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Snowflake DSA-C03資料 - DSA-C03熱門題庫

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Snowflake 認證對於具體IT工作職位提供了一個嚴格的技術資格評定方法(筆試或/和操作考試)。對於雇員來說，增加了更多事業機會，對於雇主來說，意味著更強的競爭力。DSA-C03 認證的特色在於基於工作職責的技術綱要，該綱要為使你在你的特定IT領域脫潁而出需要掌控的技術提供了明確又合理的標準。Snowflake DSA-C03 的認證在業界具有很強的權威性，是IT界認可並仰慕的一種專業技術認證。目前 Snowflake 的熱門認證有 DSA-C03 等！

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>> Snowflake DSA-C03資料 <<

Snowflake DSA-C03熱門題庫 - DSA-C03考古題介紹

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最新的 SnowPro Advanced DSA-C03 免費考試真題 (Q280-Q285):

問題 #280
You're a data scientist analyzing sensor data from industrial equipment stored in a Snowflake table named 'SENSOR READINGS' The table includes 'TIMESTAMP' , 'SENSOR ID', 'TEMPERATURE', 'PRESSURE', and 'VIBRATION'. You need to identify malfunctioning sensors based on outlier readings in 'TEMPERATURE' , 'PRESSURE' , and 'VIBRATION'. You want to create a dashboard to visualize these outliers and present a business case to invest in predictive maintenance. Select ALL of the actions that are essential for both effectively identifying sensor outliers within Snowflake and visualizing the data for a business presentation. (Multiple Correct Answers)

• A. Calculate Z-scores for 'TEMPERATURE, 'PRESSURE, and 'VIBRATION' for each 'SENSOR_ID within a rolling window of the last 24 hours using Snowflake's window functions. Define outliers as readings with Z-scores exceeding a threshold (e.g., 3).
• B. Implement a clustering algorithm (e.g., DBSCAN) within Snowflake using Snowpark Python to group similar sensor readings, identifying outliers as points that do not belong to any cluster or belong to very small clusters.
• C. Create a Snowflake stored procedure to automatically flag outlier readings in a new column 'IS OUTLIER based on a predefined rule set (e.g., IQR method or Z-score threshold), and then use this column to filter data for visualization in a dashboard.
• D. Directly connect the 'SENSOR_READINGS' table to a visualization tool and create a 3D scatter plot with 'TEMPERATURE, 'PRESSURE, and 'VIBRATION' on the axes, without any pre-processing or outlier detection in Snowflake.
• E. Calculate basic statistical summaries (mean, standard deviation, min, max) for each sensor and each variable C TEMPERATURE, 'PRESSURE, and 'VIBRATION') and use that information to filter down to the most important sensor, prior to using the other techniques.

答案：A,B,C,E

解題說明：
Options A, C, D, and E are essential. A (Z-score calculation with rolling window) provides a dynamic measure of how unusual a reading is relative to recent history for each sensor. C (DBSCAN clustering) helps identify outliers based on density; points far from any cluster are likely outliers. D (Stored procedure with outlier flagging) automates the outlier detection process and makes it easy to filter and visualize outliers in a dashboard, with a business ready explanation. Option E allows you to focus on the right data, allowing you to have a more useful visualisation. Option B (direct 3D scatter plot without pre-processing) is not effective because it will be difficult to identify outliers visually in a high- density scatter plot without any outlier detection or data reduction. The direct scatter plot becomes overwhelming very quickly with sensor data.

 

問題 #281
A data scientist is analyzing website conversion rates for an e-commerce platform. They want to estimate the true conversion rate with 95% confidence. They have collected data on 10,000 website visitors, and found that 500 of them made a purchase. Given this information, and assuming a normal approximation for the binomial distribution (appropriate due to the large sample size), which of the following Python code snippets using scipy correctly calculates the 95% confidence interval for the conversion rate? (Assume standard imports like 'import scipy.stats as St' and 'import numpy as np').

• A.
• B.
• C.
• D.
• E.

答案：B,C

解題說明：
Options A and E are correct. Option A uses the 'scipy.stats.norm.intervar function correctly to compute the confidence interval for a proportion. Option E manually calculates the confidence interval using the standard error and the z-score for a 95% confidence level (approximately 1.96). Option B uses the t-distribution which is unnecessary for large sample sizes and is inappropriate here given the context. Option C is not the correct way to calculate the confidence interval for proportion using binomial distribution interval function, it calculates range of values in dataset, instead of confidence interval. Option D uses incorrect standard deviation.

