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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q108-Q113):

NEW QUESTION # 108
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. 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.
B. 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.
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. 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.
E. 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.

Answer: C,D

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 # 109
When choosing between using a dimensional model and a flattened dataset for BI requirements in Snowflake, what considerations impact the final decision? (Select all that apply)

A. Data denormalization and redundancy needs
B. Query performance expectations
C. User access control requirements
D. Query execution plan constraints

Answer: A,B

Explanation:
Considerations such as query performance expectations and data denormalization needs influence the choice between a dimensional model and a flattened dataset, affecting query optimization and data structure suitability for BI purposes.

NEW QUESTION # 110
You are analyzing website traffic data in Snowflake to identify potential bot activity. You have a table 'WEB EVENTS' with columns 'event_timestamp' (TIMESTAMP NTZ), 'user_id' (VARCHAR), and 'ip_address' (VARCHAR). Which combination of SQL techniques and Snowflake features would be MOST effective in detecting and flagging suspicious bot-like behavior, considering high query performance and scalability?

A. Calculate event frequency per user and IP address using window functions (e.g., 'COUNT() OVER (PARTITION BY user_id, ip_address ORDER BY Then, identify users/lPs with abnormally high event rates within short time intervals using appropriate threshold criteria.
B. Use a UDF (User-Defined Function) written in Python to perform complex behavioral analysis on user event sequences, checking for patterns like rapid page transitions or form submissions within unrealistic timeframes. Apply this UDF to the 'WEB EVENTS' table.
C. Implement a stored procedure that iterates through each unique IP address in the table, calculating the average time between events for each 12 Flag IP addresses where the average time between events is significantly below a pre-defined threshold.
D. Join the table with a publicly available list of known bot IP addresses. Flag any events originating from those IP addresses as potential bot activity. Supplement this with simple frequency counts of events per user.
E. Create a scheduled task that periodically runs a query to analyze the ratio of human-generated events to server-generated events. If the ratio drops below a certain threshold, flag the time period as suspicious.

Answer: A,D

Explanation:
Options B and C offer a good balance of effectiveness and efficiency. Option B uses window functions, a powerful feature within Snowflake for analyzing data within a context (user and IP address). Option C uses a pre-defined list of bots and it is not resource intensive. Option A, while potentially accurate, can be computationally expensive due to the use of a UDF and might affect the overall cluster performance. Option D is better suited to detect DDoS attacks. Option E is inefficient as it iterates through the resultset

NEW QUESTION # 111
You are responsible for maintaining a dashboard displaying real-time website traffic data'. The data is ingested into a Snowflake table named 'WEB EVENTS using Snowpipe from cloud storage. The 'WEB EVENTS' table includes 'EVENT TIMESTAMP' , 'PAGE URL' , and 'USER ID columns. The dashboard requires near real-time updates, but you are noticing significant latency. Which of the following actions, performed in isolation, is LEAST likely to improve the dashboard update frequency?

A. Refactor dashboard queries to directly query the 'WEB EVENTS' table without any aggregations or transformations.
B. Create a materialized view that pre-aggregates the data needed for the dashboard and configure it for automatic refresh.
C. Increase the size of the Snowflake virtual warehouse used for Snowpipe data loading.
D. Reduce the frequency of micro-batch data loading by increasing the Snowpipe 'AUTO_INGEST schedule to reduce the number of pipe executions per minute.
E. Optimize the Snowpipe configuration by adjusting the 'COPY INTO' statement to use file format options appropriate for the source data (e.g., compression, field delimiters).

Answer: D

Explanation:
Reducing the frequency of micro-batch data loading (option E) is LEAST likely to improve dashboard update frequency; in fact, it will decrease it. The dashboard needs near real-time updates, so reducing how often data is loaded will directly conflict with that requirement. All the other options are designed to help with optimization. Optimizing Snowpipe configuration, creating materialized views for pre-aggregation, and increasing warehouse size for Snowpipe can help, too. Reducing the amount of aggregations that dashboard needs will help since less processing is necessary.

NEW QUESTION # 112
You are building a real-time data pipeline to ingest IoT sensor data into Snowflake. The data arrives in Avro format via Kafka topics. You need to ensure data integrity, minimize latency, and accurately determine the data volume ingested for billing and monitoring purposes. Which of the following options provide the BEST combination of techniques to achieve these goals? (Select TWO)

A. Implement a custom Kafka consumer application that validates and transforms the Avro data before loading it into a staging table in Snowflake using the Snowflake Python connector. Use a Snowflake Task to move data from the staging to the final table.
B. Utilize Snowflake Streams and Tasks to create a change data capture (CDC) pipeline within Snowflake. Initially, load all data into a raw table. Then, use a stream to track changes, validate the data, and apply transformations incrementally.
C. Use Snowpipe with auto-ingest to continuously load data from a cloud storage location (e.g., AWS S3, Azure Blob Storage) where Kafka Connect is writing the Avro data. Configure Snowpipe error notifications to capture data quality issues. Do not perform Transformation.
D. Use Snowflake's Kafka connector to directly load data from Kafka into a raw data table. After loading, run scheduled tasks to perform data quality checks and transformations.
E. Implement a stream processing framework (e.g., Apache Flink, Spark Streaming) to consume data from Kafka, perform data quality checks and transformations, and then load the processed data into Snowflake using the Snowflake JDBC driver.

Answer: B,E

Explanation:
Options B and E provide the best combination. Option B uses a stream processing framework for real-time validation and transformation before loading into Snowflake, minimizing latency and ensuring data quality. Option E leverages Snowflake Streams and Tasks for CDC, enabling incremental data validation and transformation within Snowflake, ensuring data integrity and volume tracking.

NEW QUESTION # 113
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