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DAA-C01 Guaranteed Success & DAA-C01 Certification Test Questions

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
You are tasked with building a data ingestion pipeline to retrieve data from a transactional database using Change Data Capture (CDC).The source database is a MySQL instance. Which of the following approaches is MOST suitable for efficiently retrieving and loading the changed data into Snowflake while minimizing latency?

A. Use a scheduled task to periodically query the MySQL database for records modified within a specific time window and load them into Snowflake using COPY INTO. Implement a timestamp-based or version-based change tracking mechanism in the query.
B. Create a Snowflake external table pointing to the MySQL database. Use the external table to read data directly from the MySQL database into Snowflake tables.
C. Use a third-party CDC tool that supports MySQL as a source and Snowflake as a target. Configure the tool to capture changes from the MySQL binary logs and stream them directly into Snowflake tables.
D. Create a Snowpark Python UDF that connects to the MySQL database, reads the binary logs, and applies the changes directly to Snowflake tables.
E. Configure MySQL binary log replication to a cloud storage location (e.g., AWS S3, Azure Blob Storage, Google Cloud Storage) and use Snowpipe to continuously ingest the binary log files into Snowflake.

Answer: C,E

Explanation:
Options B and C are the most efficient and reliable for CDC. Option B leverages the MySQL binary logs directly. Configuring replication to cloud storage and using Snowpipe provides a continuous and near real-time ingestion pipeline. Option C, using a dedicated CDC tool, offers a managed solution that handles the complexities of binary log parsing, change tracking, and data transformation. Option A is less efficient due to the need for periodic polling and potential data loss if changes occur between polls. Option D involves significant coding complexity and potential performance issues. Option E is generally not recommended for transactional data due to performance limitations.

NEW QUESTION # 13
A financial institution stores transaction data with various attributes, including transaction ID, account ID, transaction type, amount, and timestamp. They need to build a dashboard to monitor transaction volumes and identify unusual patterns in real-time. The data is streaming continuously. Consider these requirements: near real-time analysis, complex pattern recognition, and the need to store the raw transaction data for auditing. Which of the following approaches is optimal, considering the need for both speed and historical data integrity?

A. Use Snowflake Streams and Tasks to incrementally update a star schema data model in near real-time, with separate tables for transactions, accounts, and transaction types. Store raw data in a separate, immutable table.
B. Create a single denormalized table, indexed on transaction ID and timestamp. Use this table for both real-time analysis and historical storage.
C. Directly query the raw streaming data using Snowflake's external tables, without any data transformation or modeling. Rely on Snowflake's query optimization for real-time analysis.
D. Use Snowflake's Data Marketplace to access pre-aggregated financial data, avoiding the need for any data modeling or transformation.
E. Create a highly flattened table and use Snowflake's materialized views to aggregate data in near real-time. Store raw data in the same flattened table.

Answer: A

Explanation:
Option B is the most suitable solution. Snowflake Streams and Tasks provide a mechanism for incrementally updating a data model (in this case, a star schema) based on changes in the source data. This enables near real-time updates to aggregated tables used for the dashboard. Storing the raw data in a separate, immutable table ensures data integrity for auditing purposes. Option A might work, but managing large flattened tables with constant updates can be challenging. Option C is not efficient for complex analysis. Option D lacks the structure needed for complex joins and aggregations. Option E is irrelevant as it proposes using external data instead of processing the institution's own data.

NEW QUESTION # 14
You have a Snowflake table named 'CUSTOMER DATA' with a VARIANT column called 'PROFILE. This 'PROFILE column contains nested JSON objects with customer attributes. You need to extract the customer's first name from the 'PROFILE and handle cases where the 'firstName' field might be missing (NULL). Which of the following methods is the most efficient and concise way to achieve this?

Answer: E

Explanation:
Option E is the most efficient and concise. 'PROFILE:firstName' directly accesses the field, casts it to a string and 'IFNI-ILL' handles the potential NULL values, replacing them with 'Unknown'. Options A and C might return NULL if the field is missing. Option B is less efficient than direct path access. While option D works, casting to string is essential to ensure the correct datatype and to avoid any unexpected behaviors, making option E the ideal answer.