 

問題 #282
You are a data scientist working for an e-commerce company. You have a table named 'sales_data' with columns 'product_id' , customer_id' , 'transaction_date' , and 'sale_amount'. You need to identify the top 5 products by total sale amount for each month. Which of the following Snowflake SQL queries is the MOST efficient and correct way to achieve this, while also handling potential ties in sale amounts?

• A.
• B.
• C.
• D.
• E.

答案：D,E

解題說明：
Options C and E are correct. Both use a subquery to calculate the rank of each product within each month's sales, then filter for the top 5 products. The main difference is that option C uses DENSE_RANK(), which assigns consecutive ranks even if there are ties in sales amount (resulting in more than 5 products being selected if there are ties for the 5th position), while option E uses RANK(), which assigns the same rank to tied values but can skip ranks. Option A is incorrect because it attempts to filter using HAVING on a ranking calculated within the same query level, which is not allowed in many SQL implementations (and can be logically incorrect). Options B and D are incorrect as they employ ROW_NUMBER() and NTILE(5) respectively. ROW_NUMBER will not handle ties correctly, while NTILE just divides the data into 5 groups without explicitly identifying the 'top' 5. Option A uses a rank function inside the HAVING clause which is often syntactically invalid.

 

問題 #283
You are deploying a machine learning model to Snowflake using a Python UDF. The model predicts customer churn based on a set of features. You need to handle missing values in the input data'. Which of the following methods is the MOST efficient and robust way to handle missing values within the UDF, assuming performance is critical and you don't want to modify the underlying data tables?

• A. Use within the UDF, replacing missing values with a global constant (e.g., 0) defined outside the UDF. This constant is pre-calculated based on the training dataset's missing value distribution.
• B. Use within the UDF to forward fill missing values. This assumes the data is ordered in a meaningful way, allowing for reasonable imputation.
• C. Implement a custom imputation strategy using 'numpy.where' within the UDF, basing the imputation value on a weighted average of other features in the row.
• D. Pre-process the data in Snowflake using SQL queries to replace missing values with the mean for numerical features and the mode for categorical features before calling the UDF.
• E. Raise an exception within the UDF when a missing value is encountered, forcing the calling application to handle the missing values.

答案：D

解題說明：
Pre-processing data in Snowflake with SQL for imputation offers several advantages. It allows leveraging Snowflake's compute resources for data preparation, rather than the UDF's limited resources. Handling missing values before the UDF call also simplifies the UDF code, making it more efficient and less prone to errors. Using 'fillna' within the UDF (options A, B, and C) can lead to performance bottlenecks and potential data leakage issues if not carefully managed. Raising an exception (option E) is not practical for production deployments where missing values are expected.

 

問題 #284
You are preparing a dataset in Snowflake for a K-means clustering algorithm. The dataset includes features like 'age', 'income' (in USD), and 'number of_transactions'. 'Income' has significantly larger values than 'age' and 'number of_transactions'. To ensure that all features contribute equally to the distance calculations in K-means, which of the following scaling approaches should you consider, and why? Select all that apply:

• A. Apply RobustScaler to handle outliers and then StandardScaler or MinMaxScaler to further scale the features.
• B. Apply MinMaxScaler to all three features to scale them to a range between O and 1 .
• C. Do not scale the data, as K-means is robust to differences in feature scales.
• D. Apply StandardScaler to all three features ('age', 'income', 'number_of_transactions') to center the data around zero and scale it to unit variance.
• E. Apply PowerTransformer to transform income and StandardScaler to other features to handle skewness.

答案：A,B,D

解題說明：
K-means clustering is sensitive to the scale of the features because it relies on distance calculations. Features with larger values will have a disproportionate influence on the clustering results. StandardScaler centers the data around zero and scales it to unit variance, which ensures that all features have a similar range and variance. MinMaxScaler scales the features to a range between O and 1, which also addresses the issue of different scales. RobustScaler handles outliers which will then use the other two scaling techniques. Therefore A, B and D are the appropriate scaling techniques. C is not correct as K-means relies on distance calculations and not scaling the data could give some feature a larger weight which isn't the desired outcome. Option E: Using PowerTransformer on 'income' to reduce skewness and StandardScaler on the other features can be a valid approach, but it depends on the distribution of 'income' and the presence of outliers. If 'income' is highly skewed and/or contains outliers, this combination might be more effective than using StandardScaler or MinMaxScaler alone.

 

問題 #285
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