304
You are building a data pipeline to ingest clickstream data into Snowflake. The raw data is landed in a stage and you are using a Stream on this stage to track new files. The data is then transformed and loaded into a target table 'CLICKSTREAM DATA. However, you notice that sometimes the same files are being processed multiple times, leading to duplicate records in 'CLICKSTREAM DATA. You are using the 'SYSTEM$STREAM HAS DATA' function to check if the stream has data before processing. What are the possible reasons this might be happening, and how can you prevent it? (Select all that apply)

  • A. The stream offset is not being advanced correctly after processing the files. Ensure that the files are consumed completely and a DML operation is performed to acknowledge consumption.
  • B. The 'SYSTEM$STREAM HAS DATA' function is unreliable and should not be used for production data pipelines. Use 'COUNT( on the stream instead.
  • C. The transformation process is not idempotent. Even with the same input files, it produces different outputs each time it runs.
  • D. The auto-ingest notification integration is configured incorrectly, causing duplicate notifications to be sent for the same files. This is particularly applicable when using cloud storage event triggers.
  • E. The COPY INTO command used to load the files into Snowflake has the 'ON ERROR = CONTINUE option set, allowing it to skip corrupted files, causing subsequent processing to pick them up again.

Answer: A,C,D

Explanation:
Several factors could lead to duplicate processing: B (Stream offset not advancing): Streams track changes based on an offset. If the offset is not advanced after processing, the same changes will be re-processed. C (Non-idempotent transformation): If the transformation logic isn't idempotent, re-processing the same data will lead to different results, effectively creating duplicates. E (Duplicate Auto-ingest Notifications): If the auto-ingest process is configured to send duplicate notifications for the same files (due to misconfiguration of cloud storage event triggers, for example), the COPY INTO command will run multiple times for the same file. 'SYSTEM$STREAM HAS DATA is a valid function (A is incorrect). 'ON _ ERROR = CONTINUE (D) would prevent files from being skipped but would not itself cause duplicate processing. The skipping might surface other issues, but isn't the direct cause.


NEW QUESTION # 305
A data engineering team is responsible for an ELT pipeline that loads data into Snowflake. The pipeline has two distinct stages: a high- volume, low-complexity transformation stage using SQL on raw data, and a low-volume, high-complexity transformation stage using Python UDFs that leverages an external service for data enrichment. The team is experiencing significant queueing during peak hours, particularly impacting the high-volume stage. You need to optimize warehouse configuration to minimize queueing. Which combination of actions would be MOST effective?

  • A. Create a single, large (e.g., X-Large) warehouse and rely on Snowflake's automatic scaling to handle the workload.
  • B. Create a single, X-Small warehouse and rely on Snowflake's query acceleration service to handle the workload.
  • C. Create two separate warehouses: a Large, multi-cluster warehouse configured for auto-scale for the high-volume, low-complexity transformations and a Small warehouse for the low-volume, high-complexity transformations.
  • D. Create two separate warehouses: a Medium warehouse for the high-volume, low-complexity transformations and an X-Small warehouse for the low-volume, high-complexity transformations.
  • E. Create two separate warehouses: a Small warehouse configured for auto-suspend after 5 minutes for the high-volume, low-complexity transformations and a Large warehouse configured for auto-suspend after 60 minutes for the low-volume, high-complexity transformations.

Answer: C

Explanation:
Creating separate warehouses allows for independent scaling and resource allocation based on workload characteristics. Using a larger, multi-cluster warehouse with auto-scale for the high-volume stage ensures that sufficient resources are available to handle the load without queueing. A smaller warehouse is sufficient for the low-volume, high-complexity transformations. Options A, D and E are incorrect as they do not appropriately separate and size warehouses according to the workload profile. Option B sizes warehouses incorrectly.


NEW QUESTION # 306
You have a table 'CUSTOMERS' with columns 'CUSTOMER ID', 'FIRST NAME', 'LAST NAME, and 'EMAIL'. You need to transform this data into a semi-structured JSON format and store it in a VARIANT column named 'CUSTOMER DATA' in a table called 'CUSTOMER JSON'. The desired JSON structure should include a root element 'customer' containing 'id', 'name', and 'contact' fields. Which of the following SQL statements, used in conjunction with a CREATE TABLE and INSERT INTO statement for CUSTOMER JSON, correctly transforms the data?

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

Answer: C

Explanation:
The correct answer constructs the JSON structure using nested 'OBJECT_CONSTRUCT functions. Option A directly creates a Snowflake VARIANT, which can be inserted into the 'CUSTOMER_DATR column. While many other approaches exist that involve parsing or converting to and from string values, those approaches are unnecessary because OBJECT_CONSTRUCT supports the correct desired behavior directly.


NEW QUESTION # 307
You are tasked with creating a development environment from a production database in Snowflake. The production database is named 'PROD DB' and contains several schemas, including 'CUSTOMER DATA' and 'PRODUCT DATA'. You want to create a clone of the 'PROD DB' database named 'DEV DB', but you only need the 'CUSTOMER DATA' schema for development purposes and all the data should be masked with a custom UDF 'MASK EMAIL' for 'email' column in 'CUSTOMER' table. The 'email' column is VARCHAR. Which of the following sequences of SOL statements would achieve this in Snowflake? Note: UDF MASK EMAIL already exists in the account.

  • A.
  • B.
  • C.
  • D.
  • E.

Answer: C

Explanation:
Option B is the most appropriate solution. It clones the entire production database, drops the unnecessary schema, then clone table from PROD and after cloning, it uses masking policy on email column on the cloned DEV environment. Option A is incorrect because you cannot use MASK EMAIL while createing the table. Option C requires to drop and add column again, option D, using a view will not permanently mask data at the storage level. And Option E updates the table after cloning, which consumes resources and isn't as elegant as using a masking policy.


NEW QUESTION # 308
......

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