更新するDSA-C03日本語資格取得試験-試験の準備方法-効率的なDSA-C03合格資料DSA-C03試験ダンプは効率的かつ先鋭的に設計されているため、ユーザーはセクションを完了した後、タイムリーに学習効果を確認できます。 DSA-C03クイズガイドの成功率に関する優れた実践は、知識を習得したことを完全に示しているわけではありません。したがって、DSA-C03テスト資料は、ユーザーが学習コンテンツをできるだけ多く統合することを可能にします、しかしそれは良い統合知識の結果を達成することができます。さらに重要なことは、DSA-C03試験に合格し、夢のDSA-C03認定を取得できることです。 Snowflake SnowPro Advanced: Data Scientist Certification Exam 認定 DSA-C03 試験問題 (Q110-Q115):質問 # 110
You are building a customer churn prediction model for a telecommunications company. You have a 'CUSTOMER DATA' table with a 'MONTHLY SPENDING' column that represents the customer's monthly bill amount. You want to binarize this column to create a feature indicating whether a customer is a 'High Spender' or 'Low Spender'. You decide that customers spending more than $75 are 'High Spenders'. Which of the following Snowflake SQL statements is the most efficient and correct way to achieve this, considering performance and readability, while avoiding potential NULL values in the resulting binarized column?
A. Option E
B. Option C
C. Option D
D. Option B
E. Option A
正解：D
解説：
The 'IIF function in Snowflake provides a concise and efficient way to perform binarization based on a condition. It is generally faster than a 'CASE' statement. Options A and D are valid, but IIF is generally more performant. Option C is incorrect as it returns -1, 0, or 1 which are not the desired binary values. Option E using NVL is unnecessarily verbose and may not be as efficient as option B.
質問 # 111
You are building an image classification model within Snowflake to categorize satellite imagery based on land use types (residential, commercial, industrial, agricultural). The images are stored as binary data in a Snowflake table 'SATELLITE IMAGES. You plan to use a pre-trained convolutional neural network (CNN) from a library like TensorFlow via Snowpark Python UDFs. The model requires images to be resized and normalized before prediction. You have a Python UDF named that takes the image data and model as input and returns the predicted class. What steps are crucial to ensure optimal performance and scalability of the image classification process within Snowflake, considering the volume and velocity of incoming satellite imagery?
A. Implement image resizing and normalization directly within the 'classify_image' Python UDF using libraries like OpenCV. Ensure the UDF is vectorized to process images in batches and leverage Snowpark's optimized data transfer capabilities.
B. Load the entire 'SATELLITE IMAGES table into the UDF for processing, allowing the UDF to handle all image resizing, normalization, and classification tasks sequentially.
C. Use a combination of Snowpark Python UDFs for preprocessing tasks like resizing and normalization, and leverage Snowflake's GPU-accelerated warehouses (if available) to expedite the inference step within the 'classify_image' UDF. Ensure the model weights are efficiently cached.
D. Pre-process the images outside of Snowflake using a separate data pipeline and store the resized and normalized images in a new Snowflake table before running the 'classify_image' UDE
E. Utilize Snowflake's external functions to call an image processing service hosted on AWS Lambda or Azure Functions for image resizing and normalization, then pass the processed images to the 'classify_image' UDF.
正解：A、C
解説：
Options B and E represent the most effective strategies. Option B emphasizes in-database processing with a vectorized 'DF and optimized data transfer. Option E highlights the use of 'DFs for preprocessing and leverages GPU acceleration for the computationally intensive inference step, along with efficient model weight caching. Option A introduces unnecessary complexity with external functions, which can add latency. Option C requires additional data storage and management outside of the core classification process. Option D is inefficient because loading the entire table into the 'DF is not scalable and will likely cause performance issues. Vectorizing the 'DF allows for batch processing, which significantly improves throughput. GPU acceleration further enhances the speed of model inference, and caching the model prevents repeated loading, saving computational resources.
質問 # 112
You've built a customer churn prediction model in Snowflake, and are using the AUC as your primary performance metric. You notice that your model consistently performs well (AUC > 0.85) on your validation set but significantly worse (AUC < 0.7) in production. What are the possible reasons for this discrepancy? (Select all that apply)
A. Your training and validation sets are not representative of the real-world production data due to sampling bias.
B. The AUC metric is inherently unreliable and should not be used for model evaluation.
C. The production environment has significantly more missing data compared to the training and validation environments.
D. There's a temporal bias: the customer behavior patterns have changed since the training data was collected.
E. Your model is overfitting to the validation data. This causes to give high performance on validation set but less accurate in the real world.
正解：A、C、D、E
解説：
A, B, C, and D are all valid reasons for performance degradation in production. Sampling bias (A) means the training/validation data doesn't accurately reflect the production data. Temporal bias (B) arises when customer behavior changes over time. Overfitting (C) leads to good performance on the training/validation set but poor generalization to new data. Missing data (D) can negatively impact the model's ability to make accurate predictions. AUC is a reliable metric, especially when combined with other metrics, so E is incorrect.
質問 # 113
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. 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.
B. Use Snowflake's Data Marketplace to supplement the existing data with external datasets, regardless of their relevance to the churn prediction problem.
C. 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.
D. Directly use all available columns from each table without any preprocessing to avoid introducing bias.
E. Randomly select a subset of data from each table to reduce computational complexity and speed up model training.
正解：A
解説：
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).
質問 # 114
You are using a Snowflake Notebook to build a churn prediction model. You have engineered several features, and now you want to visualize the relationship between two key features: and , segmented by the target variable 'churned' (boolean). Your goal is to create an interactive scatter plot that allows you to explore the data points and identify any potential patterns.
Which of the following approaches is most appropriate and efficient for creating this visualization within a Snowflake Notebook?
A. Leverage Snowflake's native support for Streamlit within the notebook to create an interactive application. Query the data directly from Snowflake within the Streamlit app and use Streamlit's plotting capabilities for visualization.
B. Use the 'snowflake-connector-python' to pull the data and use 'seaborn' to create static plots.
C. Create a static scatter plot using Matplotlib directly within the Snowflake Notebook by converting the data to a Pandas DataFrame. This involves pulling all relevant data into the notebook's environment before plotting.
D. Write a stored procedure in Snowflake that generates the visualization data in a specific format (e.g., JSON) and then use a JavaScript library within the notebook to render the visualization.
E. Use the Snowflake Connector for Python to fetch the data, then leverage a Python visualization library like Plotly or Bokeh to generate an interactive plot within the notebook.
正解：A
解説：
Option D, leveraging Snowflake's native support for Streamlit, is the most appropriate and efficient approach. Streamlit allows you to build interactive web applications directly within the notebook, querying data directly from Snowflake and using Streamlit's built-in plotting capabilities (or integrating with other Python visualization libraries). This avoids pulling large amounts of data into the notebook's environment, which is crucial for large datasets. Option A is inefficient due to the data transfer overhead and limited interactivity. Option B can work but is not as streamlined as using Streamlit within the Snowflake environment. Option C will create static plots only. Option E is overly complex and less efficient than using Streamlit.
