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NEW QUESTION # 69
You are building a model deployment pipeline using a CI/CD system that connects to your Snowflake data warehouse from your external IDE (VS Code) and orchestrates model training and deployment. The pipeline needs to dynamically create and grant privileges on Snowflake objects (e.g., tables, views, warehouses) required for the model. Which of the following security best practices should you implement when creating and granting privileges within the pipeline?

• A. Use the role within the pipeline script to create and grant all necessary privileges.
• B. Hardcode the credentials of a highly privileged user (e.g., a user with the SECURITYADMIN role) in the pipeline script for authentication.
• C. Create a custom role with minimal required privileges to perform only the necessary operations for the pipeline, and grant this role to a dedicated service account used by the pipeline.
• D. Grant the 'OWNERSHIP' privilege on all objects to the service account so it can perform any operation.
• E. Grant the ' SYSADMIN' role to the service account used by the pipeline to ensure it has sufficient privileges.

Answer: C

Explanation:
The principle of least privilege dictates that the pipeline should only have the minimum necessary privileges to perform its tasks. Creating a custom role with only the required privileges and granting it to a dedicated service account is the most secure approach. Using 'ACCOUNTADMIN' (Option A) or 'SYSADMIN' (Option C) grants excessive privileges. Hardcoding credentials (Option D) is a major security vulnerability. Granting 'OWNERSHIP (Option E) is generally not necessary and grants excessive control. This follows the principle of least privilege which is essential for secure Snowflake deployments. A dedicated role ensures that the pipeline cannot inadvertently perform actions outside of its intended scope.

NEW QUESTION # 70
A pharmaceutical company is testing a new drug to lower blood pressure. They conduct a clinical trial with 200 patients. After treatment, the sample mean reduction in systolic blood pressure is 10 mmHg, with a sample standard deviation of 15 mmHg. You want to construct a 99% confidence interval for the true mean reduction in systolic blood pressure. Which of the following statements is most accurate concerning the appropriate distribution and critical value to use?

• A. Use a chi-squared distribution with 199 degrees of freedom.
• B. Use a t-distribution with 200 degrees of freedom, and the critical value is close to 2.576.
• C. Use a z-distribution because the sample size is large (n > 30), and the critical value is approximately 2.576.
• D. Use a t-distribution with 199 degrees of freedom, and the critical value is slightly larger than 2.576.
• E. Use a z-distribution because we are estimating mean, and use a critical value of 1.96.

Answer: D

Explanation:
The correct answer is B. While the sample size is considered 'large' (n > 30), it's more accurate to use a t-distribution when the population standard deviation is unknown and estimated by the sample standard deviation. The t-distribution accounts for the added uncertainty from estimating the standard deviation. The degrees of freedom are n-1 = 199. The critical value for a 99% confidence interval with a t-distribution and 199 degrees of freedom will be slightly larger than the z-score of 2.576. Option A is incorrect because using t-distribution is slightly better. Option C is incorrect because chi-squared distribution is for variance/standard deviation. Option D is incorrect since 1.96 is z score for 95%. Option E is incorrect as the degrees of freedom should be n-1.

NEW QUESTION # 71
A data scientist is developing a fraud detection model using Snowpark ML on Snowflake. They have a feature engineering pipeline implemented as a Snowpark DataFrame transformation. The pipeline includes several complex UDFs. The data scientist observes that the pipeline execution is slow. What are the most effective techniques to optimize the feature engineering pipeline's performance in Snowpark?

• A. Reduce the size of the input DataFrame by sampling the data.
• B. Disable Snowpark's lazy evaluation by executing on the DataFrame after each transformation.
• C. Rewrite Python UDFs as vectorized Python UDFs using the 'pandas' API within Snowpark to leverage batch processing.
• D. Replace Python UDFs with Snowflake SQL UDFs where possible, as SQL UDFs often offer better performance due to Snowflake's optimization capabilities.
• E. Cache intermediate DataFrames using or 'persist()' to avoid recomputation of common transformations.

Answer: C,D,E

Explanation:
Caching intermediate results (B) prevents redundant calculations. Vectorized Python UDFs (C) using pandas enhance performance by processing data in batches. Snowflake SQL UDFs (E) can often outperform Python UDFs due to Snowflake's internal optimizations. Sampling (A) might reduce accuracy. Disabling lazy evaluation (D) negates the benefits of Snowpark's query optimization.

