Dan Hunt Dan Hunt's Profile Page

Dan Hunt Dan Hunt

0 Course Enrolled • 0 Course Completed

Biography

100% Free DAA-C01–100% Free Latest Exam Online | Perfect SnowPro Advanced: Data Analyst Certification Exam Valid Exam Duration

One of the main unique qualities of PassReview SnowPro Advanced: Data Analyst Certification Exam Exam Questions is its ease of use. Our practice exam simulators are user and beginner friendly. You can use SnowPro Advanced: Data Analyst Certification Exam (DAA-C01) PDF dumps and Web-based software without installation. Snowflake DAA-C01 PDF Questions work on all the devices like smartphones, Macs, tablets, Windows, etc. We know that it is hard to stay and study for the SnowPro Advanced: Data Analyst Certification Exam (DAA-C01) exam dumps in one place for a long time.

We provide 1 year of free updates. In conclusion, PassReview guarantees that if you use the product, you will pass the DAA-C01 exam on your first try. Its primary goal is to save students time and money, not just conduct a business transaction. Candidates can take advantage of the free trials to evaluate the quality and standard of the DAA-C01 Dumps before making a purchase. With the right DAA-C01 study material and support team passing the examination at first attempt is an achievable goal.

>> DAA-C01 Latest Exam Online <<

Snowflake DAA-C01: SnowPro Advanced: Data Analyst Certification Exam braindumps - Testking DAA-C01 test

The price for DAA-C01 learning materials is quite reasonable, and no matter you are a student or you are an employee, you can afford them. Besides, we offer you free demo to have a try, and through free demo, you can know some detailed information of DAA-C01 Exam Dumps. With experienced experts to compile and verify, DAA-C01 learning materials are high quality. Besides, DAA-C01 exam dumps contain both questions and answers, and you check your answers quickly after practicing.

Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q248-Q253):

NEW QUESTION # 248
A retail company is using Snowflake to store their daily sales data'. They want to predict sales for the next week, accounting for weekly seasonality and a promotional campaign running on specific days. The sales data is in a table called 'SALES DATA' with columns 'SALE DATE' (DATE) and 'SALES AMOUNT' (NUMBER). Which of the following SQL statements is the MOST efficient and accurate way to achieve this using Snowflake's forecasting features, assuming a confidence interval of 95%?

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

Answer: A

Explanation:
Option E is the most correct. Option A uses 'timeLimit' in generator which needs additional conditions to be satisfied. Option E utilizes 'TIMESTAMP_INPUT = TRUE to correctly interpret the 'SALE_DATE column. It accurately specifies the forecast horizon (7 days) and confidence interval (0.95), and generates the future dates for forecasting using 'TABLE(GENERATOR(rowcount => 7))' to ensure the generation of 7 days for forecasting. Option D has OVER (ORDER BY NULL)' which is generally less performant than seq4().

NEW QUESTION # 249
You are a data analyst for an e-commerce company. You need to create a dashboard visualizing sales performance. The dashboard requires two key features: 1) users should be able to filter the data by product category dynamically using a dropdown list. 2) The dashboard should efficiently handle large datasets (millions of rows) without performance degradation. Which Snowflake features and techniques would you use to achieve these requirements most effectively?

A. Use Snowflake's dynamic data masking to hide sensitive sales data based on user roles, and create a view with pre-aggregated data for each product category to improve dashboard performance.
B. Create a stream on the sales data and use a task to continuously update a summary table with aggregated data for each product category. Use the summary table as the data source for the dashboard.
C. Extract all data into a pandas DataFrame and create a Dash application for the front end, as Snowflake struggles to handle real-time filtering effectively.
D. Utilize Snowflake's caching mechanisms and create materialized views to pre-compute aggregated data for the dashboard, along with using dashboard-level filtering widgets connected to the product category column.
E. Implement user-defined functions (IJDFs) in Python to perform custom filtering logic, and store the results in a temporary table for the dashboard to consume.

