RELIABLE DSA-C03 TEST TESTKING, VALID DSA-C03 MOCK TEST

Reliable DSA-C03 Test Testking, Valid DSA-C03 Mock Test

Reliable DSA-C03 Test Testking, Valid DSA-C03 Mock Test

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q261-Q266):

NEW QUESTION # 261
You are developing a Python UDTF in Snowflake to perform time series forecasting. You need to incorporate data from an external REST API as part of your feature engineering process within the UDTF. However, you are encountering intermittent network connectivity issues that cause the UDTF to fail. You want to implement a robust error handling mechanism to gracefully handle these network errors and ensure that the UDTF continues to function, albeit with potentially less accurate forecasts when external data is unavailable. Which of the following approaches is the MOST appropriate and effective for handling these network errors within your Python UDTF?

  • A. Implement a global exception handler within the UDTF that catches all exceptions, logs the error message to a Snowflake table, and returns a default forecast value when a network error occurs. Ensure the error logging table exists and has sufficient write permissions for the UDTF.
  • B. Configure Snowflake's network policies to allow outbound network access from the UDTF to the specific REST API endpoint. This will eliminate the network connectivity issues and prevent the UDTF from failing.
  • C. Use the 'try...except' block specifically around the code that makes the API call. Within the 'except block, catch specific network-related exceptions (e.g., requests.exceptions.RequestException', 'socket.timeout'). Log the error to a Snowflake stage using the 'logging' module and retry the API call a limited number of times with exponential backoff.
  • D. Before making the API call, check the network connectivity using the 'ping' command. If the ping fails, skip the API call and return a default forecast value. This prevents the UDTF from attempting to connect to an unavailable endpoint.
  • E. Use a combination of retry mechanisms (like the tenacity library) with exponential backoff around the API call. If the retry fails after a predefined number of attempts, then return pre-computed data or use a simplified model as the UDTF's output.

Answer: C,E

Explanation:
Options B and E are the MOST appropriate for handling network errors. Using a 'try...except block (B) specifically targets the API call and allows for handling network-related exceptions gracefully. Logging the error to a Snowflake stage provides valuable debugging information. Retry with exponential backoff increases the chances of success during transient network issues. Option E improves upon option B with external and maintained libraries such as tenacity and returning a model output, not just a single value, when the error is recoverable or the data is missing. Option A, a global exception handler, is too broad and might mask other errors. Option C is a necessary prerequisite but does not address intermittent connectivity issues. Option D's 'ping' command is not reliable for determining API availability and might introduce unnecessary delays or false negatives. A complete end-to-end, complete solution must focus on addressing all aspects of code and execution.


NEW QUESTION # 262
You're tasked with building an image classification model on Snowflake to identify defective components on a manufacturing assembly line using images captured by high-resolution cameras. The images are stored in a Snowflake table named 'ASSEMBLY LINE IMAGES', with columns including 'image_id' (INT), 'image_data' (VARIANT containing binary image data), and 'timestamp' (TIMESTAMP NTZ). You have a pre-trained image classification model (TensorFlow/PyTorch) saved in Snowflake's internal stage. To improve inference speed and reduce data transfer overhead, which approach provides the MOST efficient way to classify these images using Snowpark Python and UDFs?

  • A. Use Snowflake's external function feature to offload the image classification task to a serverless function hosted on AWS Lambda, passing the and 'image_icf to the function for processing.
  • B. Create a Python UDF that loads the entire table into memory, preprocesses the images, loads the pre-trained model, and performs classification for all images in a single execution.
  • C. Create a vectorized Python UDF that takes a batch of 'image_id' values as input, retrieves the corresponding 'image_data' from the 'ASSEMBLY LINE IMAGES table using a JOIN, preprocesses the images in a vectorized manner, loads the pre-trained model once at the beginning, performs classification on the batch, and returns the results.
  • D. Create a Java UDF that loads the pre-trained model and preprocesses the images. Call this Java UDF from a Python UDF to perform the image classification. Since Java is faster than Python, this will optimize performance.
  • E. Create a Python UDF that takes a single 'image_id' as input, retrieves the corresponding 'image_data' from the table, preprocesses the image, loads the pre-trained model, performs classification, and returns the result. This UDF will be called for each image individually.

