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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q260-Q265):

NEW QUESTION # 260
You have configured a Snowpipe to load data from an AWS S3 bucket into a Snowflake table. The data in S3 is updated frequently. You've noticed that despite the Snowpipe being active and the S3 event notifications being configured correctly, some newly added files are not being picked up by the Snowpipe. You run 'SYSTEM$PIPE and see the 'executionstate' is 'RUNNING' but the 'pendingFileCount' remains at O, even after new files are placed in the S3 bucket. Choose all of the reasons that could explain the observations.

A. The S3 event notification configuration is missing the 's3:ObjectCreated: event type, meaning that new file creation events are not being sent to the SQS queue or SNS topic.
B. The SQS queue or SNS topic associated with the S3 event notifications has a message retention period that is too short. Messages containing event details for new files are being deleted before Snowpipe can process them.
C. The IAM role associated with your Snowflake account does not have sufficient permissions to read from the S3 bucket. Specifically, it lacks the 's3:GetObject' permission.
D. The file format specified in the Snowpipe definition does not match the actual format of the files being placed in the S3 bucket.
E. There is an insufficient warehouse size configured for the Snowpipe. Increase the warehouse size for optimal performance.

Answer: A,C,D

Explanation:
The problem states that the snowpipe is active, but 'pendingFileCount' remains zero. This means that the events are not making its way to snowpipe. Insufficient permissions (A) on the IAM role will prevent Snowflake from accessing the files. Incorrect event notification settings (B) would stop events from being sent to the queue/topic. Mismatched file format will not generate any events, and will cause files to be skipped. Incorrect message retension period may result in loss of messages before snowpipe could process them, hence can be the root cause. (C) is incorrect as it does not impact picking up of event notifications. Warehouse size mainly affects processing data and not the notification retrieval.

NEW QUESTION # 261
A data engineering team is implementing a change data capture (CDC) process using Snowflake Streams on a table 'CUSTOMER DATA'. After several days, they observe that some records are missing from the target table after the stream is consumed. The stream 'CUSTOMER DATA STREAM' is defined as follows: 'CREATE STREAM CUSTOMER DATA STREAM ON TABLE CUSTOMER DATA;' and the transformation code to process the data is shown below. What could be the possible reasons for the missing records, considering the interaction between Time Travel and Streams? Assume all table sizes are significantly larger than micro-partitions, making full table scans inefficient.

A. The stream's offset persistence is reliant on Time Travel, If the data being ingested is older than the set Time Travel duration, the change may not be seen by the stream.
B. The parameter for the database containing 'CUSTOMER_DATR is set to a value lower than the stream's offset persistence, causing some changes to be purged before the stream could consume them.
C. DML operations (e.g., DELETE, UPDATE) performed directly against the target table are interfering with the stream's ability to track changes consistently.
D. The stream's 'AT' or 'BEFORE clause in the consumer query is incorrectly configured, causing it to skip some historical changes.
E. The underlying table 'CUSTOMER DATA' was dropped and recreated with the same name, invalidating the stream's tracking capabilities.

Answer: A,B

Explanation:
Option A is correct: If the 'DATA RETENTION_TIME IN DAYS' is less than the time it takes to consume the stream, Time Travel will not be able to retrieve the changes, leading to missing records. Option E is also correct, as time travel duration plays a significant role.

NEW QUESTION # 262
You have a large dataset of JSON documents stored in AWS S3, each document representing a customer order. You want to ingest these documents into Snowflake using Snowpipe and transform the nested 'address' field into separate columns in your target table. Considering data volume, complexity, and cost efficiency, which approach is MOST suitable?

A. Pre-process the JSON documents using an external compute service (e.g., AWS Lambda) to flatten the 'address' field before ingesting into Snowflake via Snowpipe.
B. Use Snowpipe to ingest the raw JSON data into a VARIANT column, then create a view that flattens the 'address' field.
C. Use a COPY INTO statement with a transform clause to flatten the 'address' field during ingestion.
D. Create an external table on the S3 bucket and then use CREATE TABLE AS SELECT (CTAS) to transform the data.
E. Use Snowpipe with a user-defined function (UDF) written in Python to parse the JSON and flatten the 'address' field.

Answer: B

Explanation:
Using Snowpipe to ingest into a VARIANT column and then creating a view is generally the most cost-effective and flexible approach for handling semi- structured data and performing transformations in Snowflake. CTAS involves full table scans and is less efficient for ongoing ingestion. COPY INTO with transforms has limitations for complex nested structures. Pre-processing with Lambda adds complexity and cost. UDFs can be expensive for large datasets compared to Snowflake's native JSON processing capabilities.