Answer: D

Explanation:
Using materialized views pre-computes aggregates and leverages Snowflake's caching. Dashboard-level filtering (option C) allows for dynamic filtering without querying the base data directly, providing the best performance and scalability. Options A and D are less ideal for real-time filtering; B is an anti-pattern in Snowflake, and E moves data out of Snowflake unnecessarily.

NEW QUESTION # 250
A data analyst is investigating a decline in the conversion rate on an e-commerce website. They have access to the following tables in Snowflake: 'sessions': 'session id', 'user id', 'start time', 'end_time' 'page views': 'session id', 'page_urr, 'view time' 'transactions': 'session_id', 'transaction id', 'amount', 'transaction_time' Which of the following approaches, using Snowflake features, would be MOST effective for identifying potential bottlenecks or drop-off points in the user journey?

A. Create a funnel analysis by defining key stages in the user journey (e.g., homepage visit, product page view, add to cart, checkout, purchase). Use window functions to track users as they progress through the funnel, calculating conversion rates between each stage. Visualize the funnel using a BI tool for easy identification of drop-off points.
B. Perform a cohort analysis by grouping users based on their sign-up date or initial website visit date. Track their conversion rates over time. Use a data visualization tool to see if any group exhibits an unusual drop in the conversion rate.
C. Use recursive SQL common table expressions (CTEs) to reconstruct the entire user journey for each session, from the entry page to either a successful transaction or session termination. Analyze path completion rates at each step to identify the pages where users are most likely to abandon the session.
D. Implement a data lineage tool to trace the flow of data from raw sources to the transaction table. This will reveal any data quality issues that may be impacting conversion rates.
E. Use Snowflake's 'SHOW TABLES' command to identify the most frequently updated tables. Then, create a dashboard on these tables to monitor the rate of changes.

Answer: A,B

Explanation:
Options B and C provide useful diagnostic insights. B offers direct information about conversion at each stage of the funnel. Option C enables discovery of unusual drops over time. Option A might be a difficult, resource intensive solution for complex user journeys. Option D is a poor approach as it identifies the rate of change in tables instead of the main objective - bottlenecks or drop-off points. Option E, while helpful for data governance, doesn't directly pinpoint user journey issues.

NEW QUESTION # 251
You are designing a dimensional model for a subscription-based service. You have a 'FACT SUBSCRIPTIONS' table with columns like 'subscription_id', 'customer id', 'start date', 'end date', and 'subscription_amount'. The business wants to analyze monthly recurring revenue (MRR) and churn rate. You need to model the temporal aspect of subscriptions to accurately calculate these metrics. Select the TWO best approaches to model the time dimension to facilitate these calculations:

A. Create a 'DIM_DATE table and link 'FACT_SUBSCRIPTIONS' to it using 'start_date' and 'end_date' columns.
B. Create a 'DIM_SUBSCRIPTIOW table with 'subscription_id' as the primary key and store all subscription details there, avoiding the need for a fact table.
C. Store 'start_date' and 'end_date' as VARCHAR columns in the 'FACT SUBSCRIPTIONS table to avoid data type conversions.
D. Create a 'DIM MONTH' table with columns like 'month id', 'month start date', and 'month end date' and link the 'FACT SUBSCRIPTIONS table to it based on the 'start_date' falling within the month.
E. Create a snapshot fact table FACT SUBSCRIPTION SNAPSHOTS that captures the state of each subscription at the end of each month. This table would include 'subscription_id' , 'customer_id', 'snapshot_date', 'is_active' , and 'subscription_amount' .

Answer: D,E

Explanation:
Creating a snapshot fact table (option B) allows for direct calculation of MRR and churn at a specific point in time. Analyzing subscription state at monthly intervals is helpful for these metrics. Creating a 'DIM_MONTH' table (option E) simplifies grouping and aggregation of subscriptions by month. Option A (linking to 'DIM DATE' using both start and end dates) might be useful for other types of analysis, but not directly for MRR/churn calculations. Storing dates as VARCHAR (option C) is bad practice and will hinder performance. A dimension table for subscriptions (option D) won't capture the temporal changes necessary for these metrics.

