Free PDF Quiz 2025 DAA-C01: Pass-Sure SnowPro Advanced: Data Analyst Certification Exam Practice Questions

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q176-Q181):

NEW QUESTION # 176
A data analyst observes a sudden and significant drop in sales for a particular product category within a Snowflake database. Initial investigations point to a possible data quality issue. Which of the following steps provides the MOST effective and efficient diagnostic approach using Snowflake features to pinpoint the root cause of the anomaly, focusing on data integrity?

A. Disable all ETL processes and re-ingest the sales data overnight. This will overwrite the potential problematic sales data and resolve the drop in sales.
B. Utilize Snowflake's Time Travel feature to compare the data before and after the suspected anomaly. Query both datasets and perform set operations like EXCEPT or MINUS to identify changed records within the product category. Analyze those changed records for patterns.
C. Analyze query history in Snowflake to identify any recent DML (Data Manipulation Language) operations (e.g., UPDATE, DELETE) performed on the sales table, focusing on operations targeting the affected product category. Correlate these operations with the timeframe of the sales drop.
D. Create a clone of the sales table. Run a full table scan on the clone and perform descriptive statistics on the cloned data to detect statistical outliers without impacting the production environment. Compare with known good statistics.
E. Rebuild the entire product sales table from the raw data source to ensure data consistency, as the drop in sales suggests widespread data corruption. Compare the rebuilt table with the current table.

Answer: B,C

Explanation:
Options A and C are the most effective. A utilizes Time Travel for direct data comparison before and after the incident, allowing for focused analysis on changed records. C investigates DML operations, which could directly explain data changes. Option B is inefficient and disruptive. Option D, while helpful, might not pinpoint the cause of a data corruption issue as fast as A and C. Option E is a poor solution, as it doesn't identify the root cause of the issue and it leads to potential data lost from the transactions between the last successful load and when the ETL processes were disabled.

NEW QUESTION # 177
You are tasked with performing a descriptive analysis of website traffic data stored in a Snowflake table named 'website traffic'. The table includes columns such as 'session_id', 'user id', 'page_url' , 'timestamp' , and 'device_type'. Which of the following SQL queries would be MOST efficient and accurate for calculating the daily active users (DAU) and their device distribution?

Answer: D

Explanation:
Option E is the most efficient and accurate. It correctly uses user_id)' to calculate DALI, groups by date and device type, and orders the results. Option A is missing aggregation to calculate DAU per device. Option B uses APPROX COUNT DISTINCT which is less accurate. Option C counts all user_id entries, not distinct users. Option D includes user_id in the GROUP BY, causing incorrect DAU calculation, and calculates total users incorrectly.

NEW QUESTION # 178
A data analyst needs to process a large JSON payload stored in a VARIANT column named 'payload' in a table called 'raw events' The payload contains an array of user sessions, each with potentially different attributes. Each session object in the array has a 'sessionld' , 'userld' , and an array of 'eventS. The events array contains objects with 'eventType' and 'timestamp'. The analyst wants to use a table function to flatten this nested structure into a relational format for easier analysis. Which approach is most efficient and correct for extracting and transforming this data?

A. Employ a combination of LATERAL FLATTEN and Snowpark DataFrames, using LATERAL FLATTEN to partially flatten the JSON and then Snowpark to handle the remaining complex transformations and data type handling.
B. Use LATERAL FLATTEN with multiple levels of nesting, specifying 'path' for each level and directly selecting the desired attributes.
C. Load the JSON data into a temporary table, then write a series of complex SQL queries with JOINs and UNNEST operations to flatten the data.
D. Create a recursive UDF (User-Defined Function) in Python to traverse the nested JSON and return a structured result, then call this UDF in a SELECT statement.
E. Utilize a Snowpark DataFrame transformation with multiple 'explode' operations and schema inference to flatten the nested structure and load data into a new table.

Answer: B

Explanation:
Option A is the most efficient and Snowflake-native approach. LATERAL FLATTEN is optimized for handling nested data structures within Snowflake. While other options might work, they introduce overhead (UDF execution), are less efficient (temporary tables and complex SQL), or rely on external frameworks (Snowpark), making them less suitable for this scenario. Specifying the path ensures specific fields are targeted, avoiding unnecessary processing of irrelevant data. LATERAL flatten allows you to join the output of a table function with each row of the input table. This is essential to maintain the context (e.g., userId) from the outer table.

