DSA-C03 TEST QUESTIONS VCE & ACTUAL DSA-C03 TEST

DSA-C03 Test Questions Vce & Actual DSA-C03 Test

DSA-C03 Test Questions Vce & Actual DSA-C03 Test

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Tags: DSA-C03 Test Questions Vce, Actual DSA-C03 Test, DSA-C03 Valid Exam Pdf, Test DSA-C03 Cram, DSA-C03 Certification Cost

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q101-Q106):

NEW QUESTION # 101
A data engineer is tasked with removing duplicates from a table named 'USER ACTIVITY' in Snowflake, which contains user activity logs. The table has columns: 'ACTIVITY TIMESTAMP', 'ACTIVITY TYPE', and 'DEVICE_ID. The data engineer wants to remove duplicate rows, considering only 'USER ID', 'ACTIVITY TYPE, and 'DEVICE_ID' columns. What is the most efficient and correct SQL query to achieve this while retaining only the earliest 'ACTIVITY TIMESTAMP' for each unique combination of the specified columns?

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

Answer: E

Explanation:
Option B provides the most efficient and correct solution. - It uses the 'QUALIFY' clause along with the window function to partition the data by 'USER ID, 'ACTIVITY TYPE, and 'DEVICE ICY. Within each partition, it orders the rows by 'ACTIVITY _ TIMESTAMP' in ascending order. The function assigns a unique rank to each row within the partition. The 'QUALIFY clause filters the result set, keeping only the rows where the 'ROW NUMBER()' is equal to 1, which effectively selects the earliest activity timestamp for each unique combination of 'ACTIVITY _ TYPE , and 'DEVICE_ID'. Option A is incorrect because it aggregates and only retains the minimum 'ACTIVITY TIMESTAMP' , discarding other potentially relevant columns. Option C is incorrect because it only returns rows where a combination of 'USER_ID, ACTIVITY_TYPE, DEVICE_ID, and ACTIVITY_TIMESTAMP" appears only once, not removing duplicates based on the desired columns. Option D is incorrect because it only selects distinct combinations of USER ID, ACTIVITY_TYPE, and DEVICE_ID, thus losing the ACTIVITY_TIMESTAMP. option E is incorrect. While it keeps the ACTIVITY_TIMESTAMP as the earliest, FIRST VALUE generates all other columns based on the input data which will generate duplicates.


NEW QUESTION # 102
You are building a machine learning pipeline that uses data stored in Snowflake. You want to connect a Jupyter Notebook running on your local machine to Snowflake using Snowpark. You need to securely authenticate to Snowflake and ensure that you are using a dedicated compute resource for your Snowpark session. Which of the following approaches is the MOST secure and efficient way to achieve this?

  • A. Store your Snowflake username and password directly in the Jupyter Notebook and create a Snowpark session using these credentials and the default Snowflake warehouse.
  • B. Hardcode a role with 'ACCOUNTADMIN' privileges in your Jupyter Notebook using username and password.
  • C. Use the Snowflake Python connector with username and password and execute SQL commands to create a Snowpark DataFrame.
  • D. Configure OAuth authentication for your Snowflake account and use the OAuth token to establish a Snowpark session with a dedicated virtual warehouse.
  • E. Use key pair authentication to connect to Snowflake, storing the private key securely on your local machine. Specify a dedicated virtual warehouse during session creation.

Answer: E

Explanation:
Option D is the most secure. Key pair authentication is more secure than username/password. Specifying a dedicated virtual warehouse ensures dedicated compute. Option A is highly insecure. Option B doesn't directly create a Snowpark session. Option C, while using OAuth, requires proper setup and key pair provides more control. Option E is highly insecure and grants excessive privileges.


NEW QUESTION # 103
You've built a model in Snowflake to predict house prices based on features like location, square footage, and number of bedrooms. After deploying the model, you want to ensure that the incoming data used for prediction is similar to the data the model was trained on. You decide to implement a data distribution comparison strategy. Consider these options and select all that apply:

  • A. Use Snowflake's built-in statistics functions to compute quantiles (e.g., 25th, 50th, 75th percentiles) for each numerical feature. Compare these quantiles between the training and incoming datasets and set up alerts for significant deviations.
  • B. Create a binary classification model in Snowflake that attempts to predict whether a given row of data comes from the training dataset or the incoming dataset. If the model achieves high accuracy, it indicates a significant difference in data distributions.
  • C. Only focus on monitoring the target variable (house price) and assume that if the distribution of house prices remains stable, the input data distribution is also stable.
  • D. Generate histograms for each numerical feature in both the training and incoming datasets using a Python UDF that leverages libraries like Pandas and Matplotlib. Visually compare the histograms to identify potential distribution shifts.
  • E. Calculate the mean and standard deviation for each numerical feature in both the training and incoming datasets using Snowflake SQL. Create a Snowflake Alert that triggers if the difference in means or standard deviations exceeds a predefined threshold for any feature.

