Download DEA-C02 Exam Dumps Questions to get 100% Success in Snowflake [Q94-Q113]

Download DEA-C02 Exam Dumps Questions to get 100% Success in Snowflake 

100% Accurate Answers! DEA-C02 Actual Real Exam Questions

NEW QUESTION # 94
A data engineer is using the Snowflake Spark connector to write data to a Snowflake table. The write operation fails consistently with the error 'net.snowflake.client.jdbc.SnowflakeSQLException: SQL execution error: String '. ' is too long (maximum is 16777216)'. Which of the following is the most likely cause and how can it be resolved using Spark Connector?

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

Answer: C

Explanation:
The error message clearly states that a string is too long. While options A might seem plausible, it is better to pre-process data on spark using 0, 16777216))'. This helps to ensure data conforms to the desired constraints before reaching snowflake. Option B addresses performance, but is not a root cause of the error. Option C relates to nulls but the error message is for maximum string length.

NEW QUESTION # 95
You need to unload data from a Snowflake table named 'CUSTOMER DATA to an AWS S3 bucket The data should be unloaded in Parquet format, partitioned by the 'CUSTOMER REGION' column, and automatically compressed with GZIP. Furthermore, you only want to unload customers whose 'REGISTRATION DATE is after '2023-01-01'. Which of the following 'COPY INTO' statement correctly achieves this?

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

Answer: A

Explanation:
The correct 'COPY INTO' statement requires using a named stage and a named file format. A subquery is used to filter the data based on the 'REGISTRATION_DATE. The 'PARTITION BY clause is used to partition the data by 'CUSTOMER REGION'. You must create a FILE FORMAT seperately and refer to it later. Other solutions have syntax errors, incorrect stage references, or incorrect ordering of clauses. Option 'A' doesn't use a stage nor it allows for a where condition. Option 'B' doesn't work since 'TYPE' and 'COMPRESSIONS are properties of a file format, not direct arguments to FILE_FORMAT. 'C' includes the 'TYPE and 'COMPRESSION' inline when this is not allowed. 'D' contains the same error with FILE FORMAT as 'C' and 'B' and does not use a stage.

NEW QUESTION # 96
You have a Snowflake view that joins three large tables: ORDERS, CUSTOMERS, and PRODUCTS. The query accessing this view is frequently used but performs poorly. You suspect inefficient join processing and potential skew in the data'. Which of the following strategies can be used to optimize the view's performance? (Select all that apply)

• A. Replace the view with a materialized view to precompute and store the results.
• B. Analyze the query profile to identify bottlenecks and potential data skew issues, and then re-cluster the underlying tables based on the most frequently used join keys.
• C. Use JOIN hints, such as BROADCAST or MERGE, to guide the query optimizer on join strategies.
• D. Increase the virtual warehouse size to provide more resources for query processing. Convert the view into a table using CREATE TABLE AS SELECT (CTAS).
• E. Partition the underlying tables based on the join keys to improve data locality.

Answer: A,B,C

Explanation:
Materialized views (A) can significantly improve performance by precomputing the results. JOIN hints (B) help the query optimizer choose the most efficient join strategy. Analyzing the query profile and re-clustering (C) addresses potential data skew and inefficient join processing. Increasing warehouse size (D) can help, but it's not a targeted solution for join performance. Partitioning (E) isn't directly supported in Snowflake; clustering is the analogous approach.

NEW QUESTION # 97
Your team is implementing Snowpipe Streaming to ingest data from multiple IoT devices into a Snowflake table. Each device sends data continuously, and it's crucial to minimize latency. However, you observe that some records are occasionally missing in the target table. You have verified that the devices are sending all data and the client-side application using Snowpipe Streaming is functioning correctly. Which of the following is the MOST likely cause of the missing records, and how can you address it?

• A. Duplicate records are being dropped due to Snowflake's internal deduplication mechanism; configure the table to allow duplicates.
• B. The Sequence Token is not being managed correctly, leading to potential gaps or out-of-order delivery; ensure correct implementation of the Sequence Token mechanism in your ingestion code.
• C. The Snowpipe Streaming client is exceeding its buffer capacity, leading to data loss; increase the buffer size on the client-side.
• D. The Snowflake virtual warehouse is not scaled up sufficiently to handle the ingestion rate; increase the virtual warehouse size.
• E. There is a delay in Snowflake's metadata refresh causing some new tables to be temporarily unavailable, retry after some time or manually refresh metadata.

Answer: B

Explanation:
C is the most likely cause. Snowpipe Streaming relies on a Sequence Token to ensure data is delivered in order and without gaps. If this token is not managed correctly on the client side, it can lead to missing records. A is less likely, as Snowflake doesn't automatically deduplicate data ingested through Snowpipe Streaming unless explicitly configured. B could contribute but is less likely if the client is generally functioning correctly. D affects performance but wouldn't typically cause missing records. E While possible, this scenario is highly unlikely.

NEW QUESTION # 98
You are building a data pipeline that involves ingesting data from AWS S3 into Snowflake using Snowpipe. The data arrives in small files frequently, and you are experiencing performance issues with delayed data availability. You need to optimize the pipeline for near real-time ingestion. Which combination of strategies will MOST effectively address this scenario?

