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様々な顧客のニーズに応じるために、我が社は三つのバージョンのSnowPro Advanced: Data Scientist Certification Exam関連勉強資料を作成しました。たとえば、メモ取り可能と読みやすいように印刷できるPDF版があります。本番試験をシミュレーションできるソフト版もあり、本番試験とまったく同じで事前に試験の流れと雰囲気を体験できます。自分の実力値が分かってから弱い部分に集中して勉強すればいい。電子設備を問わずスマートとIPADなどにインストールできるオンライン版もあり、オフライン使用をサポートします。携帯がたやすくてどこでも、いつでも勉強したければ可能になります。

Snowflake DSA-C03 試験シラバストピック:

セクション	比重	目標
トピック 1: データサイエンスの概念	10%–15%	- データサイエンスのワークフロー 1. 評価指標 2. モデルのライフサイクル 3. 実験の追跡 - 機械学習の概念 1. 教師あり学習 2. 強化学習 3. 教師なし学習
トピック 2: Snowflakeにおけるデータサイエンスのベストプラクティス	15%–20%	- セキュリティとガバナンス 1. データガバナンス 2. ロールベースのアクセス制御 - パフォーマンスの最適化 1. ウェアハウスのサイズ設定 2. クエリの最適化
トピック 3: モデル開発と機械学習	25%–30%	- モデルの学習 1. ハイパーパラメータ調整 2. 学習ワークフロー 3. 交差検証 - モデル評価 1. 回帰指標 2. モデルの説明可能性 3. 分類指標
トピック 4: 生成AIとLLMの機能	10%–15%	- AIガバナンス 1. 責任あるAI 2. AIモデルの監視 - SnowflakeにおけるGenAI 1. ベクトル埋め込み 2. LLM統合 3. プロンプトエンジニアリング
トピック 5: データ準備と特徴量エンジニアリング	25%–30%	- データ準備 1. データクレンジング 2. データ変換 3. 欠損値の処理 - 特徴量エンジニアリング 1. 特徴量抽出 2. 特徴量選択 3. 特徴量スケーリング

Snowflake SnowPro Advanced: Data Scientist Certification 認定 DSA-C03 試験問題：

1. You are building a churn prediction model for a telecommunications company using Snowflake and Snowpark ML. You have trained a Gradient Boosting Machine (GBM) model and want to understand the feature importance to identify key drivers of churn. You've used SHAP (SHapley Additive exPlanations) values to explain individual predictions. Given a customer with a high churn risk, you observe that the 'monthly_charges' feature has a significantly large negative SHAP value for that specific prediction. Which of the following statements best interprets this observation in the context of feature impact?

A) Increasing 'monthly_charges' for this customer is likely to increase their probability of churning.
B) The negative SHAP value indicates that 'monthly_charges' is negatively correlated with all customers' churn probability, irrespective of their individual profile.
C) Increasing 'monthly_charges' for this customer is likely to decrease their probability of churning.
D) The 'monthly_charges' feature has no impact on the customer's churn probability.
E) The negative SHAP value suggests 'monthly_charges' interacts with other features. Its precise impact is conditional and cannot be generalized without further analysis of feature interaction effects with SHAP values.

2. You're building a linear regression model in Snowflake to predict house prices. You have the following features: 'square_footage', 'number of bedrooms', 'location id', and 'year built'. 'location id' is a categorical variable representing different neighborhoods. You suspect that the relationship between 'square footage' and 'price' might differ based on the 'location id'. Which of the following approaches in Snowflake are BEST suited to explore and model this potential interaction effect?

A) Create interaction terms by multiplying 'square_footage' with one-hot encoded columns derived from 'location_id'. Include these interaction terms in the linear regression model.
B) Apply a power transformation to 'square_footage' before including it in the linear regression model. This correct, but only to one variable.
C) Use the 'QUALIFY clause in Snowflake SQL to filter the data based on 'location_id' before calculating regression coefficients. This is incorrect approach.
D) Fit separate linear regression models for each unique 'location_id', using 'square_footage', 'number_of_bedrooms', and 'year_built' as independent variables.
E) Create interaction terms by adding 'square_footage' and one-hot encoded columns derived from 'location_id'. Include these interaction terms in the linear regression model.

3. A financial services company wants to predict loan defaults. They have a table 'LOAN APPLICATIONS' with columns 'application_id', applicant_income', 'applicant_age' , and 'loan_amount'. You need to create several derived features to improve model performance.
Which of the following derived features, when used in combination, would provide the MOST comprehensive view of an applicant's financial stability and ability to repay the loan? Select all that apply

A) Requires external data from a credit bureau to determine total debt, then calculated as 'total_debt / applicant_income' (Assume credit bureau integration is already in place)
B) Calculated as 'applicant_age applicant_age'.
C) Calculated as 'applicant_age / applicant_income'.
D) Calculated as 'loan_amount I applicant_age' .
E) Calculated as 'applicant_income I loan_amount'.

4. You are building a fraud detection model using Snowflake and discover a severe class imbalance (99% legitimate transactions, 1% fraudulent). You plan to use down-sampling to address this. Which of the following strategies and Snowflake SQL commands would be MOST effective and efficient for down-sampling the majority class (legitimate transactions) in a large Snowflake table named 'TRANSACTIONS before training a model using Snowpark?

A) Create a new table 'BALANCED_TRANSACTIONS' by sampling the majority class and combining it with the minority class using 'UNION ALL'. Use the 'SAMPLE clause in Snowflake SQL for efficient sampling:

B) Use Snowpark's function with replacement to create a balanced dataset. This is efficient within the Snowpark environment but might be slower than native SQL sampling for initial data preparation.
C) Create a new table 'BALANCED TRANSACTIONS' by sampling the majority class and combining it with the minority class using UNION ALLS. Use the'SAMPLE clause in Snowflake SQL for efficient sampling:

D) Randomly delete rows from the 'TRANSACTIONS table where 'IS FRAUD = FALSE until the class distribution is balanced. This avoids data duplication but can be slow on large tables.
E) Manually iterate through the 'TRANSACTIONS' table using a Snowpark 'DataFrame' and randomly select rows from the majority class. This is the most efficient approach for very large tables.

5. 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.