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DSA-C03 Dumps Reviews & DSA-C03 Clear Exam

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q128-Q133):

NEW QUESTION # 128
You have deployed a fraud detection model in Snowflake, predicting fraudulent transactions. Initial evaluations showed high accuracy. However, after a few months, the model's performance degrades significantly. You suspect data drift and concept drift. Which of the following actions should you take FIRST to identify and address the root cause?

A. Immediately retrain the model with the latest available data, assuming data drift is the primary issue.
B. Implement a data quality monitoring system to detect anomalies in input features, alongside calculating population stability index (PSI) to quantify data drift.
C. Implement a SHAP (SHapley Additive exPlanations) analysis on recent transactions to understand feature importance shifts and potential concept drift.
D. Revert to a previous version of the model known to have performed well, while investigating the issue in the background.
E. Increase the model's prediction threshold to reduce false positives, even if it means potentially missing more fraudulent transactions.

Answer: B

Explanation:
Option D is the best first step. Data quality monitoring and PSI allow for quantifying and identifying data drift. SHAP (B) is useful after determining that concept drift is the problem. Retraining immediately (A) without understanding the cause can exacerbate the problem. Reverting (C) is a temporary fix, not a solution. Adjusting the threshold (E) without understanding the underlying issue is also not a proper diagnostic approach.

NEW QUESTION # 129
You have deployed a sentiment analysis model on AWS SageMaker and want to integrate it with Snowflake using an external function. You've created an API integration object. Which of the following SQL statements is the most secure and efficient way to create an external function that utilizes this API integration, assuming the model expects a JSON payload with a 'text' field, the API integration is named 'sagemaker_integration' , the SageMaker endpoint URL is 'https://your-sagemaker-endpoint.com/invoke' , and you want the Snowflake function to be named 'predict_sentiment'?

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

Answer: D

Explanation:
Option C is the most secure and efficient. It correctly uses the 'API_INTEGRATION' to leverage Snowflake's security features for managing credentials. It also includes the 'HEADERS' parameter to specify the content type, which is essential for proper communication with the SageMaker endpoint. REQUEST and RESPONSE translators can avoid some of the boilerplate JSON building and parsing within the Snowflake environment. The other options are either missing crucial headers or do not use the API integration securely.

NEW QUESTION # 130
You are using Snowflake Cortex to analyze customer reviews. You have created a vector embedding for each review using a UDF that calls a remote LLM inference endpoint. Now you need to perform a similarity search to identify reviews that are similar to a given query review. Which of the following SQL queries leveraging vector functions in Snowflake is the MOST efficient and appropriate way to achieve this, assuming the 'REVIEW EMBEDDINGS' table has columns 'review_id' and 'embedding' (a VECTOR column) and query_embedding' is a pre-computed vector embedding?

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

Answer: E

Explanation:
The most efficient and accurate way to perform a similarity search with vector embeddings is using ordered in descending order because inner product is the fastest of the vector functions and still gets the vector similarity score. The operator performs an exact match which doesn't consider vector similarity (A). is for array data, not vectors (B). 'QUALIFY' and 'VECTOR COSINE SIMILARITY works but isn't optimal (C), and L2 distance require some value/threshold to compare. 'ORDER BY ... LIMIT is efficient with the inner product, it's very fast (E).

NEW QUESTION # 131
You are exploring a large dataset of website user behavior in Snowflake to identify patterns and potential features for a machine learning model predicting user engagement. You want to create a visualization showing the distribution of 'session_duration' for different 'user_segments'. The 'user_segmentS column contains categorical values like 'New', 'Returning', and 'Power User'. Which Snowflake SQL query and subsequent data visualization technique would be most effective for this task?

A. Query: 'SELECT user_segments, AVG(session_duration) FROM user_behavior GROUP BY Visualization: Bar chart showing average session duration for each user segment.
B. Query: 'SELECT session_duration FROM user_behavior WHERE user_segments = 'New';- (repeated for each user segment). Visualization: Overlayed histograms showing the distribution of session duration for each user segment on the same axes.
C. Query: 'SELECT user_segments, MEDIAN(session_duration) FROM user_behavior GROUP BY user_segments;' Visualization: Box plot showing the distribution (quartiles, median, outliers) of session duration for each user segment.
D. Query: ' SELECT COUNT( ) ,user_segments FROM user_behavior GROUP BY user_segments;' Visualization: Pie chart showing proportion of each segment.
E. Query: 'SELECT user_segments, APPROX 0.25), APPROX 0.5), APPROX_PERCENTlLE(session_duration, 0.75) FROM user_behavior GROUP BY user_segments;' Visualization: Scatter plot where each point represents a user segment and the x,y coordinates represent session duration at 25th and 75th percentiles respectively.

Answer: C

Explanation:
Using the Median (option B) provides a better central tendency measure than the average (option A) when the data may have outliers. The box plot effectively visualizes the distribution, including quartiles and outliers. Option C involves generating separate queries and histograms, which is less efficient. Calculating quantiles using 'APPROX_PERCENTILE' (Option D) is good for large datasets, but the resulting scatter plot isn't the best way to show distribution. Pie chart does not show distrubution but proportions.

NEW QUESTION # 132
You are deploying a fraud detection model using Snowpark Container Services. The model requires a substantial amount of GPU memory. After deploying your service, you notice that it frequently crashes due to Out-Of-Memory (OOM) errors. You have verified that the container image itself is not the source of the problem. Which of the following strategies are most appropriate to mitigate these OOM errors when using Snowpark Container Services, assuming you want to minimize costs and complexity?

A. Implement model parallelism across multiple containers, splitting the model's workload and data across them. Configure each container with a smaller 'container.resources.memory' allocation.
B. Implement a mechanism within your model's inference code to explicitly free up unused memory after each prediction. Use Python's 'gc.collect()' and ensure proper cleanup of large data structures. Configure a smaller 'container.resources.memory' allocation.
C. Ignore OOM errors and rely on the container service to automatically restart the container. The model will eventually process all requests.
D. Utilize CPU-based inference instead of GPU-based inference, as CPU inference is generally less memory-intensive. Convert the model to a format optimized for CPU inference (e.g., using ONNX). Reduce the 'container.resources.cpu' count.
E. Increase the 'container.resources.memory' configuration setting in the service definition to a value significantly larger than the model's memory footprint. Monitor memory utilization and adjust as needed.

Answer: B,E

Explanation:
Options A and D are the best strategies. Option A directly addresses the OOM issue by increasing the memory allocation. Monitoring memory usage is crucial to optimize resource utilization. Option D focuses on efficient memory management within the model itself. Explicitly freeing memory and garbage collection can reduce memory footprint. If model need very less gpu memory then decrease container.resources.memory' configuration Option B is a valid strategy, but it introduces significantly more complexity with model parallelism and inter-container communication. Option C might be an option if GPU inference is not strictly necessary and acceptable performance can be achieved with CPU inference, but it is a significant change to the model architecture and potentially impacts performance. Option E is incorrect because ignoring OOM errors leads to unreliable service behavior and data loss.

NEW QUESTION # 133
......

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