検証するDSA-C03日本語復習赤本 &合格スムーズDSA-C03無料試験 |素敵なDSA-C03最新知識

ちなみに、ShikenPASS DSA-C03の一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=166sD1eRdP07IpfEqWLGmSLU1N1ayRSZh

DSA-C03試験シミュレーションのコンテンツシステムは、専門家によって構築されています。 DSA-C03学習教材のアフターサービスも専門家によって提供されます。製品の使用中に問題が発生した場合は、いつでも入手できます。 DSA-C03準備の質問を選択すると、プロフェッショナルサービスにより、最適な方法でそれを使用し、それを最大限に活用し、最高の学習結果をもたらすことができます。弊社のDSA-C03学習教材は、作成の最初の段階で、認定資格を取得するための専門的な態度を持っています。

ShikenPASSのDSA-C03問題集は素晴らしい参考資料です。この問題集は絶対あなたがずっと探しているものです。これは受験生の皆さんのために特別に作成し出された試験参考書です。この参考書は短い時間で試験に十分に準備させ、そして楽に試験に合格させます。試験のためにあまりの時間と精力を無駄にしたくないなら、ShikenPASSのDSA-C03問題集は間違いなくあなたに最もふさわしい選択です。この資料を使用すると、あなたの学習効率を向上させ、多くの時間を節約することができます。

>> DSA-C03日本語復習赤本 <<

信頼できるDSA-C03日本語復習赤本 & 合格スムーズDSA-C03無料試験 | 一番優秀なDSA-C03最新知識

これらの2つの特性により、DSA-C03ガイドトレントを使用するほぼすべての候補者が一度にテストに合格できることがわかります。これは自己決定ではありません。統計によると、当社のDSA-C03ガイドトレントは98％〜99％の高い合格率を達成しており、これは他のすべてをかなり上回る程度です。同時に、DSA-C03テストトレントが毎日更新されるかどうかを確認する専門スタッフがいます。メールでお問い合わせいただく場合でも、オンラインでお問い合わせいただく場合でも、できるだけ早く問題を解決できるようサポートいたします。心配する必要はまったくありません。

Snowflake SnowPro Advanced: Data Scientist Certification Exam 認定 DSA-C03 試験問題 (Q144-Q149):

質問 # 144
You've trained a binary classification model in Snowflake to predict loan defaults. You need to understand which features are most influential in the model's predictions for individual loans. Which of the following methods provide insight into model explainability, AND how can they be leveraged within the Snowflake environment? (Select all that apply)

A. Decision Tree visualization: Convert the model to decision trees and visualize it.
B. LIME (Local Interpretable Model-agnostic Explanations): Can be implemented by creating a UDF (User-Defined Function) in Snowflake that takes a loan's feature values as input and returns the feature importance scores for that specific loan, based on the LIME algorithm applied to the model's predictions.
C. Coefficient analysis: By inspecting the coefficients of a linear model, we can easily determine feature importances.
D. Permutation Feature Importance: Directly supported within Snowflake ML's model evaluation functions, allowing you to rank features based on their impact on model performance when their values are randomly shuffled.
E. SHAP (SHapley Additive explanations): Similar to LIME, SHAP values can be calculated using a Snowflake UDF, providing a more comprehensive and theoretically grounded explanation of each feature's contribution to the prediction, considering all possible feature combinations.

正解：B、E

解説：
LIME and SHAP are valid techniques. While Snowflake ML might directly support permutation feature importance through built-in functions for model evaluation in future releases (A), currently implementing LIME or SHAP via UDFs provides granular, instance-level explainability. Coefficient analysis (D) only work for linear models, and converting an arbitrary model to decision tree (E) would result in a bad approximation.

質問 # 145
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. Increase the memory allocation for the Snowpark Pandas worker nodes to accommodate the entire dataset.
B. 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.
C. 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.
D. Break the Snowpark DataFrame into smaller chunks using 'sample' and 'unionAll', process each chunk with Snowpark Pandas, and then combine the results.
E. 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.

正解：B、E

解説：
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.

質問 # 146
You are working with a dataset of customer transaction logs stored in Snowflake. Due to legal restrictions, you are unable to directly access or analyze the entire dataset. However, you can query aggregate statistics. You need to estimate the standard error of the mean transaction amount using bootstrapping. Knowing that you cannot retrieve the individual transaction amounts directly, which of the following approaches, while technically feasible within Snowflake and its stored procedure capabilities, is the least appropriate and potentially misleading application of bootstrapping?

