EXAM DSA-C03 REVIEW, DSA-C03 INTERACTIVE EBOOK

Exam DSA-C03 Review, DSA-C03 Interactive EBook

Exam DSA-C03 Review, DSA-C03 Interactive EBook

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Tags: Exam DSA-C03 Review, DSA-C03 Interactive EBook, Customizable DSA-C03 Exam Mode, Free DSA-C03 Sample, DSA-C03 Certification Questions

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q155-Q160):

NEW QUESTION # 155
You are deploying a time series forecasting model in Snowflake. You need to log the performance metrics (e.g., MAE, RMSE) of the model after each prediction run to the Snowflake Model Registry. Which of the following steps are necessary to achieve this?

  • A. Create a separate table in Snowflake to store the performance metrics and use SQL "INSERT statements to log the metrics after each prediction run.
  • B. Leverage Snowflake's Event Tables to capture and store metrics data generated during model evaluation and prediction workflows and then access via stored procedures that log to the Model Registry.
  • C. You must create a custom logging solution outside of Snowflake using external services and then integrate those logs back into Snowflake via external functions and Model Registry APIs
  • D. Use the method with the 'metrics' parameter to log the metrics directly during model registration.
  • E. Use the method to log individual metrics to the Model Registry associated with a specific model version after the prediction run.

Answer: B,D,E

Explanation:
Options A, C and D are correct. Option A: You can log metrics during model registration using the method with the 'metrics parameter. Option C: The method allows logging individual metrics associated with a model version after the prediction run. Option D: Event Tables are a good way to track and audit model usage and performance, allowing for capturing those logs. Logging to separate tables can be done, but is not as elegant. The preferred method is to use the model registry's functions. Option E, Custom logging solution requires additional overhead and complexity, when Snowflake provides native model registry logging features.


NEW QUESTION # 156
You are using Snowflake Cortex to perform sentiment analysis on customer reviews stored in a table called 'CUSTOMER REVIEWS' The table has a column containing the text of each review. You want to create a user-defined function (UDF) to extract sentiment score between the range of -1 to 1 using the 'snowflake_cortex.sentiment' function in Snowflake Cortex. Which of the following UDF definitions would correctly implement this, allowing it to be called directly on the column?

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

Answer: B

Explanation:
The 'snowflake_cortex.sentiment' function returns a VARIANT containing the sentiment score and sentiment label. To extract the sentiment score as a float, you need to access the 'sentiment_score' field and cast it to FLOAT or NUMBER data type using Option D does this correctly, using the Snowflake's preferred casting syntax.Option A return type is incorrect, where it returns the Variant instead of FLOAT. Option B return type is correct but it doesnt cast the result to Float, which is not correct syntax as result is VARIANT. Option C is incorrect because of TO_NUMBER function. Option E result is the SENTIMENT Label instead sentiment score.


NEW QUESTION # 157
You have trained a complex machine learning model using Snowpark for Python and are now preparing it for production deployment using Snowpark Container Services. You have containerized the model and pushed it to a Snowflake-managed registry. However, you need to ensure that only authorized users can access and deploy this model. Which of the following actions MUST you take to secure your model in the Snowflake Model Registry, ensuring appropriate access control, and minimizing the risk of unauthorized deployment or modification?

  • A. Create a custom role, grant the USAGE' privilege on the database and schema containing the model registry, grant the 'READ privilege on the registry, and then grant this custom role to only those users authorized to deploy the model. Consider masking sensitive model parameters using masking policies.
  • B. Store the model outside of Snowflake managed registry and use external authentication to control access.
  • C. Grant the 'USAGE privilege on the stage where the model files are stored to all users who need to deploy the model.
  • D. Grant the 'READ privilege on the container registry to all users who need to deploy the model. Create a custom role with the 'APPLY MASKING POLICY privilege and grant this role to the deployment team.
  • E. Grant the 'USAGE privilege on the database and schema containing the model registry, grant the 'READ privilege on the registry itself, and grant the EXECUTE TASK' privilege to the deployment team for the deployment task.