Answer: A,B,E

Explanation:
Options A, C, and E are all valid and effective strategies for comparing data distributions. Option A provides a simple and easily implementable approach using basic descriptive statistics. Option C is more robust than just mean and standard deviation, providing a more detailed comparison of the distributions. Option E utilizes a machine learning approach to quantify the difference in distributions, which can be very sensitive to subtle changes. Option B is helpful for visualizations but lacks an automated, quantifiable alert mechanism. Option D is flawed because the target variable's distribution can remain stable even if the input data distribution changes due to compensatory effects or other factors.


NEW QUESTION # 104
You have a binary classification model deployed in Snowflake to predict customer churn. The model outputs a probability score between 0 and 1. You've calculated the following confusion matrix on a holdout set: I I Predicted Positive I Predicted Negative I --1 1 Actual Positive | 80 | 20 | I Actual Negative | 10 | 90 | What are the Precision, Recall, and Accuracy for this model, and what do these metrics tell you about the model's performance? SELECT statement given for true and false condition (True Positive, True Negative, False Positive, False Negative)

  • A. Precision = 0.89, Recall = 0.80, Accuracy = 0.85. The model is slightly better at avoiding false positives than identifying true positives.
  • B. Precision = 0.80, Recall = 0.89, Accuracy = 0.85. The model is slightly better at identifying true positives than avoiding false positives.
  • C. Precision = 0.80, Recall = 0.90, Accuracy = 0.90. The model is performing poorly, with a high rate of both false positives and false negatives.
  • D. Precision = 0.89, Recall = 0.80, Accuracy = 0.85. The model has good overall performance with balanced precision and recall.
  • E. Precision = 0.90, Recall = 0.80, Accuracy = 0.80. The model has good overall performance but needs to be adjusted to improve the false negative rate.

Answer: A

Explanation:
The correct answer is C. Precision is calculated as True Positives / (True Positives + False Positives) = 80 / (80 + 10) = 0.89. Recall is calculated as True Positives / (True Positives + False Negatives) = 80 / (80 + 20) = 0.80. Accuracy is calculated as (True Positives + True Negatives) / Total = (80 + 90) / 200 = 0.85. High precision indicates fewer false positives, while lower recall indicates more false negatives. Also the select statement calculates true positives, true negatives, false positives, and false negatives from churn_predictions table and then accuracy, precision , recall has to be calculated.


NEW QUESTION # 105
You have implemented a Python UDTF in Snowflake to train a machine learning model incrementally using incoming data'. The UDTF performs well initially, but as the volume of data processed increases significantly, you observe a noticeable degradation in performance and an increase in query execution time. You suspect that the bottleneck is related to the way the model is being updated and persisted within the UDTF. Which of the following optimization strategies, or combination of strategies, would be MOST effective in addressing this performance issue?

  • A. Persist the trained model to a Snowflake stage after each batch update. Use a separate UDF (User-Defined Function) to load the model from the stage before processing new data. This decouples model training from inference.
  • B. Rewrite the UDTF in Java or Scala, as these languages generally offer better performance compared to Python for computationally intensive tasks. Use the same machine learning libraries that you used with Python.
  • C. Leverage Snowflake's external functions and a cloud-based ML platform (e.g., SageMaker, Vertex A1) to offload the model training process. The UDTF would then only be responsible for data preparation and calling the external function.
  • D. Use the 'cachetools' library within the UDTF to cache intermediate results and reduce redundant calculations during each function call. Configure the cache with a maximum size and eviction policy appropriate for the data volume.
  • E. Instead of updating the model incrementally within the UDTF for each row, batch the incoming data into larger chunks and perform model updates only on these batches. Use Snowflake's VARIANT data type to store these batches temporarily.

Answer: A,C,E

Explanation:
Options B, C, and D offer the most effective strategies for optimizing performance when training a model incrementally with a Python UDTF in Snowflake. Batching updates (B) reduces the overhead of model updates. Persisting the model to a Snowflake stage (C) decouples training from inference and allows for model reuse. Offloading training to an external function (D) leverages dedicated ML infrastructure. Caching (A) might offer some marginal improvement but is unlikely to address the core performance bottleneck. While Java or Scala (E) can be faster than Python, rewriting the UDTF is a significant undertaking and might not be necessary if other optimization strategies are applied effectively. Also the question is specific about Python. In summary, consider batching and persistence as key in performance optimization.


NEW QUESTION # 106
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