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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と手法	20%	- データサイエンスのライフサイクル 1. 探索的データ分析 2. 課題の定義と要件の整理 3. データの収集と取得 - 統計学および数学の基礎知識 1. 評価指標 2. 確率論と統計学
トピック 2: 生成AIおよびLLMの機能	15%	- SnowflakeにおけるLLMの連携 1. 埋め込み表現とベクトル検索 2. プロンプトエンジニアリング - 生成AIの活用事例 1. テキスト生成と要約 2. 検索拡張生成
トピック 3: Snowflakeにおけるデータの前処理と特徴量エンジニアリング	25%	- データの取り込みと統合 1. データのクレンジングと変換 2. 構造化データおよび半構造化データの処理 - 特徴量エンジニアリングの手法 1. スケーリング、エンコーディング、正規化 2. Snowflakeの関数を活用した特徴量の加工 3. 特徴量の作成と選択
トピック 4: モデルのデプロイ、モニタリング、ガバナンス	15%	- モニタリングと保守 1. 性能の追跡 2. データドリフトおよびモデルドリフトの検知 - デプロイ戦略 1. バッチ推論およびリアルタイム推論 2. Snowflakeにおけるモデルの提供 - ガバナンスとコンプライアンス 1. セキュリティとアクセス制御 2. データの系統管理と監査
トピック 5: 機械学習モデルの開発と学習	25%	- モデルの種類と選定 1. 時系列モデル 2. 教師あり学習 3. 教師なし学習 - 学習と最適化 1. ハイパーパラメータの調整 2. モデルの検証とテスト 3. Snowflake MLおよびSnowparkの活用

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

1. You've trained a sales forecasting model using Snowpark ML and want to deploy it within Snowflake for real-time predictions. You've decided to store the predictions directly in a Snowflake table. The model predicts sales for different product categories based on historical data and promotional activities. Which of the following approaches is the MOST efficient and scalable way to store these predictions, considering a high volume of prediction requests and the need for quick retrieval for downstream dashboards?

A) Storing predictions in a single, wide table with all features and predictions as columns. No partitioning or clustering is implemented.
B) Storing predictions in a key-value store like Redis and referencing the keys from a Snowflake table. Requires external network access from Snowflake.
C) Storing predictions in a separate table with a composite key of product category and timestamp, with clustering on the timestamp column and partitioning by product category.
D) Storing predictions in an external stage (e.g., AWS S3) and querying them using an external table. The external table definition includes the sales prediction as a column.
E) Storing predictions in a VARIANT column in a single table. All prediction results for a given product category are stored as a JSON document within the VARIANT column.

2. You are deploying a fraud detection model using Snowpark Container Services. The model requires a substantial amount of GPU memory. After deploying your service, you notice that it frequently crashes due to Out-Of-Memory (OOM) errors. You have verified that the container image itself is not the source of the problem. Which of the following strategies are most appropriate to mitigate these OOM errors when using Snowpark Container Services, assuming you want to minimize costs and complexity?

A) Implement model parallelism across multiple containers, splitting the model's workload and data across them. Configure each container with a smaller 'container.resources.memory' allocation.
B) Ignore OOM errors and rely on the container service to automatically restart the container. The model will eventually process all requests.
C) Utilize CPU-based inference instead of GPU-based inference, as CPU inference is generally less memory-intensive. Convert the model to a format optimized for CPU inference (e.g., using ONNX). Reduce the 'container.resources.cpu' count.
D) Implement a mechanism within your model's inference code to explicitly free up unused memory after each prediction. Use Python's 'gc.collect()' and ensure proper cleanup of large data structures. Configure a smaller 'container.resources.memory' allocation.
E) Increase the 'container.resources.memory' configuration setting in the service definition to a value significantly larger than the model's memory footprint. Monitor memory utilization and adjust as needed.

3. You're building a fraud detection model and want to determine if the average transaction amount for fraudulent transactions is significantly higher than the average transaction amount for legitimate transactions. You have two tables in Snowflake:
'FRAUDULENT TRANSACTIONS and 'LEGITIMATE TRANSACTIONS, both with a 'TRANSACTION AMOUNT column. You believe that FRAUDULENT TRANSACTIONS contains fewer than 30 transactions. You don't know the population standard deviations. What are the proper steps to conduct the hypothesis test, and what is the correct hypothesis statement?

A) Perform a Z-test. Null Hypothesis: The average transaction amount for fraudulent transactions is less than or equal to the average transaction amount for legitimate transactions. Alternative Hypothesis: The average transaction amount for fraudulent transactions is greater than the average transaction amount for legitimate transactions.
B) Perform a t-test. Null Hypothesis: The average transaction amount for fraudulent transactions is less than or equal to the average transaction amount for legitimate transactions. Alternative Hypothesis: The average transaction amount for fraudulent transactions is greater than the average transaction amount for legitimate transactions.
C) Perform a Z-test. Null Hypothesis: The average transaction amount for fraudulent transactions is equal to the average transaction amount for legitimate transactions. Alternative Hypothesis: The average transaction amount for fraudulent transactions is not equal to the average transaction amount for legitimate transactions.
D) Perform a t-test. Null Hypothesis: The average transaction amount for fraudulent transactions is equal to the average transaction amount for legitimate transactions. Alternative Hypothesis: The average transaction amount for fraudulent transactions is not equal to the average transaction amount for legitimate transactions.
E) Perform a chi-squared test. Null Hypothesis: There is no relationship between transaction amount and whether a transaction is fraudulent. Alternative Hypothesis: There is a relationship between transaction amount and whether a transaction is fraudulent.

4. You are using the Snowflake Python connector from within a Jupyter Notebook running in VS Code to train a model. You have a Snowflake table named 'CUSTOMER DATA' with columns 'ID', 'FEATURE 1', 'FEATURE_2, and 'TARGET. You want to efficiently load the data into a Pandas DataFrame for model training, minimizing memory usage. Which of the following code snippets is the MOST efficient way to achieve this, assuming you only need 'FEATURE 1', 'FEATURE 2, and 'TARGET' columns?

A)

B)

C)

D)

E)

5. You are analyzing sensor data collected from industrial machines, which includes temperature readings. You need to identify machines with unusually high temperature variance compared to their peers. You have a table named 'sensor _ readings' with columns 'machine_id', 'timestamp', and 'temperature'. Which of the following SQL queries will help you identify machines with a temperature variance that is significantly higher than the average temperature variance across all machines? Assume 'significantly higher' means more than two standard deviations above the mean variance.