John Lee John Lee's صفحة الملف الشخصي

John Lee John Lee

0 دورة ملتحَق بها • 0 اكتملت الدورة

سيرة شخصية

100% Pass Snowflake - Trustable DSA-C03 - Customizable SnowPro Advanced: Data Scientist Certification Exam Exam Mode

After the client pay successfully they could receive the mails about DSA-C03 guide questions our system sends by which you can download our test bank and use our DSA-C03 study materials in 5-10 minutes. The mail provides the links and after the client click on them the client can log in and gain the DSA-C03 Study Materials to learn. The procedures are simple and save clients' time. For the client the time is limited and very important and our DSA-C03 learning guide satisfies the client's needs to download and use our DSA-C03 practice engine immediately.

Snowflake DSA-C03 practice questions are based on recently released Snowflake DSA-C03 exam objectives. Includes a user-friendly interface allowing you to take the SnowPro Advanced: Data Scientist Certification Exam practice exam on your computers, like downloading the PDF, Web-Based DSA-C03 Practice Test TestPDF, and Desktop Snowflake DSA-C03 practice exam TestPDF.

>> Customizable DSA-C03 Exam Mode <<

Cheap DSA-C03 Dumps, DSA-C03 Latest Exam Tips

A free demo of DSA-C03 practice test questions and up to 1 year of free updates are also available at TestPDF. So, this is the time to download valid Snowflake DSA-C03 exam questions and start studying. There is no room for delays in SnowPro Advanced: Data Scientist Certification Exam (DSA-C03) preparation exams or second thoughts when you know that you have to survive the competition and safeguard your job.

Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q116-Q121):

NEW QUESTION # 116
You are evaluating a binary classification model built in Snowflake for predicting customer churn. You have access to the model's predictions on a holdout dataset, and you want to use both the ROC curve and the confusion matrix to comprehensively assess its performance. Which of the following statements regarding the interpretation and use of ROC curves and confusion matrices are correct in this scenario?

A. In Snowflake, you can generate ROC curves and confusion matrices directly using the 'SYSTEM$PREDICT function with appropriate parameters and visualizing the results using a tool like Snowsight or Tableau.
B. The confusion matrix allows you to calculate precision, recall, F I-score, and accuracy, which are all useful for understanding the model's performance in terms of correctly and incorrectly classified instances.
C. While the ROC curve is independent of the class distribution, the metrics derived from the confusion matrix (e.g., precision, recall) can be significantly affected by imbalanced datasets.
D. The area under the ROC curve (AUC) provides a single scalar value representing the overall discriminatory power of the model, with a higher AUC indicating better performance. An AUC of 0.5 indicates that the model performs no better than random chance.
E. The ROC curve visualizes the trade-off between true positive rate (sensitivity) and false negative rate (1 - specificity) at various threshold settings.

Answer: B,C,D

Explanation:
Options B, C, and D are correct. Option A is incorrect because the ROC curve plots the True Positive Rate (Sensitivity) against the False Positive Rate (1 - Specificity). Option E is partially correct in the sense that you can use SYSTEM$PREDICT but it requires extra data processing steps and the result may need formatting using other Snowflake functionalities or external tools (Snowsight, Tableau) for complete visualization as a ROC or Confusion Matrix.

NEW QUESTION # 117
You are tasked with deploying a fraud detection model in Snowflake using the Model Registry. The model is trained on a dataset that is updated daily. You need to ensure that your deployed model uses the latest approved version and that you can easily roll back to a previous version if any issues arise. Which of the following approaches would provide the most robust and maintainable solution for model versioning and deployment, considering minimal downtime during updates and rollback?

A. Register each new model version in the Snowflake Model Registry and promote the desired version to 'PRODUCTION' stage. Update a single UDF that dynamically fetches the model based on the 'PRODUCTION' stage metadata.
B. Deploy a new Snowflake UDF referencing the model file directly in cloud storage every time the model is retrained. Rely on cloud storage versioning for rollback.
C. Store all model versions within a single model registry entry without versioning, overwriting the existing file with each new training run.
D. Use Snowflake Tasks to periodically refresh a table containing the latest model weights. The UDF directly queries this table for predictions.
E. Create multiple Snowflake UDFs, each corresponding to a different model version. Manually switch the active UDF by updating application code when a new model is deployed.

