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最新的 SnowPro Advanced DSA-C03 免費考試真題 (Q181-Q186):

問題 #181
You've built a complex machine learning model using scikit-learn and deployed it as a Python UDF in Snowflake. The UDF takes a JSON string as input, containing several numerical features, and returns a predicted probability However, you observe significant performance issues, particularly when processing large batches of data'. Which of the following approaches would be MOST effective in optimizing the performance of this UDF in Snowflake?

• A. Use Snowflake's vectorized UDF feature to process data in micro-batches, minimizing the overhead of repeated Python interpreter initialization.
• B. Rewrite the UDF in Java or Scala to leverage the JVM's performance advantages over Python in Snowflake.
• C. Pre-process the input data outside of the UDF using SQL transformations, reducing the amount of data passed to the UDF and simplifying the Python code.
• D. Serialize the scikit-learn model using 'joblib' instead of 'pickle' for potentially faster deserialization within the UDF.
• E. Increase the warehouse size to improve the overall compute resources available for UDF execution.

答案：A,C

解題說明：
Vectorized UDFs (A) are designed specifically for performance optimization by processing data in batches, reducing overhead. Pre- processing data using SQL (D) can significantly reduce the complexity and data volume handled by the UDF. While 'joblib' (B) might offer a slight improvement, it's less impactful than vectorization. Increasing warehouse size (C) helps but doesn't address the underlying inefficiencies of repeated interpreter initialization. Rewriting in Java/Scala (E) is a viable option but requires significant effort and might not be necessary if vectorization and pre-processing are sufficient.

 

問題 #182
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 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.
• B. 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.
• 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 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.
• E. 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.

答案：A

解題說明：
The correct answer is C. Since the sample size for fraudulent transactions is less than 30, and the population standard deviations are unknown, a t-test is more appropriate than a Z-test. The null hypothesis should state the assumption that we are trying to disprove (i.e., fraudulent transactions are not, on average, higher). The alternative hypothesis is the claim we are trying to support (i.e., fraudulent transactions ARE, on average, higher). The chi-squared test is used for categorical data, not continuous data like transaction amount. We are interested in knowing if one set of transaction amounts is greater, so its a one tailed t test.

 

問題 #183
A marketing team at 'RetailSphere' wants to segment their customer base using unstructured textual data (customer reviews) stored in a Snowflake VARIANT column named 'REVIEW TEXT within the table 'CUSTOMER REVIEWS'. They aim to identify distinct customer segments based on sentiment and topics discussed in their reviews. They want to use a Supervised Learning approach for this task. Which of the following strategies best describes the appropriate approach within Snowflake, considering performance and scalability? Assume you have pre-trained sentiment and topic models deployed as Snowflake external functions.

• A. Extract the column, apply sentiment analysis and topic modeling using Python within a Snowflake UDF, and then perform K-Means clustering directly on the resulting features within Snowflake. Define the labels after clustering based on the majority class of the topics and sentiments in each cluster.
• B. Extract the 'REVIEW TEXT column, apply sentiment analysis and topic modeling using Java within a Snowflake UDF, and then perform hierarchical clustering directly on the resulting features within Snowflake. Manually label the clusters after visual inspection.
• C. Create a Snowflake external function to call a pre-trained sentiment analysis and topic modeling model hosted on AWS SageMaker. Apply these functions to the ' REVIEW_TEXT column to generate sentiment scores and topic probabilities. Subsequently, use these features as input to a supervised classification model (e.g., XGBoost) also deployed as a Snowflake external function, training on a manually labeled subset of reviews.
• D. Create a Snowflake external function to call a pre-trained sentiment analysis and topic modeling model hosted on Azure ML. Apply these functions to the REVIEW_TEXT column to generate sentiment scores and topic probabilities. Subsequently, use these features as input to an unsupervised clustering algorithm (e.g., DBSCAN) within Snowflake, relying solely on data density to define segments.
• E. Extract the ' REVIEW_TEXT column, manually categorize a small subset of reviews into predefined segments. Train a text classification model (e.g., using scikit-learn) externally, deploy it as a Snowflake external function, and then apply this function to the entire 'REVIEW TEXT column to predict segment assignments. Manually adjust cluster centroids to represent the manually labeled dataset.

答案：C

解題說明：
Option C provides the most robust and scalable approach. Using Snowflake external functions allows leveraging pre-trained models without moving the data out of Snowflake. Applying sentiment analysis and topic modeling generates features that can be used by a supervised classification model trained on a labeled subset of reviews. This combines the power of external models with Snowflake's data processing capabilities. Using labeled data allows for better segment definition using Supervised approach.

 

問題 #184
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.

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

答案：E

解題說明：
The correct answer is A. This query first calculates the variance for each machine using a CTE (Common Table Expression). Then, it calculates the average variance and standard deviation of variances across all machines. Finally, it selects the machine IDs where the variance is more than two standard deviations above the average variance. Option B is incorrect because it tries to calculate aggregate functions within the HAVING clause without proper grouping. Option C uses a JOIN which is inappropriate in this scenario. Option D is incorrect because the window functions will not return the correct aggregate values. Option E is syntactically incorrect. QUALIFY clause should have partition BY statement.

 

問題 #185
A telecom company, 'ConnectPlus', observes that the individual call durations of its customers are heavily skewed towards shorter calls, following an exponential distribution. A data science team aims to analyze call patterns and requires to perform hypothesis testing on the average call duration. Which of the following statements regarding the applicability of the Central Limit Theorem (CLT) in this scenario are correct if the sample size is sufficiently large?

• A. The CLT is applicable, and the distribution of sample means of call durations will approximate a normal distribution, regardless of the skewness of the individual call durations.
• B. The CLT is applicable only if the sample size is extremely large (e.g., greater than 10,000), due to the exponential distribution's heavy tail.
• C. The CLT is applicable as long as the sample size is reasonably large (typically n > 30), and the distribution of sample means will be approximately normal. The specific minimum sample size depends on the severity of the skewness.
• D. The CLT is applicable, and the sample mean will converge to the population median.
• E. The CLT is not applicable because the population distribution (call durations) is heavily skewed.

答案：A,C

解題說明：
The Central Limit Theorem (CLT) states that the distribution of sample means will be approximately normally distributed, regardless of the shape of the population distribution, as long as the sample size is large enough. While the rule of thumb is typically n > 30, the skewness of the original population distribution can influence how large the 'large enough' sample size needs to be. In this scenario, since the call durations follow an exponential distribution (which is skewed), a reasonably large sample size will still allow the CLT to be applicable, and the sample means' distribution will approach normality. The CLT ensures convergence toward normality in the distribution of sample means, not convergence of the sample mean to the population median.

 

問題 #186
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