NEW QUESTION # 72
You are performing exploratory data analysis on a large sales dataset in Snowflake using Snowpark. The dataset contains columns such as 'order_id', , and 'profit'. You want to identify the top 5 most profitable products for each month. You have already created a Snowpark DataFrame named 'sales_df. Which of the following Snowpark operations, when combined correctly, will efficiently achieve this?

• A. Use 'ntile(5)' partitioned by ordered by 'sum(profit) DESC' after grouping by and 'product_id', and aggregating 'sum(profit)'.
• B. Group by and 'product_id' , aggregate 'sum(profit)' , then use partitioned by ordered by 'sum(profit) DESC'.
• C. Group by 'product_id', aggregate 'sum(profity, then use partitioned by ordered by 'sum(profit) DESC' within a UDF.
• D. First, create a temporary table with aggregated monthly profit for each product using SQL. Then, use Snowpark to read the temporary table and apply a window function partitioned by ordered by 'sum(profit) DESC'.
• E. Use 'rank()' partitioned by ordered by 'sum(profit) DESC' , after grouping by and 'product_id' , and aggregating 'sum(profity.

Answer: B

Explanation:
Option A correctly describes the process. First group by month and product to calculate total profit, then use with correct partitioning and ordering to assign a rank within each month based on profit. Options B and C use less efficient ranking functions. Option D groups by product globally, missing the monthly granularity. Option E 'ntile' divides products into 5 buckets which is not what we are looking for.

NEW QUESTION # 73
A data scientist needs to analyze website session data stored in a Snowflake table named 'WEB SESSIONS'. The table contains columns like 'SESSION D', 'USER_ID, 'PAGE_VIEWS', 'TIME SPENT_SECONDS', and 'TIMESTAMP. They want to identify potential bot traffic by analyzing the correlation between 'PAGE VIEWS' and 'TIME SPENT SECONDS'. Which of the following Snowflake SQL queries is the MOST efficient and statistically sound way to calculate the Pearson correlation coefficient between these two columns, handling potential NULL values appropriately?

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

Answer: B

Explanation:
The 'CORR function in Snowflake directly calculates the Pearson correlation coefficient and implicitly handles NULL values by excluding rows where either input is NULL. Option A is incorrect because it does not explicitly filter NULL values, though the 'CORR' function itself handles it, Option B is mathematically correct but less concise. Option C uses 'APPROX CORR, which is useful for large datasets where approximate results are acceptable, but for a general scenario without size constraints, 'CORR is preferred for accuracy. While Option E correctly calculates the correlation coefficient using covariance and standard deviation, it uses approximation functions which may impact accuracy without a necessary tradeoff.

NEW QUESTION # 74
You are working with a large dataset of sensor readings stored in a Snowflake table. You need to perform several complex feature engineering steps, including calculating rolling statistics (e.g., moving average) over a time window for each sensor. You want to use Snowpark Pandas for this task. However, the dataset is too large to fit into the memory of a single Snowpark Pandas worker. How can you efficiently perform the rolling statistics calculation without exceeding memory limits? Select all options that apply.

• A. Use the 'grouped' method in Snowpark DataFrame to group the data by sensor ID, then download each group as a Pandas DataFrame to the client and perform the rolling statistics calculation locally. Then upload back to Snowflake.
• B. Break the Snowpark DataFrame into smaller chunks using 'sample' and 'unionAll', process each chunk with Snowpark Pandas, and then combine the results.
• C. Increase the memory allocation for the Snowpark Pandas worker nodes to accommodate the entire dataset.
• D. Explore using Snowpark's Pandas user-defined functions (UDFs) with vectorization to apply custom rolling statistics logic directly within Snowflake. UDFs allow you to use Pandas within Snowflake without needing to bring the entire dataset client-side.
• E. Utilize the 'window' function in Snowpark SQL to define a window specification for each sensor and calculate the rolling statistics using SQL aggregate functions within Snowflake. Leverage Snowpark to consume the results of the SQL transformation.

