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Snowflake SnowPro Advanced: Data Scientist Certification Exam 認定 DSA-C03 試験問題 (Q125-Q130):

質問 # 125
A data scientist at 'Polaris Analytics' wants to estimate the average transaction value of all online purchases made during the Black Friday sale. Due to the enormous volume of data in Snowflake, they decide to use the Central Limit Theorem (CLT). They randomly sample 1000 transactions daily for 30 days and calculate the sample mean for each day. The sample mean values are stored in a Snowflake table named Which of the following SQL queries, assuming the table has a column of 'FLOAT' type, will provide the best estimate of the population mean and its confidence interval using the CLT?

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

正解：B

解説：
The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. The standard error of the mean is calculated as the population standard deviation divided by the square root of the sample size. In this case, we are estimating the standard deviation from a sample of sample means, hence using 'STDDEV_SAMP', and the sample size here is the number of days, i.e. 30.

 

質問 # 126
You've trained a machine learning model using Scikit-learn and saved it as 'model.joblib'. You need to deploy this model to Snowflake. Which sequence of commands will correctly stage the model and create a Snowflake external function to use it for inference, assuming you already have a Snowflake stage named 'model_stage'?

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

正解：B

解説：

 

質問 # 127
You are tasked with building a Python stored procedure in Snowflake to train a Gradient Boosting Machine (GBM) model using XGBoost.
The procedure takes a sample of data from a large table, trains the model, and stores the model in a Snowflake stage. During testing, you notice that the procedure sometimes exceeds the memory limits imposed by Snowflake, causing it to fail. Which of the following techniques can you implement within the Python stored procedure to minimize memory consumption during model training?

• A. Convert the Pandas DataFrame used for training to a Dask DataFrame and utilize Dask's distributed processing capabilities to train the XGBoost model in parallel across multiple Snowflake virtual warehouses.
• B. Write the training data to a temporary table in Snowflake, then use Snowflake's external functions to train the XGBoost model on a separate compute cluster outside of Snowflake. Then upload the model to snowflake stage.
• C. Reduce the sample size of the training data and increase the number of boosting rounds to compensate for the smaller sample. Use the 'predict_proba' method to avoid storing probabilities for all classes.
• D. Implement XGBoost's 'early stopping' functionality with a validation set to prevent overfitting. If the stored procedure exceeds the memory limits, the model cannot be saved. Always use larger virtual warehouse.
• E. Use the 'hist' tree method in XGBoost, enable gradient-based sampling ('gosS), and carefully tune the 'max_depth' and parameters to reduce memory usage during tree construction. Convert all features to numerical if possible.

正解：E

解説：
Option B is the MOST effective way to minimize memory consumption within the Python stored procedure. The 'hist' tree method in XGBoost uses a histogram-based approach for finding the best split points, which is more memory-efficient than the exact tree method. Gradient- based sampling ('goss') reduces the number of data points used for calculating the gradients, further reducing memory usage. Tuning 'max_depth' and helps to control the complexity of the trees, preventing them from growing too large and consuming excessive memory. Converting categorical features to numerical is crucial as categorical features when One Hot Encoded, can explode feature space and significantly increase memory footprint. Option A will not work directly within Snowflake as Dask is not supported on warehouse compute. Option C may reduce the accuracy of the model. Option D requires additional infrastructure and complexity. Option E doesn't directly address the memory issue during the training phase, although early stopping is a good practice, the underlying memory pressure will remain.

 

質問 # 128
You have trained a complex machine learning model using Snowpark for Python and are now preparing it for production deployment using Snowpark Container Services. You have containerized the model and pushed it to a Snowflake-managed registry. However, you need to ensure that only authorized users can access and deploy this model. Which of the following actions MUST you take to secure your model in the Snowflake Model Registry, ensuring appropriate access control, and minimizing the risk of unauthorized deployment or modification?

• A. Grant the 'USAGE privilege on the stage where the model files are stored to all users who need to deploy the model.
• B. Grant the 'USAGE privilege on the database and schema containing the model registry, grant the 'READ privilege on the registry itself, and grant the EXECUTE TASK' privilege to the deployment team for the deployment task.
• C. Grant the 'READ privilege on the container registry to all users who need to deploy the model. Create a custom role with the 'APPLY MASKING POLICY privilege and grant this role to the deployment team.
• D. Create a custom role, grant the USAGE' privilege on the database and schema containing the model registry, grant the 'READ privilege on the registry, and then grant this custom role to only those users authorized to deploy the model. Consider masking sensitive model parameters using masking policies.
• E. Store the model outside of Snowflake managed registry and use external authentication to control access.

正解：D

解説：
Option D is the correct answer because it provides the most secure and granular access control. 'USAGE on the database and schema allows access to the container registry. 'READ on the registry allows viewing of model metadata without modification. Creating a custom role and granting it to specific users limits access to only authorized personnel. Utilizing masking policies further secures sensitive parameters. Option A is incorrect because it does not control access to the registry itself. 'USAGE privilege on a stage alone is insufficient for managing model registry access. Option B is incorrect because 'APPLY MASKING POLICY is not relevant for controlling access to the model registry. Option C is partially correct, but 'EXECUTE TASK' grants unnecessary privileges related to task execution, which is beyond the scope of registry access. It also lacks fine-grained control over who can deploy. Option E is incorrect because while it offers security, it bypasses the advantages of using Snowflake's managed registry.

 

質問 # 129
You have a dataset in Snowflake containing customer reviews. One of the columns, 'review_text', contains free-text customer feedback. You want to perform sentiment analysis on these reviews and include the sentiment score as a feature in your machine learning model. Furthermore, you wish to categorize the sentiment into 'Positive', 'Negative', and 'Neutral'. Given the need for scalability and efficiency within Snowflake, which methods could be employed?

• A. Create a series of Snowflake SQL queries utilizing complex string matching and keyword analysis to determine sentiment based on predefined lexicons. Categories are assigned through CASE statements.
• B. Create a Snowpark Python DataFrame from the Snowflake table, use a sentiment analysis library within the Snowpark environment, categorize the sentiments, and then save the resulting DataFrame back to Snowflake as a new table.
• C. Use a Python UDF (User-Defined Function) with a pre-trained sentiment analysis library (e.g., NLTK or spaCy) to calculate the sentiment score and categorize it. Deploy the UDF in Snowflake and apply it to the 'review_text' column.
• D. Use a Snowflake procedure that reads all 'review_text' data, transfers data outside of Snowflake to an external server running sentiment analysis software, and then writes results back into a new table.
• E. Utilize Snowflake's external functions to call a pre-existing sentiment analysis API (e.g., Google Cloud Natural Language API or AWS Comprehend) passing the review text and storing the returned sentiment score and category. Ensure proper API key management and network configuration.

正解：B、C、E

解説：
Options A, B, and C are viable and efficient methods for sentiment analysis within Snowflake. A Python UDF leverages the compute power of Snowflake while utilizing popular Python NLP libraries. Snowpark offers a scalable way to process data within Snowflake using Python. Snowflake's External Functions provide access to pre-built sentiment analysis APIs, which can be highly accurate but may incur costs based on API usage. Option D is not appropriate as it transfers the data out of Snowflake to perform the sentiment analysis, which is a bad design. Option E can be used as well but sentiment scores based on SQL are not going to be as accurate as calling an API or leveraging an established library.

 

質問 # 130
......

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