A. Even without individual transaction data, bootstrapping is fundamentally impossible in this scenario, as bootstrapping requires resampling from the original data . All given options are therefore equally inappropriate.
B. Construct a stored procedure that uses the available aggregated statistics (e.g., mean, standard deviation, and sample size) to generate bootstrap samples based on an assumed parametric distribution (e.g., gamma or log-normal) fitted to the data, and then estimate the standard error from these resamples.
C. Use the available aggregate statistics to create many synthetic datasets, all adhering to the same mean, variance, and total sample size. Then, compute the statistic of interest (mean transaction amount) for each of these synthetic datasets, and use this collection to estimate the standard error. This is a valid approach.
D. Develop a stored procedure that generates random samples from a normal distribution with the same mean and standard deviation as the aggregated transaction data available to you, then calculates the standard error of the mean from these synthetic resamples.
E. Attempt to apply the central limit theorem rather than bootstrapping.

正解：D

解説：
Option A is the least appropriate. Generating random samples from a normal distribution with the same mean and standard deviation as the aggregated data, fundamentally violates the principle of bootstrapping. Bootstrapping relies on resampling from the original data to approximate the sampling distribution of a statistic. Creating data from a pre-defined distribution removes the inherent characteristics of the true data generating process and produces potentially very misleading results. Option B, using a parametric distribution, while still based on assumptions, is slightly better than A as it attempts to fit a distribution to the known data characteristics, but still relies on potentially incorrect distribution assumptions. Option C is not correct. Even the most inappropriate usage will give an answer. Option D is a valid approach, but it not Bootstrapping. Option E follows the basic idea of bootstrapping.

質問 # 147
You are tasked with creating a new feature in a machine learning model for predicting customer lifetime value. You have access to a table called 'CUSTOMER ORDERS which contains order history for each customer. This table contains the following columns: 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. To improve model performance and reduce the impact of outliers, you plan to bin the 'ORDER AMOUNT' column using quantiles. You decide to create 5 bins, effectively creating quintiles. You also want to create a derived feature indicating if the customer's latest order amount falls in the top quintile. Which of the following approaches, or combination of approaches, is most appropriate and efficient for achieving this in Snowflake? (Choose all that apply)

A. Create a temporary table storing quintile information, then join this table to original table to find the top quintile order amount.
B. Calculate the 20th, 40th, 60th, and 80th percentiles of the 'ORDER AMOUNT' using 'APPROX PERCENTILE or 'PERCENTILE CONT and then use a 'CASE statement to assign each order to a quantile bin. Calculate and see if on that particular date is in top quintile.
C. Use the window function to create quintiles for 'ORDER AMOUNT and then, in a separate query, check if the latest 'ORDER AMOUNT for each customer falls within the NTILE that represents the top quintile.
D. Use a Snowflake UDF (User-Defined Function) written in Python or Java to calculate the quantiles and assign each 'ORDER AMOUNT to a bin. Later you can use other statement to check the top quintile amount from result set.
E. Use 'WIDTH_BUCKET function, after finding the boundaries of quantile using 'APPROX_PERCENTILE' or 'PERCENTILE_CONT. Using MAX(ORDER to determine recent amount is in top quantile.

正解：B、C、E

解説：
Options A, B, and E are valid and efficient approaches. Option A using 'NTILE' is a direct and efficient way to create quantile bins within Snowflake SQL, and can find the most recent order date for customer with a case statement. Option B calculates the percentiles directly and then uses a CASE statement to assign bins. This is also efficient for explicit boundaries. Option E finds the boundaries of the quantile using 'APPROX_PERCENTILE or 'PERCENTILE_CONT , after that you can use 'WIDTH_BUCKET to categorize into quantile bins based on ranges. Option C is possible but generally less efficient due to the overhead of UDF execution and data transfer between Snowflake and the UDF environment. Option D is valid, but creating a temporary table adds complexity and potentially reduces performance compared to window functions or direct quantile calculation within the query.

質問 # 148
A data scientist is analyzing website click-through rates (CTR) for two different ad campaigns. Campaign A ran for two weeks and had 10,000 impressions with 500 clicks. Campaign B also ran for two weeks with 12,000 impressions and 660 clicks. The data scientist wants to determine if there's a statistically significant difference in CTR between the two campaigns. Assume the population standard deviation is unknown and unequal for the two campaigns. Which statistical test is most appropriate to use, and what Snowflake SQL code would be used to approximate the p-value for this test (assume 'clicks_b' , and are already defined Snowflake variables)?