Answer: A

Explanation:
Option D is the correct answer because it provides the most secure and granular access control. 'USAGE on the database and schema allows access to the container registry. 'READ on the registry allows viewing of model metadata without modification. Creating a custom role and granting it to specific users limits access to only authorized personnel. Utilizing masking policies further secures sensitive parameters. Option A is incorrect because it does not control access to the registry itself. 'USAGE privilege on a stage alone is insufficient for managing model registry access. Option B is incorrect because 'APPLY MASKING POLICY is not relevant for controlling access to the model registry. Option C is partially correct, but 'EXECUTE TASK' grants unnecessary privileges related to task execution, which is beyond the scope of registry access. It also lacks fine-grained control over who can deploy. Option E is incorrect because while it offers security, it bypasses the advantages of using Snowflake's managed registry.


NEW QUESTION # 158
You are working with a Snowflake table named 'CUSTOMER DATA' that contains personally identifiable information (PII), including customer names, email addresses, and phone numbers. Your team needs to perform exploratory data analysis on this data to understand customer demographics and behavior. However, you must ensure that the PII is protected and that only authorized personnel can access the sensitive information. Which of the following strategies should you implement in Snowflake to achieve secure EDA?

  • A. Use transient tables to store the customer data after PII is obfuscated, drop the table and reload new data daily.
  • B. Grant 'SELECT privileges on the 'CUSTOMER DATA' table to all data scientists, and rely on them to avoid querying PII columns directly.
  • C. Create a view on top of that excludes the PII columns (e.g., name, email, phone). Grant 'SELECT privileges on this view to data scientists. Also implement data masking policies on the 'CUSTOMER DATA' table for the PII columns and grant 'SELECT on the table to specific roles requiring access to the masked values.
  • D. Create a copy of the 'CUSTOMER DATA table without the PII columns and grant 'SELECT' privileges on this copy to the data scientists. Use masking policies on the original table.
  • E. Apply dynamic data masking to the entire 'CUSTOMER_DATA' table, masking all columns by default, and provide decryption keys only to authorized users.

Answer: C,D

Explanation:
Options B and E are both valid strategies. Option B provides a view with non-PII data, while using masking policies on the table. Option E creates a copy of the 'CUSTOMER_DATR table and leverages masking on original table. Option A is insecure. Option C while obfuscating the PII, will lead to data loss and will be costly to move the data. Option D isn't practical, it would overly restrict access.


NEW QUESTION # 159
You're developing a Python UDTF in Snowflake to perform sentiment analysis on customer reviews. The UDTF uses a pre-trained transformer model from Hugging Face. The code is as follows:

When deploying this UDTF, you encounter a 'ModuleNotFoundError: No module named 'transformers" error. Considering best practices for managing dependencies in Snowflake UDTFs, what is the most effective way to resolve this issue?

  • A. Create a Conda environment containing the 'transformers' library, package it into a zip file, upload it to a Snowflake stage, and specify the stage path in the 'imports' parameter when registering the UDTF.
  • B. Install the 'transformers' library directly on the Snowflake compute nodes using Snowpark's 'add_packageS method at the session level:
  • C. Include the 'transformers' library in the same Python file as the UDTF definition. This is acceptable for smaller libraries.
  • D. Upload all the dependencies of Transformers (manually downloaded libraries) to the internal stage.
  • E. Use the 'snowflake-ml-python' library and its dependency management features to automatically resolve and deploy the 'transformers' dependency.

Answer: A

Explanation:
Option B is the recommended approach for managing dependencies like 'transformers' in Snowflake UDTFs. Creating a Conda environment ensures that all required libraries and their dependencies are packaged together, preventing version conflicts and ensuring reproducibility. Uploading the environment to a stage and specifying it in the 'imports' parameter makes the dependencies available to the UDTF during execution. Option A is incorrect because Snowpark's 'add_packageS is the ideal way for adding packages. Option C is impractical for large libraries like 'transformers'. Option D, although using snowflake-ml-python is valid, manually creating conda environment will reduce the depndency on other services. Option E is very tedious.


NEW QUESTION # 160
......

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