DSA-C03 VALID TEST SIMULATOR UPDATED QUESTIONS POOL ONLY AT BRAINDUMPSPREP

DSA-C03 Valid Test Simulator Updated Questions Pool Only at BraindumpsPrep

DSA-C03 Valid Test Simulator Updated Questions Pool Only at BraindumpsPrep

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q74-Q79):

NEW QUESTION # 74
You have successfully deployed a real-time prediction service using Snowpark Container Services, consuming events from a Kafka topic. The service leverages a large language model (LLM) stored in the Snowflake Model Registry. You observe that inference latency is high and the service is struggling to keep up with the incoming event rate. You need to optimize the service for higher throughput and lower latency. Which of the following actions, when implemented together, would most effectively improve the performance of your Snowpark Container Services deployment?

  • A. Switch to a smaller, less accurate LLM. Increase the 'container.resources.cpu' allocation for the service. Ensure data is pre-processed before sending to kafka.
  • B. Enable autoscaling for the service based on CPU utilization. Remove all logging statements from the containerized application to reduce 1/0 overhead.
  • C. Implement custom monitoring solution outside of snowflake and determine bottleneck of your application. Increase the container.resources.gpu allocation for the service.
  • D. Increase the 'container.resources.memory' allocation for the service. Implement caching of frequently accessed data within the containerized application.
  • E. Increase the number of replicas for the service. Implement batching within the containerized application to process multiple events in a single inference call.

Answer: D,E

Explanation:
Options A and D, when combined, offer the most effective approach for improving throughput and reducing latency. Increasing the number of replicas allows for parallel processing of incoming events, distributing the load across multiple containers. Batching reduces the overhead of individual inference calls by processing multiple events together, improving overall throughput. Increasing the memory allocation allows the container to handle larger batches and cache more data. Implementing caching will reduce the number of times your container will pull the model, hence increasing the overall throughput. Option B might improve latency, but at the cost of accuracy. Increasing CPU allocation alone may not be sufficient if the bottleneck is memory or 1/0. Preprocessing data before sending to kafka is a good practice but it doesn't specifically impact the container performance. Option C Autoscaling is beneficial, but it won't address the underlying issue of inefficient inference. Removing logging statements might offer a minor performance improvement, but it's unlikely to be a significant factor. Option E While monitoring is important, it doesn't directly address the performance bottleneck. Also increasing gpu may not solve the problem.


NEW QUESTION # 75
Consider the following Python UDF intended to train a simple linear regression model using scikit-learn within Snowflake. The UDF takes feature columns and a target column as input and returns the model's coefficients and intercept as a JSON string. You are encountering an error during the CREATE OR REPLACE FUNCTION statement because of the incorrect deployment of the package during runtime. What would be the right way to fix this deployment and execute your model?

  • A. The code works seamlessly without modification as Snowflake automatically resolves all the dependencies and ensures the execution of code within the create or replace function statement.
  • B. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
  • C. The required packages 'scikit-learn' is not present. The correct way to create UDF is by including the import statement within the function along with the deployment.
  • D. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
  • E. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.

Answer: D

Explanation:
Option E is the correct option and provides explanation for deploying the packages and ensuring that model executes successfully.


NEW QUESTION # 76
You are tasked with identifying Personally Identifiable Information (PII) within a Snowflake table named 'customer data'. This table contains various columns, some of which may contain sensitive information like email addresses and phone numbers. You want to use Snowflake's data governance features to tag these columns appropriately. Which of the following approaches is the MOST effective and secure way to automatically identify and tag potential PII columns with the 'PII CLASSIFIED tag in your Snowflake environment, ensuring minimal manual intervention and optimal accuracy?

  • A. Manually inspect each column in the 'customer_data' table and apply the 'PII_CLASSIFIED' tag to columns that appear to contain PII based on their names and a small sample of data.
  • B. Use Snowflake's built-in classification feature with a pre-defined sensitivity category to identify potential PII columns. Associate a masking policy that redacts the data, and apply a tag 'PII_CLASSIFIED' via automated tagging to the columns identified as containing PII.
  • C. Export the 'customer_data' to a staging area in cloud storage, use a third-party data discovery tool to scan for PII, and then manually apply the "PII_CLASSIFIED' tag to the corresponding columns in Snowflake based on the tool's findings.
  • D. Write a SQL script to query the 'INFORMATION SCHEMA.COLUMNS' view, identify columns with names containing keywords like 'email' or 'phone', and then apply the 'PII_CLASSIFIED tag to those columns.
  • E. Create a custom Snowpark for Python UDF that uses regular expressions to analyze the data in each column and apply the 'PII_CLASSIFIED tag if a match is found. Schedule this UDF to run periodically using Snowflake Tasks.