NEW QUESTION # 179
You are working on a data ingestion pipeline that loads data from a CSV file into a Snowflake table called The CSV file occasionally contains invalid characters in the 'Email' column (e.g., spaces, non-ASCII characters). You want to ensure data integrity and prevent the entire load from failing due to these errors. Which of the following strategies, used in conjunction, would BEST handle this situation during the COPY INTO command and maintain data quality?

A. Employ the 'VALIDATE function during the 'COPY INTO command to identify erroneous Email columns and use the 'ON ERROR = 'CONTINUE" along with using file format that specifies 'TRIM_SPACE = TRUE and ENCODING = 'UTF8".
B. Use a file format with 'VALIDATE UTF8 = TRUE, and 'ON ERROR='SKIP FILE". Create a separate stage containing invalid data to be handled at a later stage with another transformation job
C. Use the 'ON ERROR = 'SKIP FILE" option in the 'COPY INTO' command and then run a subsequent SQL query to identify and correct any invalid email addresses in the 'EmployeeData' table.
D. Use the 'ON ERROR = 'CONTINUE" option in the 'COPY INTO' command. Create a separate error queue table and configure the 'COPY INTO' command to automatically insert error records into the queue.
E. Use the ERROR = 'SKIP FILE" option in the 'COPY INTO' command along with a file format that specifies 'TRIM SPACE = TRUE and 'ENCODING ='UTF8".

Answer: A,D

Explanation:
Options B and E provide the most robust solution. ERROR = 'CONTINUE'' allows the load to proceed despite errors. Creating an error queue (implicitly handled by Snowflake if using allows you to examine and address the problematic records later. By including in the file format definition = TRUE' and 'ENCODING = 'UTF8" and 'VALIDATE' function during the 'COPY INTO command to identify erroneous Email columns, you can standardize character encoding. 'SKIP_FILE (options A, C, and D) might lose valuable data. While correcting data with SQL after the load (option C) is possible, capturing the error data directly during the load is more efficient.

NEW QUESTION # 180
You are using Snowpipe to continuously load data from an external stage (AWS S3) into a Snowflake table named 'RAW DATA. You notice that the pipe is frequently encountering errors due to invalid data formats in the incoming files. You need to implement a robust error handling mechanism that captures the problematic records for further analysis without halting the pipe's operation. Which of the following approaches is the MOST effective and Snowflake-recommended method to achieve this?

A. Disable the Snowpipe and manually load data using a COPY INTO statement with the 'ON_ERROR = 'SKIP_FILE" option, then manually inspect the skipped files.
B. Implement Snowpipe's 'ERROR _ INTEGRATION' object, configuring it to automatically log error records to a designated stage location in JSON format for later analysis. This requires updating the pipe definition.
C. Configure Snowpipe's 'ON_ERROR parameter to 'CONTINUE' and rely on the 'SYSTEM$PIPE_STATUS' function to identify files with errors. Then, manually query those files for problematic records.
D. Implement a custom error logging table and modify the Snowpipe's COPY INTO statement to insert error records into this table using a stored procedure called upon failure.
E. Utilize Snowpipe's 'VALIDATION_MODE' parameter set to to identify and handle invalid records. This requires modification of the COPY INTO statement to redirect errors to an error table.

Answer: B

Explanation:
Snowflake's 'ERROR INTEGRATION' feature, when configured with a pipe, automatically logs details of records that fail during ingestion to a specified stage. This provides a structured and readily accessible log of errors without interrupting the data loading process. Option A is not a native feature. Option B, while potentially usable, doesn't directly integrate with pipes as the PRIMARY mechanism. Option C involves more manual intervention and doesn't offer structured error logging. Option E defeats the purpose of automated loading via Snowpipe.

NEW QUESTION # 181
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Free PDF Quiz 2025 DAA-C01: Pass-Sure SnowPro Advanced: Data Analyst Certification Exam Practice Questions

Help You in Snowflake DAA-C01 Exam Preparation [2025]

Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q176-Q181):

Free PDF 2025 Snowflake First-grade DAA-C01: SnowPro Advanced: Data Analyst Certification Exam Practice Questions

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