NEW QUESTION # 263
You have a 'SALES table and a 'PRODUCTS table. The 'SALES table contains daily sales transactions, including 'SALE DATE , 'PRODUCT ID', and 'QUANTITY. The 'PRODUCTS table contains 'PRODUCT and 'CATEGORY. You need to create a materialized view to track the total quantity sold per category daily, optimized for fast query performance. You anticipate frequent updates to the 'SALES table but infrequent changes to the 'PRODUCTS table. Which of the following strategies would provide the MOST efficient materialized view implementation, considering both data freshness and query performance?

A. Create a standard materialized view that joins 'SALES' and 'PRODUCTS' , grouping by 'SALE DATE and 'CATEGORY without any specific clustering key.
B. Create two materialized views: one for daily sales by product and another joining the first with 'PRODUCTS' to aggregate by category. Cluster the first view by 'SALE DATE' and the second by 'CATEGORY'.
C. Create a standard materialized view that joins 'SALES' and 'PRODUCTS' , grouping by 'SALE_DATE and 'CATEGORY, and defining a clustering key on ' CATEGORY.
D. Create a standard materialized view that joins 'SALES' and 'PRODUCTS' , grouping by 'SALE_DATE and 'CATEGORY, and defining a clustering key on 'SALE DATE' and 'CATEGORY.
E. Create a standard materialized view that joins 'SALES' and 'PRODUCTS' , grouping by 'SALE_DATE and 'CATEGORY, and defining a clustering key on 'SALE DATE.

Answer: E

Explanation:
Option B is most efficient. Clustering the materialized view on 'SALE_DATE will significantly improve query performance when filtering or grouping by date, which is a common operation in time-series data. Although frequent updates will affect the maintenance costs of the materialized view, querying on date will be very efficient. Option A is less efficient due to the lack of clustering. Option C may not be the best choice if filtering/grouping primarily occurs on date. Option D is also good, but Option B is better if most of the query filter is on SALE DATE. Option E introduces complexity and two refreshes may create a delay in data available.

NEW QUESTION # 264
A financial institution is using Snowflake to store transaction data for millions of customers. The data is stored in a table named 'TRANSACTIONS with columns such as 'TRANSACTION ID, 'CUSTOMER ID', 'TRANSACTION DATE, 'TRANSACTION_AMOUNT, and 'MERCHANT CATEGORY'. Analysts are running complex analytical queries that often involve filtering transactions by 'TRANSACTION_DATE, 'MERCHANT CATEGORY' , and 'TRANSACTION_AMOUNT ranges. These queries are experiencing performance bottlenecks. The data team wants to leverage query acceleration service to improve performance without significantly altering the existing query patterns. Which of the following actions or combination of actions would be MOST beneficial, considering the constraints and the nature of the queries? (Select TWO)

A. Increase the size of the virtual warehouse used for running the queries and enable query acceleration on the warehouse without further modifications.
B. Enable Automatic Clustering on the 'TRANSACTIONS' table, ordering the keys as 'TRANSACTION_DATE, 'MERCHANT_CATEGORY', 'CUSTOMER_ID. Then, enable query acceleration on the virtual warehouse.
C. Create separate virtual warehouses dedicated to reporting queries and ad-hoc queries respectively. Enable query acceleration only for the warehouse running reporting queries.
D. Enable Search Optimization Service for the 'TRANSACTIONS' table, specifically targeting the 'MERCHANT_CATEGORY column. Enable query acceleration on the virtual warehouse.
E. Create materialized views pre-aggregating the transaction data by 'MERCHANT_CATEGORY and 'TRANSACTION_DATE, and enable query acceleration on the virtual warehouse.

Answer: B,D

Explanation:
Enabling Automatic Clustering on 'TRANSACTIONS with the specified key order ('TRANSACTION DATES, 'MERCHANT_CATEGORY , 'CUSTOMER_ID') aligns the data layout with common query patterns, allowing Snowflake to efficiently prune irrelevant data during query execution. This drastically improves query performance. Enabling Search Optimization on the 'MERCHANT_CATEGORY further enhances query performance by creating search access paths that enable faster lookups and filtering based on merchant category. Simply increasing the warehouse size (option A) may provide some improvement, but it's less targeted and potentially less cost-effective than optimizing the data organization. While dedicated warehouses (option C) can improve concurrency, they do not address the underlying performance bottleneck related to data access. Materialized views (option E) can be beneficial, but they require careful design and maintenance, and they might not be flexible enough for ad-hoc queries with varying filter conditions. Clustering and search optimization provide a more general and efficient solution in this scenario.

NEW QUESTION # 265
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