NEW QUESTION # 15
You're working with a 'WEB EVENTS' table in Snowflake that stores user activity data'. The table includes columns like 'USER ID' , 'EVENT TIMESTAMP', 'EVENT TYPE (e.g., 'page_view', 'button_click', 'form_submission'), and 'EVENT DETAILS' (a VARIANT column containing JSON data specific to each event type). You need to identify users who submitted a specific form ('contact_us') more than 3 times within a 24-hour period. However, you are concerned about data quality, and the 'EVENT TIMESTAMP' column might contain duplicate entries for the same user and event. Which of the following SQL queries is the MOST robust and efficient way to achieve this in Snowflake, ensuring that duplicate timestamps for the same user and 'contact_us' form submission are not counted multiple times?

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

Answer: E

Explanation:
Option B is the most robust and efficient for handling potential duplicate timestamps. Here's why: Inner Query & QUALIFY: The inner query filters for the specific event type ('form_submission') and form ID ('contact_us'). The 'QUALIFY' clause, along with OVER (PARTITION BY USER ID, EVENT TIMESTAMP ORDER BY EVENT TIMESTAMP) = 1', efficiently de-duplicates the data. It assigns a row number to each combination of USER_ID and 'EVENT_TIMESTAMP' , and only keeps the first occurrence (ROW_NUMBER = 1), thus removing duplicate timestamp entries for the same user. Outer Query & Aggregation: The outer query then groups the de-duplicated data by 'USER_ID' and uses 'HAVING COUNT( ) > to identify users who have more than 3 distinct form submissions. Here's why the other options are less suitable: A: This option doesn't handle duplicate timestamps. It will count each duplicate timestamp as a separate form submission, leading to inaccurate results. C: Option C has similar function as option B, however it's slightly less elegant using 'WHERE rn = 1 ' than using Snowflake's Qualify command D: This Option aggregates to just Date Level and thus doesn't provide a robust solution within a 24 hour Period. E: Option E uses a 'COUNT( ) OVER clause that doesn't provide de-duplication of the underlying data, thus a duplicate Event timestamp would be counted twice.

NEW QUESTION # 16
You are analyzing customer order data in Snowflake and need to determine if there is a statistically significant correlation between the number of items in an order ('ITEM COUNT) and the total order value CORDER VALUE'). You have a table named 'ORDERS' with columns 'ORDER ID', 'ITEM COUNT', and 'ORDER VALUE'. Which of the following Snowflake functions or methods, used in combination, would be the MOST appropriate and statistically sound for calculating the correlation coefficient between these two variables, taking into account the need to handle potential NULL values appropriately?

A. Use 'AVG' and 'STDDEV_POP functions to calculate the mean and standard deviation for both and Then, manually compute the correlation coefficient using these statistics.
B. Use the 'CORR function directly on the 'ITEM_COUNT and 'ORDER_VALUE columns. No special handling is needed for NULL values as 'CORR automatically ignores them.
C. Use a combination of 'WHERE clause to filter out rows where either 'ITEM COUNT or 'ORDER VALUE is NULL, and then apply the 'CORR function to the filtered data.
D. First, replace NULL values in both 'ITEM COUNT and 'ORDER _ VALUE with 0 using 'COALESCE, then apply the 'CORR function.
E. Use 'QUALIFY Clause with 'CORR function to determine the coefficient values, and then apply statistical significance tests manually by calculating p-values.

Answer: C

Explanation:
The 'CORR function in Snowflake can calculate the Pearson correlation coefficient. However, NULL values can affect the result. Option E correctly handles this by explicitly filtering out rows containing NULL values in either column using a 'WHERE' clause, ensuring that the 'CORR function is applied only to complete pairs of data. Replacing NULL with zero can skew the distribution. Manual computation using AVG and STDDEV POP are more error prone and time taking.

NEW QUESTION # 17
......

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