Answer: D,E

Explanation:
Explanation:Options B and D are the most appropriate and efficient solutions for handling large datasets when calculating rolling statistics with Snowpark Pandas. Option B uses the 'window' function in Snowpark SQL. Leverage the 'window' function in Snowpark SQL to define a window specification for each sensor and calculate the rolling statistics using SQL aggregate functions within Snowflake. Option D uses Snowpark's Pandas UDFs. Snowpark's Pandas UDFs with vectorization allow you to bring the processing logic to the data within Snowflake, avoiding the need to move the entire dataset to the client-side and bypassing memory limitations. This approach is generally more scalable and performant for large datasets. Option A is inefficient as it retrieves groups of data from Snowflake to client side before creating the calculations before sending back to snowflake. Option C is correct but complex and not optimal. Option E is possible, but it's not a scalable solution and can be costly.

NEW QUESTION # 75
You are exploring a large dataset of website user behavior in Snowflake to identify patterns and potential features for a machine learning model predicting user engagement. You want to create a visualization showing the distribution of 'session_duration' for different 'user_segments'. The 'user_segmentS column contains categorical values like 'New', 'Returning', and 'Power User'. Which Snowflake SQL query and subsequent data visualization technique would be most effective for this task?

• A. Query: 'SELECT user_segments, AVG(session_duration) FROM user_behavior GROUP BY Visualization: Bar chart showing average session duration for each user segment.
• B. Query: ' SELECT COUNT( ) ,user_segments FROM user_behavior GROUP BY user_segments;' Visualization: Pie chart showing proportion of each segment.
• C. Query: 'SELECT session_duration FROM user_behavior WHERE user_segments = 'New';- (repeated for each user segment). Visualization: Overlayed histograms showing the distribution of session duration for each user segment on the same axes.
• D. Query: 'SELECT user_segments, MEDIAN(session_duration) FROM user_behavior GROUP BY user_segments;' Visualization: Box plot showing the distribution (quartiles, median, outliers) of session duration for each user segment.
• E. Query: 'SELECT user_segments, APPROX 0.25), APPROX 0.5), APPROX_PERCENTlLE(session_duration, 0.75) FROM user_behavior GROUP BY user_segments;' Visualization: Scatter plot where each point represents a user segment and the x,y coordinates represent session duration at 25th and 75th percentiles respectively.

Answer: D

Explanation:
Using the Median (option B) provides a better central tendency measure than the average (option A) when the data may have outliers. The box plot effectively visualizes the distribution, including quartiles and outliers. Option C involves generating separate queries and histograms, which is less efficient. Calculating quantiles using 'APPROX_PERCENTILE' (Option D) is good for large datasets, but the resulting scatter plot isn't the best way to show distribution. Pie chart does not show distrubution but proportions.

NEW QUESTION # 76
You are a data scientist working for a retail company. You've been tasked with identifying fraudulent transactions. You have a Snowflake table named 'TRANSACTIONS' with columns 'TRANSACTION ID', 'AMOUNT', 'TRANSACTION DATE', 'CUSTOMER ID', and 'LOCATION'. You suspect outliers in transaction amounts might indicate fraud. Which of the following SQL queries is the MOST efficient and appropriate to identify potential outliers using the Interquartile Range (IQR) method, and incorporate necessary data type considerations for robust percentile calculations? Consider also the computational cost associated with each approach on a large dataset.

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

Answer: A

Explanation:
Option B is the most efficient and readable. It calculates the IQR values (QI and Q3) once in a CTE (Common Table Expression) called IQR_Values' and then uses these values to filter the 'TRANSACTIONS table. The 'APPROX_PERCENTILE function is used for efficient approximation on large datasets. Using QUALIFY (option C) is syntactically valid but it can be less performant than using a CTE in this scenario, especially if the data requires significant scanning across multiple partitions or micro-partitions due to the window function. Option A, C and D are inefficient because they calculate the percentiles multiple times. Option E uses a JOIN, which although can be functionally correct, might be less clear than filtering within the CTE-based approach.

NEW QUESTION # 77
A team is using Snowflake to build a supervised machine learning model for image classification. The images are stored in a Snowflake table, and the labels are in a separate table. The goal is to train a model using Snowpark Python. Which of the following code snippets represents the MOST efficient way to join the image data with its corresponding labels, pre-process the images (resize and normalize), and prepare the data for model training using Snowpark DataFrame transformations? Assume contains image data as binary, 'label df contains the image labels, and 'resize normalize udf' is a UDF that handles resizing and normalization.

• A.
• B.
• C.
• D.
• E.

