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Valid DAA-C01 Exam Pdf & DAA-C01 Detailed Answers

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q155-Q160):

NEW QUESTION # 155
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. 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. 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.
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: 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 # 156
A large fact table is partitioned by and clustered by 'customer _ id'. The table has the following columns: 'customer_id', and 'transaction_amount'. You need to optimize queries that frequently filter on a specific range of 'transaction_date' and then aggregate by 'customer _ id'. Given the existing partitioning and clustering, which of the following strategies will BEST improve query performance related to partition pruning and clustering?

A. Create a new table partitioned by and clustered by 'customer_id' and migrate data. Drop the Original Table.
B. No further optimization is needed, the existing partitioning and clustering are sufficient.
C. Add a secondary index on the 'transaction_date' column.
D. Recluster the table frequently using 'ALTER TABLE fact_transactions RECLUSTER;'
E. Create a materialized view that pre-aggregates 'transaction_amount' by 'customer_id' and 'transaction_date' .

Answer: E

Explanation:
Option B is the best strategy. Creating a materialized view that pre-aggregates the data by and 'transaction_date' addresses both aspects:Partition pruning is naturally leveraged because the materialized view will store aggregated data, allowing queries filtering on 'transaction_date' to use partition pruning during refresh and query. Clustering helps because the data within each partition (date) is clustered by 'customer_id' , making aggregations by customer efficient. Option A might not provide sufficient performance improvement if the aggregation by customer is still slow. Option C will improve query performance marginally but is not a good option with partition pruning, because the data is already partition on date. Option D reclustering too frequently can be costly and may not always result in significant performance gains. Option E can be a costly operation and also data migration may be hectic. Thus the best is to have materialized view.

NEW QUESTION # 157
You have a Snowflake table 'RAW DATA containing a 'VARIANT column called 'json_data'. This column stores JSON objects representing customer orders. The structure includes a nested array of items within each order. You need to create a flattened table called 'ORDER ITEMS with the following columns: 'order_id', and However, the field is not directly present in the JSON data'. Instead, it needs to be derived by concatenating the 'order_id' with the index (ordinal position) of the item within the 'items' array. The structure looks like this: { "order_id": "ORD-123", "customer_id": "CUST-456", "items": [ { "item_name": "Laptop", "item_price": 1200 }, { "item_name": "Mouse", "item_price": 25 } ] } Which of the following SQL statements correctly creates the 'ORDER ITEMS table?

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

Answer: B

Explanation:
Option E correctly uses the 'LATERAL FLATTEN' function to unnest the 'items' array. The key is using 'f.seq' (sequence number) provided by 'FLATTEN' function, which is the ordinal position of the item in the array (starting from 1), to create the item_id. It concatenates the order_id with the sequence number to generate a unique item_id. Option A is wrong because row_number is an aggregate function, and needs Group by to execute. Option B uses f.index, which does not exist in the output of Lateral flatten. Options C is correct in most of the parameters, however, 'raw_data' alias is missing, as a result the result will be error. Option D also uses seq, however it adds 1, which changes the index to start from 1, which might be wrong.

NEW QUESTION # 158
You are tasked with ingesting data from a REST API that returns data in JSON format. The API requires authentication via an API key passed in the header and returns a large dataset, potentially exceeding Snowflake's recommended file size for optimal loading. Furthermore, the API has rate limits, and exceeding them results in temporary blocking. What is the MOST efficient and reliable way to retrieve this data into Snowflake?

A. Employ a third-party ETL tool that supports REST API connections, data partitioning, rate limiting, and Snowflake integration. Configure the tool to extract, transform, and load the data into Snowflake.
B. Write a Snowpark stored procedure that invokes a Java UDF to connect to the REST API. Handle rate limiting and authentication within the Java UDF and use it to insert data directly into a Snowflake table.
C. Use a Snowflake external function to directly call the API within a COPY INTO statement. Handle authentication and rate limiting within the external function's code.
D. Create a Python script using Snowpark that connects to the API, handles authentication, rate limiting, and partitions the data into smaller files. Stage these files in cloud storage and then use COPY INTO to load them into Snowflake.
E. Utilize Snowpipe with a REST API integration configured directly with the API endpoint. Snowflake will automatically handle the data retrieval and loading.

Answer: A,D

Explanation:
Options B and D offer the most robust solutions. Option B, using a Python script with Snowpark, allows for fine-grained control over authentication, rate limiting, and data partitioning before loading into Snowflake. Option D, utilizing a dedicated ETL tool, provides a pre-built and often more scalable solution for complex data ingestion scenarios, including handling authentication, rate limits, and data transformation. Option A is less scalable and maintainable. Option C is incorrect because Snowpipe is designed for continuous loading from cloud storage, not directly from REST APIs. Option E is less efficient because it uses Java UDF inside stored procedure.

NEW QUESTION # 159
You observe that a Snowflake query, intended to perform aggregations on a 'SALES table (partitioned by 'SALE DATE), exhibits unexpectedly poor performance despite the data being relatively well clustered. Further investigation reveals that a user recently modified the 'SESSION' parameter NTE OUTPUT FORMAT to 'YYYY-MM'. The aggregation query filters the 'SALES' table using a 'WHERE clause on 'SALE DATE. Which of the following explains the performance degradation, and what actions can be taken to remediate?

A. The parameter is irrelevant to query performance as it only affects the output representation of dates. The performance issue is due to a different factor, such as insufficient warehouse size.
B. The modified causes Snowflake to perform implicit conversions on 'SALE_DATE in the 'WHERE clause, preventing partition pruning. Modify the query to use a consistent date format or reset the session parameter.
C. The change in increases the size of the query's result set, leading to network bottlenecks. Reduce the number of columns returned by the query.
D. The change in impacts the cost-based optimizer and impacts the explain plan, causing a full table scan, use 'ALTER SESSION SET DATE OUTPUT FORMAT = 'AUTO".
E. The change in alters the internal storage format of 'SALE_DATE, invalidating existing clustering metadata. Re-clustering the 'SALES' table is required.

Answer: B,D

Explanation:
The parameter itself doesn't change underlying data or invalidate clustering directly (A). While a larger result set can impact network (C), it's less likely than partition pruning issues in this scenario. 'DATE OUTPUT FORMAT can affect query performance if it causes implicit conversion on 'DATE columns in 'WHERE clauses, which can prevent partition pruning; setting it back to 'AUTO' or default behavior fixes this. The optimizer can be affected, forcing full table scan which is sub-optimal.

NEW QUESTION # 160
......

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