Answer: C

Explanation:
Option C offers the most efficient solution. Vectorized UDFs allow processing batches of data at once, significantly reducing overhead compared to processing each image individually (Option B). Loading the model once per batch avoids redundant model loading. Option A is highly inefficient as it attempts to load the entire table into memory. While Java can be faster in certain scenarios, the complexity of calling a Java UDF from a Python UDF (Option D) will likely introduce more overhead than benefits. External functions (Option E) introduce network latency and are generally less efficient than in-database processing, unless there's a specific need for external resources or specialized hardware that Snowflake doesn't offer.


NEW QUESTION # 263
You are working with a dataset containing customer reviews for various products. The dataset includes a 'REVIEW TEXT column with the raw review text and a 'PRODUCT ID' column. You want to perform sentiment analysis on the reviews and create a new feature called 'SENTIMENT SCORE for each product. You plan to use a UDF to perform the sentiment analysis. Which of the following steps and SQL code snippets are essential for implementing this feature engineering task in Snowflake, ensuring optimal performance and scalability? Select all that apply:

  • A. Cache the results of the sentiment analysis UDF in a temporary table to avoid recomputing the scores for the same reviews in subsequent queries. Use 'CREATE TEMPORARY TABLE to create a temporary table.
  • B. Ensure the UDF is vectorized to process batches of reviews at once, improving performance. This can be achieved using decorator on top of the python function.
  • C. Apply the sentiment analysis UDF to the 'REVIEW TEXT column within a 'SELECT statement, grouping by 'PRODUCT ID and calculating the average 'SENTIMENT_SCORE' using
  • D. Use the 'SNOWFLAKE.ML' package to train a sentiment analysis model directly within Snowflake, eliminating the need for a separate UDF.
  • E. Create a Python UDF that takes the 'REVIEW_TEXT as input and returns a sentiment score (e.g., between -1 and 1). Then, use 'CREATE OR REPLACE FUNCTION' statement to register the UDF.

Answer: B,C,E

Explanation:
Options A, C and E are correct. Option A is essential for performing sentiment analysis. Option C correctly integrates the UDF into a SQL query to generate the 'SENTIMENT SCORE'. Option E is crucial for performance since vectorized UDFs are much faster and more efficient for large datasets. Option B is not a correct usage pattern for sentiment analysis as Snowflake ML is in early stages to cater this. Option D, while seeming logical is not ideal for the task because this review data changes continuously and the model would be outdated, also temporary table is for the scope of session it is created.


NEW QUESTION # 264
You have trained a logistic regression model in Python using scikit-learn and plan to deploy it as a Python stored procedure in Snowflake. You need to serialize the model for deployment. Consider the following code snippet:

  • A. The code will execute successfully. The model serialization and deserialization using pickle are correctly implemented within the stored procedure.
  • B.
  • C. The code will fail because the 'model_bytes' variable is not accessible within the 'predict' function's scope.
  • D. The code will fail because it does not handle potential security vulnerabilities associated with deserializing pickled objects from untrusted sources.
  • E. The code will fail because Snowflake stages cannot be used to store model objects.

Answer: C,D

Explanation:
The correct answers are C and D. The 'model_bytes' variable is defined within the scope of the 'train_moder function and is not accessible within the 'predict' function (C). Additionally, using 'pickle' to deserialize data from untrusted sources poses significant security risks. Snowflake stages can be used to store model objects, however, in this example, the model is serialized but never uploaded to the stage, rendering it useless. Option B is incorrect because the code will fail due to scope issue. Option A is incorrect because code will not execute successfully and pickle library can be potentially dangerous.


NEW QUESTION # 265
You are developing a Python stored procedure in Snowflake to train a machine learning model using scikit-learn. The training data resides in a Snowflake table named 'SALES DATA. You need to pass the feature columns (e.g., 'PRICE, 'QUANTITY) and the target column ('REVENUE) dynamically to the stored procedure. Which of the following approaches is the MOST secure and efficient way to achieve this, preventing SQL injection vulnerabilities and ensuring data integrity within the stored procedure?

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

Answer: A

Explanation:
Passing the column names as a VARIANT array and using parameterized queries is the safest and most efficient approach. This avoids SQL injection vulnerabilities, as the column names are treated as data rather than code. It also allows Snowflake to optimize the query execution plan. Options A and C are vulnerable to SQL injection. Option D doesn't address the core problem of dynamically specifying columns and security. Option E introduces an extra layer of abstraction (the view) but doesn't inherently solve the dynamic column specification or SQL injection risks if the view definition is itself dynamically constructed.


NEW QUESTION # 266
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