Answer: B,C

Explanation:
Options C and E represent the most efficient approaches using Snowpark DataFrames. Option C performs the join, preprocesses the images using the UDF, and selects the required columns, all within the Snowflake environment without pulling data to the client prematurely. It prepares the data for downstream tasks such as model training or saving to a new table. Option E enhances upon this by converting the Snowpark DataFrame to a Pandas DataFrame and then to NumPy arrays, which are common formats for machine learning libraries. This is a efficient way to perform complex transformations that are not readily available within the standard Snowpark API. Option A collects the entire DataFrame to the client, which is highly inefficient for large datasets. Option B uses RDDs (Resilient Distributed Datasets), which are an older Spark API and less efficient than DataFrames in Snowpark. Option D performs individual queries for each image ID, resulting in a large number of round trips to the database and is extremely inefficient. Option E also implicitly uses the power of pandas vectorized operations, leading to increased performance.

NEW QUESTION # 78
You are developing a data transformation pipeline in Python that reads data from Snowflake, performs complex operations using Pandas DataFrames, and writes the transformed data back to Snowflake. You've implemented a function, 'transform data(df)', which processes a Pandas DataFrame. You want to leverage Snowflake's compute resources for the DataFrame operations as much as possible, even for intermediate transformations before loading the final result. Which of the following strategies could you employ to optimize this process, assuming you have a configured Snowflake connection "conn"?

• A. Read the entire Snowflake table into a single Pandas DataFrame, apply , and then write the entire transformed DataFrame back to Snowflake.
• B. Create a series of Snowflake UDFs that perform the individual transformations within Snowflake, load the data into Pandas DataFrames, apply UDFs on these DataFrames, and use to upload to Snowflake.
• C. Use 'snowflake.connector.pandas_tools.write_pandas(conn, df, table_name, auto_create_table=Truey to write the transformed DataFrame to Snowflake and let Snowflake handle the transformations using SQL.
• D. Chunk the Snowflake table into smaller DataFrames using 'fetchmany()' , apply to each chunk, and then append each transformed chunk to a Snowflake table using multiple INSERT statements. Call columns=[col[0] for col in cur.description]))'
• E. Use Snowpark Python DataFrame API to perform the transformation directly on Snowflake's compute and then load results into the same table. Call 'df_snowpark = session.create_dataframe(df)'.

Answer: E

Explanation:
Snowpark for Python is specifically designed to push down DataFrame operations to the Snowflake engine for execution. Option C directly leverages Snowflake's compute resources for DataFrame transformations by creating a Snowpark DataFrame. Option A is inefficient as it loads the entire dataset into memory and performs transformations locally. Option B directly only handles write function . Option D involves manual chunking and multiple INSERT statements, which is slow and inefficient. Option E is overly complex and doesn't fully utilize Snowflake's capabilities; Snowpark provides a more seamless and efficient way to express DataFrame transformations within Snowflake. Using Snowpark eliminates the need for data transfer between Python environment and Snowflake for intermediate transformations which is more efficient and scalable for Data Scientist (DSA-C03) Certification Exam Model Development.

NEW QUESTION # 79
You are using Snowflake ML to train a binary classification model. After training, you need to evaluate the model's performance. Which of the following metrics are most appropriate to evaluate your trained model, and how do they differ in their interpretation, especially when dealing with imbalanced datasets?

• A. Accuracy: It measures the overall correctness of the model. Precision: It measures the proportion of positive identifications that were actually correct. Recall: It measures the proportion of actual positives that were identified correctly. Fl-score: It is the harmonic mean of precision and recall.
• B. AUC-ROC: Measures the ability of the model to distinguish between classes. It is less sensitive to class imbalance than accuracy. Log Loss: Measures the performance of a classification model where the prediction input is a probability value between 0 and 1.
• C. Mean Squared Error (MSE): The average squared difference between the predicted and actual values. R-squared: Represents the proportion of variance in the dependent variable that is predictable from the independent variables. These are great for regression tasks.
• D. Precision, Recall, F I-score, AUC-ROC, and Log Loss: Precision focuses on the accuracy of positive predictions; Recall focuses on the completeness of positive predictions; Fl-score balances Precision and Recall; AUC-ROC evaluates the separability of classes and Log Loss quantifies the accuracy of probabilities, especially valuable for imbalanced datasets because they provide a more nuanced view of performance than accuracy alone.
• E. Confusion Matrix: A table that describes the performance of a classification model by showing the counts of true positive, true negative, false positive, and false negative predictions. This isnt a metric but representation of the metrics.

