DSA-C03최신인증시험공부자료 - DSA-C03유효한공부

Snowflake DSA-C03 덤프에 대한 자신감이 어디서 시작된것이냐고 물으신다면Snowflake DSA-C03덤프를 구매하여 시험을 패스한 분들의 희소식에서 온다고 답해드리고 싶습니다. 저희Snowflake DSA-C03덤프는 자주 업데이트되고 오래된 문제는 바로 삭제해버리고 최신 문제들을 추가하여 고객님께 가장 정확한 덤프를 제공해드릴수 있도록 하고 있습니다.

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DSA-C03유효한 공부, DSA-C03학습자료

IT업계의 치열한 경쟁속에 살아 남으려면 자신의 능력을 증명하여야 합니다. 국제승인을 받는 IT인증자격증을 많이 취득하시면 취직이든 승진이든 이직이든 모든 면에서 이득을 볼수 있습니다. 최근 Snowflake인증 DSA-C03시험에 도전하는 분이 많은데 KoreaDumps에서 Snowflake인증 DSA-C03시험에 대비한 가장 최신버전 덤프공부가이드를 제공해드립니다.

최신 SnowPro Advanced DSA-C03 무료샘플문제 (Q230-Q235):

질문 # 230
You are working with a large dataset of transaction data in Snowflake to identify fraudulent transactions. The dataset contains millions of rows and includes features like transaction amount, location, time, and user ID. You want to use Snowpark and SQL to identify potential outliers in the 'transaction amount' feature. Given the potential for skewed data and varying transaction volumes across different locations, which of the following data profiling and feature engineering techniques would be the MOST effective at identifying outlier transaction amounts while considering the data distribution and location-specific variations?

A. Apply a clustering algorithm (e.g., DBSCAN) using Snowpark ML to the transaction data, using transaction amount, location and time as features. Treat data points in small, sparse clusters as outliers. This approach does not need to be performed for each location, just the entire dataset.
B. Calculate the mean and standard deviation of the 'transaction amount' feature for the entire dataset using SQL. Identify outliers as transactions with amounts that fall outside of 3 standard deviations from the mean.
C. Use Snowflake's APPROX_PERCENTILE function with Snowpark to calculate percentiles of the 'transaction amount' feature. Transactions with amounts in the top and bottom 1% are flagged as outliers.
D. Use Snowpark to calculate the interquartile range (IQR) of the 'transaction amount' feature for the entire dataset. Identify outliers as transactions with amounts that fall below QI - 1.5 IQR or above Q3 + 1.5 IQR.
E. Partition the data by location using Snowpark. For each location, calculate the median and median absolute deviation (MAD) of the 'transaction amount' feature. Identify outliers as transactions with amounts that fall outside of the median +/- 3 MAD for that location.

정답：A,E

설명：
Options C and E are the most effective for identifying outliers, considering the skewed nature of transaction data and location-specific variations. The IQR is better than mean and Standard Deviation. The MAD is more robust to outliers compared to standard deviation, which may be inflated by extreme values. Partitioning by location allows for a more nuanced identification of outliers specific to each location. DBSCAN is a great option to include with the partitioning because it considers transaction amount, location, and time as a factor in determine whether the data is an outlier. A and B are less effective because the median and standard deviation are sensitive to extreme values, and the IQR will not consider other dimensions such as location and time. D is only okay because it does not consider the impact of location on determining outliers.

질문 # 231
You are evaluating a binary classification model's performance using the Area Under the ROC Curve (AUC). You have the following predictions and actual values. What steps can you take to reliably calculate this in Snowflake, and which snippet represents a crucial part of that calculation? (Assume tables 'predictions' with columns 'predicted_probability' (FLOAT) and 'actual_value' (BOOLEAN); TRUE indicates positive class, FALSE indicates negative class). Which of the below code snippet should be used to calculate the 'True positive Rate' and 'False positive Rate' for different thresholds

A. The best way to calculate AUC is to randomly guess the probabilities and see how it performs.
B. Export the 'predicted_probability' and 'actual_value' columns to a local Python environment and calculate the AUC using scikit-learn.
C. The AUC cannot be reliably calculated within Snowflake due to limitations in SQL functionality for statistical analysis.
D. Calculate AUC directly within a Snowpark Python UDF using scikit-learn's function. This avoids data transfer overhead, making it highly efficient for large datasets. No further SQL is needed beyond querying the predictions data.
E. Using only SQL, Create a temporary table with calculated True Positive Rate (TPR) and False Positive Rate (FPR) at different probability thresholds. Then, approximate the AUC using the trapezoidal rule.

정답：D,E

설명：
Options A and C are correct. Option A demonstrates calculating AUC directly within Snowflake using a Snowpark Python UDF and scikit-learn's . This is efficient for large datasets as it avoids data transfer. Option C correctly outlines the process of calculating TPR and FPR using SQL and approximating AUC using the trapezoidal rule, another viable approach within Snowflake. Option B is incorrect; AUC can be calculated reliably within Snowflake. Option D is inefficient due to data transfer. Option E is blatantly incorrect.

질문 # 232
A retail company, 'GlobalMart,' wants to optimize its product placement strategy in its physical stores. They have transactional data stored in Snowflake, capturing which items are purchased together in the same transaction. They aim to use association rule mining to identify frequently co-occurring items. Given the following simplified transactional data in a Snowflake table named 'SALES TRANSACTIONS:

Which of the following SQL-based approaches, combined with Snowpark Python for association rule generation (using a library like 'mlxtend'), would be the MOST efficient and scalable way to prepare this data for association rule mining, specifically focusing on converting it into a transaction-item matrix suitable for algorithms like Apriori? Assume 'spark' is a 'snowpark.Session' object connected to your Snowflake environment.