Answer: B

Explanation:
Snowflake's built-in classification feature is the most effective because it uses machine learning models to automatically identify sensitive data with a high degree of accuracy. Associating masking policies with the identified columns provides additional data protection. Automated tagging further streamlines the governance process. Option A, while viable, requires custom code and maintenance. Option C is manual and error-prone. Option D is based solely on column names and can lead to false positives and negatives. Option E introduces unnecessary complexity and security risks by exporting data.


NEW QUESTION # 77
You have deployed a regression model in Snowflake as an external function using AWS Lambda'. The external function takes several numerical features as input and returns a predicted value. You want to continuously monitor the model's performance in production and automatically retrain it when the performance degrades below a predefined threshold. Which of the following methods represent VALID approaches for calculating and monitoring model performance within the Snowflake environment and triggering the retraining process?

  • A. Create a Snowflake Task that periodically executes a SQL query to calculate performance metrics (e.g., RMSE) by comparing predicted values from the external function with actual values stored in a separate table. Trigger a Python UDF, deployed as a Snowflake stored procedure, to initiate retraining if the RMSE exceeds the threshold.
  • B. Build a Snowpark Python application deployed on Snowflake which periodically polls the external function's performance by querying the function with a sample data set and comparing results to ground truth stored in Snowflake. Initiate retraining directly from the Snowpark application if performance degrades.
  • C. Create a view that joins the input features with the predicted output and the actual result. Configure model monitoring within the AWS Sagemaker to perform continuous validation of the model.
  • D. Utilize Snowflake's Alerting feature, setting an alert rule based on the output of a SQL query that calculates performance metrics. Configure the alert action to invoke a webhook that triggers a retraining pipeline.
  • E. Implement custom logging within the AWS Lambda function to capture prediction results and actual values. Configure AWS CloudWatch to monitor these logs and trigger an AWS Step Function that initiates a new training job and updates the Snowflake external function with the new model endpoint upon completion.

Answer: A,D,E

Explanation:
Options A, B, and C all represent valid approaches. A uses Snowflake Tasks, SQL queries for metrics, and UDFs/stored procedures for retraining. B uses AWS Lambda logging, CloudWatch, and Step Functions to orchestrate retraining. C leverages Snowflake's Alerting feature and webhooks. D, while technically possible, is not scalable as polling an external function from Snowpark introduces unnecessary latency and overhead. E is partially correct; however Sagemaker can't directly validate data with the actual result in Snowflake. Therefore, we must use alerting or tasks within snowflake.


NEW QUESTION # 78
You are developing a real-time fraud detection system using Snowflake and an external function. The system involves scoring incoming transactions against a pre-trained TensorFlow model hosted on Google Cloud A1 Platform Prediction. The transaction data resides in a Snowflake stream. The goal is to minimize latency and cost. Which of the following strategies are most effective to optimize the interaction between Snowflake and the Google Cloud A1 Platform Prediction service via an external function, considering both performance and cost?

  • A. Batch multiple transactions from the Snowflake stream into a single request to the external function. The external function then sends the batched transactions to the Google Cloud A1 Platform Prediction service in a single request. This increases throughput but might introduce latency.
  • B. Invoke the external function for each individual transaction in the Snowflake stream, sending the transaction data as a single request to the Google Cloud A1 Platform Prediction service.
  • C. Implement a caching mechanism within the external function (e.g., using Redis on Google Cloud) to store frequently accessed model predictions, thereby reducing the number of calls to the Google Cloud A1 Platform Prediction service. This requires managing cache invalidation.
  • D. Use a Snowflake pipe to automatically ingest the data from the stream, and then trigger a scheduled task that periodically invokes a stored procedure to train the model externally.
  • E. Implement asynchronous invocation of the external function from Snowflake using Snowflake's task functionality. This allows Snowflake to continue processing transactions without waiting for the response from the Google Cloud A1 Platform Prediction service, but requires careful monitoring and handling of asynchronous results.

Answer: A,C,E

Explanation:
Options B, C and E are correct. Caching (B) reduces calls to the external prediction service, minimizing both latency and cost, especially for redundant transactions. Batching (C) amortizes the overhead of invoking the external function and reduces the number of API calls to Google Cloud, improving throughput. Asynchronous invocation (E) allows Snowflake to continue processing without waiting, improving responsiveness. Option A is incorrect, as it will be a very slow and costly process. Option D mentions training the model which is unrelated to the prediction goal and would involve different steps involving the external function and model training.


NEW QUESTION # 79
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