Answer: A

Explanation:
Option B provides the most robust and maintainable solution. Registering each model version in the Snowflake Model Registry allows for easy tracking and rollback. Promoting the desired version to 'PRODUCTION' and dynamically fetching the model in a UDF based on this metadata ensures minimal downtime during updates and rollbacks. Option A relies on cloud storage versioning, which is less integrated with Snowflake's metadata management. Option C requires manual UDF switching, which is error-prone. Option D doesn't utilize the Model Registry effectively. Option E eliminates the benefits of version control.

NEW QUESTION # 118
A financial institution suspects fraudulent activity based on unusual transaction patterns. They want to use association rule mining to identify relationships between different transaction attributes (e.g., transaction amount, location, time of day, merchant category code) that are indicative of fraud. The data is stored in a Snowflake table called 'TRANSACTIONS'. Which of the following considerations are CRITICAL when applying association rule mining in this fraud detection scenario?

A. Ensure that the Apriori algorithm is run directly within Snowflake using SQL to maximize performance and scalability, rather than extracting the data and processing it in an external Python environment.
B. Focus solely on rules with very high support (e.g., > 0.1) to ensure statistical significance and avoid overfitting to rare fraudulent events.
C. Carefully discretize continuous variables like 'transaction amount' and 'time of day' into meaningful categories to enable association rule mining, and consider the impact of different discretization strategies on the resulting rules.
D. Prioritize rules with high confidence and lift, even if support is relatively low, as rare but highly predictive combinations of attributes can be strong indicators of fraudulent activity.
E. Ignore transaction attributes that have a large number of distinct values (e.g., specific location coordinates) as they will likely lead to an explosion of rules and make interpretation difficult.

Answer: C,D

Explanation:
Option B is critical because discretization is essential for handling continuous variables in association rule mining. The way these variables are binned can significantly influence the rules discovered. Option C is also critical because in fraud detection, identifying rare but highly predictive rules is crucial. Low support rules, if they have high confidence and lift, can point to specific patterns indicative of fraud. Option A is incorrect because requiring high support would miss rare fraud patterns. Option D is incorrect because some high cardinality attributes might be important indicators.Option E is incorrect as Apriori algorith cannot be directly run using SQL, Snowpark and python is a good option.

NEW QUESTION # 119
You are working with a large dataset of transaction data in Snowflake to identify fraudulent transactions. The dataset contains millions of rows and includes features like transaction amount, location, time, and user ID. You want to use Snowpark and SQL to identify potential outliers in the 'transaction amount' feature. Given the potential for skewed data and varying transaction volumes across different locations, which of the following data profiling and feature engineering techniques would be the MOST effective at identifying outlier transaction amounts while considering the data distribution and location-specific variations?

A. Partition the data by location using Snowpark. For each location, calculate the median and median absolute deviation (MAD) of the 'transaction amount' feature. Identify outliers as transactions with amounts that fall outside of the median +/- 3 MAD for that location.
B. Apply a clustering algorithm (e.g., DBSCAN) using Snowpark ML to the transaction data, using transaction amount, location and time as features. Treat data points in small, sparse clusters as outliers. This approach does not need to be performed for each location, just the entire dataset.
C. Calculate the mean and standard deviation of the 'transaction amount' feature for the entire dataset using SQL. Identify outliers as transactions with amounts that fall outside of 3 standard deviations from the mean.
D. Use Snowflake's APPROX_PERCENTILE function with Snowpark to calculate percentiles of the 'transaction amount' feature. Transactions with amounts in the top and bottom 1% are flagged as outliers.
E. Use Snowpark to calculate the interquartile range (IQR) of the 'transaction amount' feature for the entire dataset. Identify outliers as transactions with amounts that fall below QI - 1.5 IQR or above Q3 + 1.5 IQR.

Answer: A,B

Explanation:
Options C and E are the most effective for identifying outliers, considering the skewed nature of transaction data and location-specific variations. The IQR is better than mean and Standard Deviation. The MAD is more robust to outliers compared to standard deviation, which may be inflated by extreme values. Partitioning by location allows for a more nuanced identification of outliers specific to each location. DBSCAN is a great option to include with the partitioning because it considers transaction amount, location, and time as a factor in determine whether the data is an outlier. A and B are less effective because the median and standard deviation are sensitive to extreme values, and the IQR will not consider other dimensions such as location and time. D is only okay because it does not consider the impact of location on determining outliers.