A. Employing a custom UDF (User-Defined Function) written in Java or Scala that directly processes the transactional data within Snowflake and outputs the transaction-item matrix in a format suitable for Snowpark. This offloads processing to compiled code within Snowflake, maximizing performance.
B. Using Snowpark's 'DataFrame.groupBy(V and functions to aggregate items by transaction ID, then pivoting the data using to create the transaction-item matrix. This approach requires loading all data into the Snowpark DataFrame before pivoting.
C. Utilizing Snowflake's SQL function within a stored procedure to concatenate items purchased in each transaction into a string, then processing the string using Python in Snowpark to create the transaction-item matrix. This approach minimizes data transfer but introduces string parsing overhead in Python.
D. First extracting all the data from snowflake into pandas dataframe and then use pivoting and other pandas operations to convert to the needed format.
E. Creating a temporary table in Snowflake using a SQL query that aggregates items by transaction and represents them in a format suitable for Snowpark's 'mlxtend' library. Then load this temporary table into a Snowpark DataFrame and use it as input to the Apriori algorithm.

정답：B

설명：
Option A is the most efficient and scalable approach because Snowpark DataFrames are designed to handle large datasets efficiently within the Snowflake environment. Using 'groupBy(V, "agg()", and 'pivot()' allows Snowflake's engine to perform the data transformation in parallel and at scale. While option B avoids loading all the data, the string parsing in Python introduces overhead and potential scalability issues. Option C, while potentially performant, adds complexity to the solution. Option D can be a viable interim step, but performing the pivoting and aggregation directly within the Snowpark DataFrame is generally more streamlined. Option E is not efficient because it loads the data into pandas which is not scalable for big datasets.

질문 # 233
You are developing a regression model in Snowflake using Snowpark to predict house prices based on features like square footage, number of bedrooms, and location. After training the model, you need to evaluate its performance. Which of the following Snowflake SQL queries, used in conjunction with the model's predictions stored in a table named 'PREDICTED PRICES, would be the most efficient way to calculate the Root Mean Squared Error (RMSE) using Snowflake's built-in functions, given that the actual prices are stored in the 'ACTUAL PRICES' table?

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

정답：A

설명：
Option D is the most efficient and correct way to calculate RMSE. RMSE is the square root of the average of the squared differences between predicted and actual values. - p.predicted_price), 2)' calculates the squared difference. calculates the average of these squared differences. calculates the square root of the average, resulting in the RMSE. Option A is less efficient because it requires creating a temporary table. Option B and E are incorrect since they uses 'MEAN' which is unavailable in Snowflake and Exp/ln will return geometic mean instead of RMSE. Option C calculates the standard deviation of the differences, not the RMSE.

질문 # 234
You are building a model to predict loan defaults using a dataset stored in Snowflake. After training your model and calculating residuals, you create a scatter plot of the residuals against the predicted values. The plot shows a cone-shaped pattern, with residuals spreading out more as the predicted values increase. Which of the following SQL queries, run within a Snowpark Python session, could be used to address the underlying issue indicated by this residual pattern, assuming the predicted values are stored in a column named and the residuals in a column named 'loan_default_residuar in a Snowflake table named 'loan_predictionds'?

정답：C

설명：
A cone-shaped pattern in the residuals plot (heteroscedasticity) indicates that the variance of the errors is not constant. Applying a transformation like Box-Cox to the target variable before retraining the model (Option D) is the most appropriate way to address this. Option A attempts to filter outliers based on the residuals, but does not address the heteroscedasticity itself and requires statistical functions unavailable within standard SQL. Option B attempts to take the natural log of the residuals, which is nonsensical as residuals can be negative. Option C attempts to filter based on the rank of residuals, which is similarly unhelpful, does not fix the problem, and uses inappropriate outlier removal with SQL QUALIFY clause. Option E scaling the features might sometimes improve model performance, but it does not directly address heteroscedasticity.

질문 # 235
......

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Snowflake인증 DSA-C03시험은 IT인증자격증중 가장 인기있는 자격증을 취득하는 필수시험 과목입니다, DSA-C03덤프는 PDF버전 , Testing Engine버전 , Online Test Engine 버전 세가지 버전으로 되어있습니다, 경험이 풍부한 IT전문가들이 연구제작해낸 DSA-C03 최신덤프자료는 시험패스율이 100%에 가까워 시험의 첫번째 도전에서 한방에 시험패스하도록 도와드립니다, 구매후 일년무료 업데이트 서비스를 제공해드리기에 DSA-C03시험문제가 변경되어도 업데이트된 덤프를 받으면 가장 최신시험에 대비할수 있습니다, KoreaDumps의Snowflake인증 DSA-C03덤프는 이해하기 쉽고 모든Snowflake인증 DSA-C03시험유형이 모두 포함되어 있어 덤프만 잘 이해하고 공부하시면 시험패스는 문제없습니다.

사실 뇌물이기도 합니다, 난 유나 씨 아픈 것만 생각했지, 곤란한 것까지 생각 못 했어요, Snowflake인증 DSA-C03시험은 IT인증자격증중 가장 인기있는 자격증을 취득하는 필수시험 과목입니다, DSA-C03덤프는 PDF버전 , Testing Engine버전 , Online Test Engine 버전 세가지 버전으로 되어있습니다.

DSA-C03최신 인증시험 공부자료 시험덤프공부자료

경험이 풍부한 IT전문가들이 연구제작해낸 DSA-C03 최신덤프자료는 시험패스율이 100%에 가까워 시험의 첫번째 도전에서 한방에 시험패스하도록 도와드립니다, 구매후 일년무료 업데이트 서비스를 제공해드리기에 DSA-C03시험문제가 변경되어도 업데이트된 덤프를 받으면 가장 최신시험에 대비할수 있습니다.

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