Answer: D

Explanation:
Option E correctly identifies the most appropriate metrics (Precision, Recall, Fl-score, AUC-ROC, and Log Loss) for evaluating a binary classification model, especially in the context of imbalanced datasets. It also correctly describes the focus of each metric. Accuracy can be misleading with imbalanced datasets. MSE and R-squared are for regression problems (Option B). Confusion Matrix is a table, and Options D, contains incorrect statement.

NEW QUESTION # 80
You are building a data science pipeline in Snowflake to predict customer churn. The pipeline includes a Python UDF that uses a pre- trained scikit-learn model stored as a binary file in a Snowflake stage. The UDF needs to load this model for prediction. You've encountered an issue where the UDF intermittently fails, seemingly related to resource limits when multiple concurrent queries invoke the UDF. Which of the following strategies would best optimize the UDF for concurrency and resource efficiency, minimizing the risk of failure?

• A. Load the scikit-learn model outside the UDF function in the global scope of the module so that all invocations share the same loaded model instance. Use the 'context.getExecutionContext(Y to track execution, making sure it is thread safe.
• B. Load the scikit-learn model inside the UDF function on every invocation to ensure the latest version is used.
• C. Utilize Snowflake's session-level caching by storing the loaded model in 'session.get('model')' to be reused across multiple UDF calls within the same session. Reload the model if 'session.get('model')' is None.
• D. Implement a global, lazy-loaded cache for the scikit-learn model within the UDF's module. The model is loaded only once during the first invocation and shared across subsequent calls. Protect the loading process with a lock to prevent race conditions in concurrent environments.
• E. Increase the memory allocated to the Snowflake warehouse to accommodate multiple UDF invocations.

Answer: D

Explanation:
Option D provides the most efficient and robust solution. Loading the model only once (lazy loading) reduces overhead. A global cache ensures reusability. A lock is crucial to prevent race conditions during the initial loading in a concurrent environment. Option A is inefficient due to repeated loading. Option B is problematic because Snowflake UDFs do not directly support global variables in a thread-safe manner. Option C is incorrect as 'session.get' is not a valid Snowflake API for Python UDFs and lacks thread safety. Option E, while potentially helpful, doesn't address the underlying inefficiency of repeatedly loading the model.

NEW QUESTION # 81
A Data Scientist is designing a machine learning model to predict customer churn for a telecommunications company. They have access to various data sources, including call logs, billing information, customer demographics, and support tickets, all residing in separate Snowflake tables. The data scientist aims to minimize bias and ensure data quality during the data collection phase. Which of the following strategies would be MOST effective for collecting and preparing the data for model training?

• A. Randomly select a subset of data from each table to reduce computational complexity and speed up model training.
• B. Directly use all available columns from each table without any preprocessing to avoid introducing bias.
• C. Use Snowflake's Data Marketplace to supplement the existing data with external datasets, regardless of their relevance to the churn prediction problem.
• D. Perform exploratory data analysis (EDA) on each table to identify relevant features and potential biases. Use feature selection techniques to reduce dimensionality. Implement robust data validation checks to ensure data quality and consistency before joining the tables. Handle missing values strategically based on the specific column and its potential impact on the model.
• E. Create a single, wide table by performing a series of INNER JOINs on all tables using customer ID as the primary key. Handle missing values by imputing with the mean for numerical columns and 'Unknown' for categorical columns.

Answer: D

Explanation:
Option C is the MOST effective because it emphasizes a thorough and rigorous approach to data collection and preparation. It highlights the importance of EDA for identifying relevant features and biases, feature selection for dimensionality reduction, data validation for ensuring data quality, and strategic handling of missing values. This approach helps to minimize bias, improve model performance, and ensure the reliability of the churn prediction model. The other options are flawed because they either ignore potential biases and data quality issues (A), use a simplistic approach to handling missing values (B), compromise data representativeness (D), or introduce potentially irrelevant data (E).