NEW QUESTION # 252
You have a Snowflake table 'EMPLOYEES' with columns 'EMPLOYEE ID' ONT, PRIMARY KEY), 'SALARY' and 'DEPARTMENT' (VARCHAR). You need to enforce the following business rules: 1. 'SALARY' must be a positive value. 2. 'DEPARTMENT' must be one of the following values: 'SALES', 'MARKETING', 'ENGINEERING'. 3. If the Employee is in 'SALES' Department, Salary should be between 50000 and 100000. Which of the following is the most appropriate and efficient approach using Snowflake constraints and features?

A. Use a CHECK constraint for 'SALARY > , an ENUM type for 'DEPARTMENT' , and a TRIGGER to enforce the salary range rule for the 'SALES' department.
B. Use a CHECK constraint for 'SALARY > , a CHECK constraint 'DEPARTMENT IN ('SALES', 'MARKETING', 'ENGINEERING') , and a third CHECK constraint 'CASE WHEN DEPARTMENT = 'SALES' THEN SALARY BETWEEN 50000 AND 100000 ELSE TRUE END'.
C. Enforce rules using stored procedure at the time of insertion and updation.
D. Use CHECK constraints for both rules and a third CHECK constraint to combine rules 2 and 3 to apply on each record.
E. Use CHECK constraint for SALARY, Create a Lookup table for Departments, and apply a Foreign key relationship for DEPARTMENT field in EMPLOYEE table.

Answer: B

Explanation:
Option D is the most appropriate and efficient. CHECK constraints are designed for these types of validations. The 'CASE' statement within the third CHECK constraint allows conditional validation based on the 'DEPARTMENT value. CHECK constriants are enforced at the time of record insert or update. Stored procedures could be an option but are not the most appropriate. Snowflake does not directly support ENUM types for column definitions. Creating Lookup table with Foreign key is another option.

NEW QUESTION # 253
......

The DAA-C01 certification verifies that you are a skilled professional. PassReview product is designed by keeping all the rules and regulations in focus that Snowflake publishes. Our main goal is that you can memorize the actual Snowflake DAA-C01 Exam Question to complete the SnowPro Advanced: Data Analyst Certification Exam (DAA-C01) test in time with extraordinary grades.

DAA-C01 Valid Exam Duration: https://www.passreview.com/DAA-C01_exam-braindumps.html

Our DAA-C01 study materials can come today, Why do customers give the priority to our DAA-C01 practice vce among the multitudinous products, Snowflake DAA-C01 Latest Exam Online You will have pre-trying experience before you buy it, Besides, we can ensure 100% passing and offer the Money back guarantee when you choose our DAA-C01 pdf dumps, If there is any renewal about DAA-C01 dumps PDF materials, the customers will receive it in the mail boxes as we will send it to them automatically.

In this chapter, you will learn the essentials of the Photoshop Camera Raw plug-in, Setting Up Your Camera for Continuous Shooting and Autofocus, Our DAA-C01 Study Materials can come today.

Why do customers give the priority to our DAA-C01 practice vce among the multitudinous products, You will have pre-trying experience before you buy it, Besides, we can ensure 100% passing and offer the Money back guarantee when you choose our DAA-C01 pdf dumps.

Benefits of Preparing with the DAA-C01

If there is any renewal about DAA-C01 dumps PDF materials, the customers will receive it in the mail boxes as we will send it to them automatically.

Cookie	Duration	Description
cookielawinfo-checkbox-analytics	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checkbox-functional	11 months	The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checkbox-necessary	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-others	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-performance	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
viewed_cookie_policy	11 months	The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.

Dan Hunt Dan Hunt

Biography

COOKIE NOTICE