DSA-C03 RELIABLE TEST VCE | DSA-C03 DUMPS DOWNLOAD

DSA-C03 Reliable Test Vce | DSA-C03 Dumps Download

DSA-C03 Reliable Test Vce | DSA-C03 Dumps Download

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q143-Q148):

NEW QUESTION # 143
A data scientist is analyzing website traffic data stored in Snowflake. The data includes daily page views for different pages. The data scientist suspects that the variance of page views for a particular page, 'home', has significantly increased recently. Which of the following steps and Snowflake SQL queries could be used to identify a potential change in the variance of 'home' page views over time (e.g., comparing variance before and after a specific date)? Select all that apply.

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

Answer: A,B,D,E

Explanation:
Options B, C, D and E are correct. Option B directly compares the variance before and after a date, allowing for a direct assessment of change. Option C uses a window function for a rolling variance calculation, revealing trends over time. Option D creates a histogram, which helps visualize the distribution and identify shifts in spread. Option E calculates standard deviation before and after a date. Option A, while calculating the overall variance, doesn't provide insight into changes over time.


NEW QUESTION # 144
You are tasked with building a data pipeline using Snowpark Python to process customer feedback data stored in a Snowflake table called FEEDBACK DATA'. This table contains free-text feedback, and you need to clean and prepare this data for sentiment analysis. Specifically, you need to remove stop words, perform stemming, and handle missing values. Which of the following code snippets and strategies, potentially used in conjunction, provide the most effective and performant solution for this task within the Snowpark environment?

  • A. Use a Python UDF that utilizes the NLTK library to remove stop words and perform stemming on the feedback text. Handle missing values by replacing them with an empty string using the .fillna(")' method on the Snowpark DataFrame after applying the UDF.
  • B. Leverage Snowflake's built-in string functions within SQL to remove common stop words based on a predefined list. Use a Snowpark DataFrame to execute this SQL transformation. For stemming, research and deploy a Java UDF implementing stemming algorithms, then chain it within a Snowpark transformation pipeline. Replace missing values with the string 'N/A' during the DataFrame construction using 'na.fill('N/A')'.
  • C. Implement all data cleaning tasks within a single SQL stored procedure including removing stop words using REPLACE functions, stemming using a custom lookup table, and handling NULL values using COALESC Call this stored procedure from Snowpark for Python.
  • D. Utilize Snowpark's 'call_function' with a Java UDF pre-loaded into Snowflake, which removes stop words and performs stemming with libraries like Lucene. Missing values can be handled with SQL's 'NVL' function during the initial data extraction into a Snowpark DataFrame.
  • E. Load the FEEDBACK DATA' table into a Pandas DataFrame using perform stop word removal and stemming using libraries like spacy or NLTK, handle missing values using Pandas' 'fillna()' method. Then, convert the cleaned Pandas DataFrame back into a Snowpark DataFrame. Use vectorization of text column in dataframe after above step

Answer: B,D

Explanation:
Options B and C provide the most effective and performant solutions.Option B leverages a combination of SQL and Java UDF to efficiently handle different parts of the cleaning process. The use of Snowflake's built-in string functions for removing stop words in SQL is efficient for common stop words, and Java UDF provides a more flexible and potentially more efficient solution for stemming. DataFrame .na.fill' is the most appropriate way to fill the missing values during the DataFrame creation. Option C: Utilizes pre-loaded Java UDFs for word processing, combined with SQL's NVL for missing value handling, is a strategy to leverage different components of Snowflake for performance and efficiency.Option A: While Python UDFs are flexible, they can be less performant than SQL or Java UDFs, especially for large datasets. Loading entire dataframe is an anti pattern. Also using .fillna on the dataframe instead of on the dataframe construction will reduce the performance. Option D: Loading all data into pandas is a bad habit and might reduce the performance. Also vectorization is not appropriate for cleaning the data. Option E: Stored procedures can be performant, relying solely on nested REPLACE functions for stop word removal can be cumbersome, and difficult to maintain compared to other approaches.


NEW QUESTION # 145
You've trained a model using Snowflake ML and want to deploy it for real-time predictions using a Snowflake UDF. To ensure minimal latency, you need to optimize the UDF's performance. Which of the following strategies and considerations are most important when creating and deploying a UDF for model inference in Snowflake to minimize latency, especially when the model is large (e.g., > 100MB)?
Select all that apply.

  • A. Use a Snowflake Stage to store the model file and load the model within the UDF using 'snowflake.snowpark.files.SnowflakeFile' to minimize memory footprint.
  • B. Utilize a Snowflake external function instead of a UDF if the model requires access to resources outside of Snowflake's environment.
  • C. Ensure the UDF code is written in Python and utilizes vectorized operations with libraries like NumPy to process data in batches efficiently.
  • D. Use smaller warehouse size for UDF evaluation in order to reduce latency and compute costs.
  • E. Store the trained model as a BLOB within the UDF code itself to avoid external dependencies.

Answer: A,C

Explanation:
Options A and C are the most important strategies. Option A: Vectorized operations in Python using libraries like NumPy can significantly improve the performance of UDFs, especially for large datasets. Option C: Storing the model in a Snowflake Stage and loading it within the UDF helps manage memory usage efficiently, especially when dealing with large models. Option B is not recommended as embedding large BLOB data within UDF code increases UDF size. Option D: External functions introduce additional latency due to the need to communicate with external resources. Option E is incorrect because smaller warehouses may lead to longer processing times.


NEW QUESTION # 146
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. 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.
  • B. 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.
  • C. Use transient tables to store the customer data after PII is obfuscated, drop the table and reload new data daily.
  • D. Grant 'SELECT privileges on the 'CUSTOMER DATA' table to all data scientists, and rely on them to avoid querying PII columns directly.
  • 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: A,B

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 # 147
You are using Snowpark Feature Store to manage features for your machine learning models. You've created several Feature Groups and now want to consume these features for training a model. To optimize retrieval, you want to use point-in-time correctness. Which of the following actions/configurations are essential to ensure point-in-time correctness when retrieving features using Snowpark Feature Store?

  • A. Create an associated Stream on the source tables used for Feature Groups
  • B. Explicitly specify a in the call.
  • C. When creating Feature Groups, specify a 'timestamp_key' that represents the event timestamp of the data in the source tables.
  • D. Ensure that all source tables used by the Feature Groups have Change Data Capture (CDC) enabled.
  • E. Use the method on the Feature Store client, providing a dataframe containing the 'primary_keyS and the desired for each record.

Answer: C,E

Explanation:
Options B and C are correct. B: Specifying a 'timestamp_key' during Feature Group creation is crucial for enabling point-in-time correctness. This tells the Feature Store which column represents the event timestamp. C: The method is specifically designed for point-in-time lookups. It requires a dataframe containing primary keys and the desired timestamp for each lookup. This enables the Feature Store to retrieve the feature values as they were at that specific point in time. Option A is incorrect, while enabling CDC is valuable for incremental updates, it does not guarantee point-in-time correctness without specifying the timestamp key and retrieving historical features using that key. Option D is not necessary, streams enable incremental loads but are separate from point in time. Option E, is not needed, its implicit via using .


NEW QUESTION # 148
......

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