A. An independent samples t-test, because we are comparing the means of two independent samples. Snowflake code: SELECT
B. Az-test, because we know the population standard deviation. Snowflake code: 'SELECT normcdf(clicks_a/impressions_a - clicks_b/impressions_b, O, 1)'
C. A one-sample t-test, because we are comparing the sample mean of campaign A to the sample mean of campaign Snowflake code: 'SELECT t_test_lsamp(clicks_a/impressions_a - clicks_b/impressions_b, 0)'
D. A paired t-test, because we are comparing two related samples over time. Snowflake code: 'SELECT t_test_ind(clicks_a/impressions_a, 'VAR EQUAL-TRUE')
E. An independent samples t-test (Welch's t-test), because we are comparing the means of two independent samples with unequal variances. Snowflake code (approximation using UDF - assuming UDF 'p_value_from_t_stat' exists that calculates p-value from t-statistic and degrees of freedom):

正解：A

解説：
The correct answer is E. Since we're comparing the means of two independent samples (Campaign A and Campaign B) and the population standard deviations are unknown, an independent samples t-test is appropriate. Because the problem stated that the variances are unequal, Welch's t-test provides a more accurate p-value and confidence intervals. The Snowflake function handles independent samples and the 'VAR_EQUAL=FALSE' parameter specifies that the variances should not be assumed to be equal. The other options are incorrect because they use inappropriate tests given the problem conditions. The z-test is not appropriate because the population standard deviations are unknown. A paired t-test is for related samples. A one sample test is to test one mean against a constant not another mean.

質問 # 149
......

DSA-C03認証試験に合格したいのは簡単ではなく、いい復習方法は必要です。我々はあなたに詳しい問題と答えがあるDSA-C03問題集を提供します。この問題集は我々の経験がある専門家たちによって開発されています。我々のすばらしいDSA-C03問題集はお客様の試験への成功を確保することができます。

DSA-C03無料試験: https://www.shikenpass.com/DSA-C03-shiken.html

我々のDSA-C03テスト学習資料を購入すると、仕事に有用の色々な実用的な知識を勉強します、DSA-C03テストの質問で提供されるサービスは、非常に具体的かつ包括的なものです、ShikenPASSは最も質の良いSnowflakeのDSA-C03問題集を提供できるし、君の認定試験に合格するのに大変役に立ちます、Snowflake DSA-C03日本語復習赤本あなたが購入する前に、無料のデモを使用することができます、お客様はDSA-C03試験を受ける時、たくさんの知識を学ぶことができます、彼らは仕事に忙しく、試験準備に時間がないので、我々のDSA-C03予備資料とDSA-C03試験問題集は彼らに最適です、Snowflake DSA-C03日本語復習赤本私たちの教材は常にユーザーのために考慮されています。

柔らかな肌を斑に染める鱗は、赤黒い色といい大きさといい、ともすれば痣か皮膚病のようだ、僕と違ってモテモテなんじゃないの、我々のDSA-C03テスト学習資料を購入すると、仕事に有用の色々な実用的な知識を勉強します。

DSA-C03試験の準備方法｜便利なDSA-C03日本語復習赤本試験｜一番優秀なSnowPro Advanced: Data Scientist Certification Exam無料試験

DSA-C03テストの質問で提供されるサービスは、非常に具体的かつ包括的なものです、ShikenPASSは最も質の良いSnowflakeのDSA-C03問題集を提供できるし、君の認定試験に合格するのに大変役に立ちます、あなたが購入する前に、無料のデモを使用することができます。

お客様はDSA-C03試験を受ける時、たくさんの知識を学ぶことができます。

BONUS！！！ ShikenPASS DSA-C03ダンプの一部を無料でダウンロード：https://drive.google.com/open?id=166sD1eRdP07IpfEqWLGmSLU1N1ayRSZh

Jim Clark Jim Clark

Biography

検証するDSA-C03日本語復習赤本 &合格スムーズDSA-C03無料試験 |素敵なDSA-C03最新知識

信頼できるDSA-C03日本語復習赤本 & 合格スムーズDSA-C03無料試験 | 一番優秀なDSA-C03最新知識

Snowflake SnowPro Advanced: Data Scientist Certification Exam 認定 DSA-C03 試験問題 (Q144-Q149):

DSA-C03試験の準備方法｜便利なDSA-C03日本語復習赤本試験｜一番優秀なSnowPro Advanced: Data Scientist Certification Exam無料試験

Courses

Quick Links

Contact