NEW QUESTION # 82
A Snowflake table named 'SALES DATA contains a 'TRANSACTION DATE column stored as VARCHAR. The data in this column is inconsistent; some rows have dates in 'YYYY-MM-DD' format, others in 'MM/DD/YYYY' format, and some contain invalid date strings like 'N/A'. You need to standardize all dates to 'YYYY-MM-DD' format and store them in a new column called FORMATTED DATE in a new table 'STANDARDIZED_SALES DATA. Which of the following approaches, using Snowpark Python and SQL, most effectively handles these inconsistencies and minimizes errors during data transformation? Select all that apply:

• A. Using a Snowpark Python UDF to parse each date string individually, handling different formats with conditional logic, and returning a formatted date string. This provides flexibility in handling diverse date formats.
• B. Creating a view on top of 'SALES_DATA' that implements the conversion logic. This avoids creating a new physical table immediately and allows for experimentation with different conversion strategies before materializing the data.
• C. Using a series of DATE" and 'TO_VARCHAR SQL functions in Snowpark to attempt converting the date in different formats and then formatting the result to 'YYYY-MM-DD'. Any conversion failing returns NULL.
• D. Using a single 'TO_DATE function with format parameter set to 'AUTO' combined with 'TO_VARCHAR to format the date to 'YYYY-MM-DD'.
• E. Employing Snowpark's error handling mechanism (e.g., 'try...except' blocks) within a loop to iteratively convert each date string, catching and logging errors, and storing valid dates in a new column.

Answer: B,C

Explanation:
Options B and D are the most effective. Option B uses with different formats to handle inconsistencies. If a format fails, it returns NULL, providing a clean way to handle invalid dates. Combining this with VARCHAR formats the valid dates to 'YYYY-MM-DD'. Option D suggests creating a view. Views are useful for testing transformation logic without immediately impacting the base table, allowing experimentation before committing to a data transformation pipeline. Materializing the data into a table would be a subsequent step, after verifying the transformation's correctness. Option A, while flexible, is less performant because UDFs (User-Defined Functions) generally add overhead compared to built-in SQL functions. Option C is inefficient and not a recommended practice in Snowpark for vectorized operations. Option E will not work in most of the cases, as the AUTO parameter cannot reliably differentiate all provided formats. Furthermore, it does not account for data quality issues where there is no date format.

NEW QUESTION # 83
You are a data scientist working for a retail company that stores its transaction data in Snowflake. You need to perform feature engineering on customer purchase history data to build a customer churn prediction model. Which of the following approaches best combines Snowflake's capabilities with a machine learning framework (like scikit-learn) for efficient feature engineering? Assume your data is stored in a table named 'CUSTOMER TRANSACTIONS' with columns like 'CUSTOMER ID, 'TRANSACTION DATE, 'AMOUNT, and 'PRODUCT CATEGORY.

• A. Create a Snowflake external function that calls a cloud-based (AWS, Azure, GCP) machine learning service for feature engineering, passing the raw transaction data for each customer and processing the aggregated data into features in Snowflake SQL.
• B. Use Snowflake's SQL UDFs (User-Defined Functions) written in Python to perform feature engineering directly within Snowflake on smaller aggregated sets of data to optimize compute costs. Integrate these UDFs to query the entire 'CUSTOMER TRANSACTIONS table to build your features.
• C. Develop a custom Spark application to read data from Snowflake, perform feature engineering in Spark, and write the resulting features back to a new table in Snowflake, and avoid use of Snowflake SQL UDFs to minimize complexity.
• D. Extract all the data from 'CUSTOMER_TRANSACTIONS' into a Pandas DataFrame, perform feature engineering using Pandas and scikit-learn, and then load the processed data back into Snowflake.
• E. Load a small subset of 'CUSTOMER_TRANSACTIONS' into an in-memory database like Redis, perform feature engineering using custom Python scripts interacting with Redis, and periodically sync the results back to Snowflake.

Answer: B

Explanation:
Snowflake UDFs allow you to execute Python code directly within Snowflake. This is particularly useful for feature engineering, as it allows you to leverage Snowflake's compute power and data locality. Extracting all data to Pandas (Option A) can be inefficient for large datasets. External functions (Option C) introduce latency and complexity. Spark (Option D) adds an external dependency, and leveraging redis (Option E) increases operational overhead. Using UDFs allows you to push down the computation to the data, improving performance and reducing data transfer costs.

NEW QUESTION # 84
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