NEW QUESTION # 120
You're deploying a pre-trained model for fraud detection that's hosted as a serverless function on Google Cloud Functions. This function requires two Snowflake tables: 'TRANSACTIONS (containing transaction details) and 'CUSTOMER PROFILES (containing customer information), to be joined and used as input for the model. The external function in Snowflake, 'DETECT FRAUD', should process batches of records efficiently. Which of the following approaches are most suitable for optimizing data transfer and processing between Snowflake and the Google Cloud Function?

A. Create a Snowflake pipe that automatically streams new transaction data to the Google Cloud Function whenever new records are inserted into the 'TRANSACTIONS' table, triggering the fraud detection model in real-time.
B. Serialize the joined 'TRANSACTIONS' and 'CUSTOMER_PROFILES data into a large CSV file, store it in a cloud storage bucket, and then pass the URL of the CSV file to the 'DETECT FRAUD function.
C. Utilize Snowflake's external functions feature to send batches of data from the joined 'TRANSACTIONS' and 'CUSTOMER PROFILES tables to the 'DETECT_FRAUD function in a structured format (e.g., JSON) using HTTP requests. Implement proper error handling and retry mechanisms.
D. Within the 'DETECT FRAUD function, execute SQL queries directly against Snowflake using the Snowflake JDBC driver to fetch the necessary data from the "TRANSACTIONS' and 'CUSTOMER PROFILES' tables.
E. Use Snowflake's Java UDF functionality to directly connect to the Google Cloud Function's database, bypassing the need for an external function or data transfer through HTTP.

Answer: C

Explanation:
Option D is the most appropriate. External functions are designed for this type of integration, allowing Snowflake to send batches of data to external services for processing. Using JSON provides a structured and efficient way to transfer the data. Option A is inefficient due to the overhead of writing and reading large files. Option B bypasses external functions which defeats the purpose of the question and also is not a standard integration pattern. Option C is not recommended as Snowflake is better at parallel processing. Option E would be appropriate for real- time streaming and fraud detection use case but involves much more setup than a single function invocation, so is a possible but not the most practical choice.

NEW QUESTION # 121
......

TestPDF SnowPro Advanced: Data Scientist Certification Exam (DSA-C03) practice test material covers all the key topics and areas of knowledge necessary to master the Snowflake Certification Exam. Experienced industry professionals design the DSA-C03 exam questions and are regularly updated to reflect the latest changes in the SnowPro Advanced: Data Scientist Certification Exam (DSA-C03) exam. In addition, TestPDF offers three different formats of practice material which are discussed below.

Cheap DSA-C03 Dumps: https://www.testpdf.com/DSA-C03-exam-braindumps.html

The DSA-C03 softeware file can make you as you are in the real exam, after you do the exercise, you can assess your score and have knowledge of your own levels about SnowPro Advanced: Data Scientist Certification Exam exam, Snowflake Customizable DSA-C03 Exam Mode In this way, how possible can they not achieve successfully fast learning, If you can pass exam (DSA-C03 dumps torrent materials) and obtain a certification, you will obtain salary raise and considerable annual bonus, Lower piece with higher quality, that's the reason why you should choose our DSA-C03 exam practice torrent.

Yeah, i passed successfully, Finding All Occurrences of a Pattern, The DSA-C03 softeware file can make you as you are in the real exam, after you do the exercise, you DSA-C03 can assess your score and have knowledge of your own levels about SnowPro Advanced: Data Scientist Certification Exam exam.

2025 Reliable DSA-C03: Customizable SnowPro Advanced: Data Scientist Certification Exam Exam Mode

In this way, how possible can they not achieve successfully fast learning, If you can pass exam (DSA-C03 dumps torrent materials) and obtain a certification, you will obtain salary raise and considerable annual bonus.

Lower piece with higher quality, that's the reason why you should choose our DSA-C03 exam practice torrent, Below are specifications of these three formats.

John Lee John Lee

سيرة شخصية

القائمة الرئيسية

روابط هامة

ساعات العمل