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DSA-C03資料 & DSA-C03認證考試解析

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DSA-C03認證考試解析 - DSA-C03最新考題

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最新的 SnowPro Advanced DSA-C03 免費考試真題 (Q37-Q42):

問題 #37
You are tasked with performing data profiling on a large customer dataset in Snowflake to identify potential issues with data quality and discover initial patterns. The dataset contains personally identifiable information (PII). Which of the following Snowpark and SQL techniques would be most appropriate to perform this task while minimizing the risk of exposing sensitive data during the exploratory data analysis phase?

A. Utilize Snowpark to create a sampled dataset (e.g., 1% of the original data) and perform all exploratory data analysis on the sample to reduce the data volume and potential exposure of PII.
B. Apply differential privacy techniques using Snowpark to add noise to the summary statistics generated from the customer data, masking the individual contributions of each customer while revealing overall trends.
C. Create a masked view of the customer data using Snowflake's dynamic data masking features. This view masks sensitive PII columns while allowing you to compute aggregate statistics and identify patterns using SQL and Snowpark functions. Columns like 'email' are masked using and columns like are masked using .
D. Directly query the raw customer data using SQL and Snowpark, computing descriptive statistics like mean, median, and standard deviation for all numeric columns and frequency counts for categorical columns. Store the results in a temporary table for further analysis.
E. Export the entire customer dataset to an external data lake for exploratory analysis using Spark and Python. Apply data masking in Spark before analysis.

答案：B,C

解題說明：
Options C and D provide the most secure and effective ways to perform exploratory data analysis while protecting PII. Differential privacy (C) ensures that aggregate statistics do not reveal too much information about individuals. Masked views (D) prevent direct access to sensitive data, replacing it with masked values during the analysis. A is dangerous because it exposes the raw data. B while reduces the volume, still exposes raw data. E is risky because it involves exporting sensitive data outside of Snowflake.

問題 #38
Consider the following Snowflake SQL query used to calculate the RMSE for a regression model's predictions, where 'actual_value' is the actual value and 'predicted value' is the model's prediction. However, you notice that the RMSE calculation is incorrect due to an error in the query. Identify the error in the query and provide the corrected query. The table name is 'sales_predictions'.

Which of the following options represents the corrected query that accurately calculates the RMSE?

答案：D

解題說明：
The original query uses 'AVG', which is perfectly valid. However, Snowflake also supports the 'MEAN' function, which is an alias for the 'AVG' function and is used for calculated descriptive statistics. The corrected query maintains the functionality and structure, only substituting AVG with MEAN. B and C options lack the square root function. Option D calculates the Mean Absolute Error (MAE), not the RMSE. Option E uses a window function unnecessarily.

問題 #39
You are a data scientist working with a Snowflake table named 'CUSTOMER DATA' that contains a 'PHONE NUMBER' column stored as VARCHAR. The 'PHONE NUMBER' column sometimes contains non-numeric characters like hyphens and parentheses, and in some rows the data is missing. You need to create a new table 'CLEANED CUSTOMER DATA' with a column named 'CLEANED PHONE NUMBER that contains only the numeric part of the phone number (as VARCHAR) and replaces missing or invalid phone numbers with NULL. Which of the following Snowpark Python code snippets achieves this most efficiently, ensuring no errors occur during the data transformation, and considers Snowflake's performance best practices?

A. Option E
B. Option B
C. Option D
D. Option C
E. Option A

答案：A

解題說明：
Option E is the most efficient because it leverages Snowpark's built-in functions for string manipulation and conditional logic directly. It first removes all non-numeric characters using 'regexp_replace' and then uses 'iff (if and only if) to replace empty strings (resulting from cleaning) with NULL. This approach avoids using UDFs (User-Defined Functions), which can introduce overhead. Option B, although using 'regexp_replace' , requires an additional 'with_column' to handle empty strings after cleaning. Option A introduces UDF that decreases performance. Option C calls UDF with undefined 'call_udf function and 'snowflake-snowpark-python' library. Option D is missing dataframe and its transformation is not happening on top of Dataframe. Option E is preferrable over Option B, as it uses the single transformation.

問題 #40
You are analyzing website clickstream data stored in Snowflake to identify user behavior patterns. The data includes user ID, timestamp, URL visited, and session ID. Which of the following unsupervised learning techniques, combined with appropriate data transformations in Snowflake SQL, would be most effective in discovering common navigation paths followed by users? (Choose two)

A. Principal Component Analysis (PCA) to reduce the dimensionality of the URL data, followed by hierarchical clustering. This will group similar URLs together.
B. K-Means clustering on features extracted from the URL data, such as the frequency of visiting specific domains or the number of pages visited per session. This requires feature engineering using SQL.
C. Sequence clustering using time-series analysis techniques (e.g., Hidden Markov Models), after transforming the data into a sequence of URLs for each session using Snowflake's LISTAGG function ordered by timestamp.
D. Association rule mining (e.g., Apriori) applied directly to the raw URL data to find frequent itemsets of URLs visited together within the same session. No SQL transformations are required.
E. DBSCAN clustering on the raw URL data, treating each URL as a separate dimension. This will identify URLs that are frequently visited by many users.

答案：B,C

解題說明：
Sequence clustering is appropriate for identifying navigation paths because it considers the order of URLs visited within a session. Using Snowflake's LISTAGG function allows for creating the required sequential data. K-Means clustering can also be effective if relevant features are engineered from the URL data (e.g., frequency of visiting specific domains). Association rule mining is less suitable for identifying navigation paths as it focuses on co-occurrence rather than sequence. PCA followed by hierarchical clustering and DBSCAN are not well-suited for identifying sequential navigation paths from clickstream data. Option 'A' is incorrect because association rule mining directly on raw URL data is unlikely to be effective without prior sequence extraction. Option 'D' and 'E' are not suitable for this type of analysis.

問題 #41
You have successfully trained a binary classification model using Snowpark ML and deployed it as a UDF in Snowflake. The UDF takes several input features and returns the predicted probability of the positive class. You need to continuously monitor the model's performance in production to detect potential data drift or concept drift. Which of the following methods and metrics, when used together, would provide the MOST comprehensive and reliable assessment of model performance and drift in a production environment? (Select TWO)

A. Monitor the volume of data processed by the UDF per day. A sudden drop in volume indicates a problem with the data pipeline.
B. Check for null values in the input features passed to the UDF. A sudden increase in null values indicates a problem with data quality.
C. Monitor the average predicted probability score over time. A significant shift in the average score indicates data drift.
D. Calculate the Kolmogorov-Smirnov (KS) statistic between the distribution of predicted probabilities in the training data and the production data over regular intervals. Track any substantial changes in the KS statistic.
E. Continuously calculate and track performance metrics like AUC, precision, recall, and Fl-score on a representative sample of labeled production data over regular intervals. Compare these metrics to the model's performance on the holdout set during training.

答案：D,E

解題說明：
Options B and D provide the most comprehensive assessment of model performance and drift. Option D, by continuously calculating key performance metrics (AUC, precision, recall, F1 -score) on labeled production data, directly assesses how well the model is performing on real- world data. Comparing these metrics to the holdout set provides insights into potential overfitting or degradation over time (concept drift). Option B, calculating the KS statistic between the predicted probability distributions of training and production data, helps to identify data drift, indicating that the input data distribution has changed. Option A can be an indicator but is less reliable than the KS statistic. Option C monitors data pipeline health, not model performance. Option E focuses on data quality, which is important but doesn't directly assess model performance drift.

問題 #42
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