• A. Configure S3 event notifications to trigger Snowpipe only when a sufficient number of files have arrived in the S3 bucket, using a serverless function (like AWS Lambda) to manage the file count threshold.
• B. Implement a micro-batching process using a third-party tool (like Apache Spark) to aggregate the small files into larger batches before loading them into S3, then configure Snowpipe to ingest the larger files.
• C. Enable auto-ingest on the Snowpipe and increase the frequency of S3 event notifications to Snowflake. Combine this with clustering the target table on a relevant column to optimize query performance after loading.
• D. Manually refresh the Snowpipe using the 'ALTER PIPE ... REFRESH' command every few minutes to force ingestion of new data.
• E. Increase the warehouse size used for the Snowpipe load process and adjust the 'MAX FILE SIZE' parameter on the pipe definition to match the size of the incoming files.

Answer: C

Explanation:
Enabling auto-ingest on the Snowpipe and increasing the frequency of S3 event notifications is the most effective way to handle frequent small files. Auto- ingest automatically loads files as soon as they are available, and frequent notifications ensure minimal delay. Clustering the target table improves query performance by organizing data based on a relevant column, leading to faster data retrieval. The option A related to MAX FILE SIZE is invalid since it does not exist in the context of Snowpipe. Options involving third-party tools add complexity and latency. Manually refreshing the pipe is not a scalable solution. Option C is not ideal because you want low latency, not delaying trigger. Option D provides the lowest latency at scale, while A, B, C and E would likely have a negative impact on latency, add extra steps, or are plain incorrect.

NEW QUESTION # 99
You have a Snowflake Stream named 'ORDERS STREAM' on an 'ORDERS' table, which is used to incrementally load data into a historical orders table named 'HISTORICAL ORDERS'. The data pipeline involves a series of tasks: 1) Consume changes from the 'ORDERS STREAM', 2) Apply transformations and data quality checks, and 3) Merge the changes into 'HISTORICAL ORDERS' using a MERGE statement. After a recent data load, you notice that the 'HISTORICAL ORDERS' table contains duplicate records for certain 'ORDER values. The MERGE statement uses 'ORDER ID' as the matching key. You have confirmed that the transformation logic is correct and idempotent. Examine the MERGE statement below. What could be causing the duplicates, given the context of Streams and incremental loading?

• A. Multiple tasks are concurrently consuming from the same 'ORDERS STREAM' without proper coordination, causing records to be processed multiple times.
• B. The MERGE statement is not correctly handling updates and deletes from the stream. The 'WHEN NOT MATCHED' and 'WHEN MATCHED' clauses are not mutually exclusive, leading to potential insertions of duplicate rows.
• C. The stream is not configured to capture DELETE operations from the ORDERS table, causing records that should have been removed in HISTORICAL ORDERS to remain.
• D. The 'ORDERS STREAM' is retaining historical data beyond the data retention period, causing older records to be re-processed.
• E. The stream's or 'BEFORE clause is being used incorrectly, potentially rewinding the stream to an earlier point in time.

Answer: A

Explanation:
The most likely cause of duplicate records, given the correct transformation logic and idempotent behavior, is D (Concurrent consumption from the stream) . If multiple tasks or processes are consuming from the same stream without proper coordination , they can both read the same changes and apply them to the 'HISTORICAL ORDERS' table. Option A (Retention Period) would cause data loss , not duplication, because older changes are simply lost. Option B is unlikely if you're not explicitly using or 'BEFORE' and even if you were, a simple stream consumption would advance the offset. If that clause was incorrectly configured, it would still not cause duplicates. Option C is possible but less likely if the transformation logic has been verified. If so many records are malformed that MERGE is broken, more evidence than duplication would be expected. Duplication alone with idempotent transformation suggests consumption error. Option E is a factor that could lead to 'HISTORICAL ORDERS' diverging from 'ORDERS but not a cause duplication.

NEW QUESTION # 100
You are tasked with implementing a Row Access Policy (RAP) on a table 'customer_data' that contains Personally Identifiable Information (PII). The policy must meet the following requirements: 1. Data analysts with the 'ANALYST role should only see anonymized customer data (e.g., masked email addresses, hashed names). 2. Data engineers with the 'ENGINEER role should see the full, unmasked customer data for data processing purposes. 3. No other roles should have access to the data'. You create the following UDFs: 'MASK EMAIL(email address VARCHAR)': Returns an anonymized version of the email address. 'HASH NAME(name VARCHAR): Returns a hash of the customer name. Which of the following is the most efficient and secure way to implement this RAP, assuming minimal performance impact is desired?

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

Answer: C

Explanation:
Option D is the most efficient because it filters access based on roles in the RAP without applying expensive UDFs within the policy itself. This minimizes the performance impact of the RAP. The view 'analyst_view' then applies the masking/hashing for analysts. Options A and B apply the UDFs within the RAP, which will significantly degrade performance. The 'MASK EMAIL(email_address) IS NOT NULL' conditions are also incorrect as they are not validating the email. Option C doesn't implement the required masking/hashing for analysts at all, and also is not as effecient. Option E allows both roles to see all data which does not meet requirement 1.

NEW QUESTION # 101
A data engineer is tasked with optimizing a Snowflake data pipeline that ingests data from multiple external sources, transforms it, and loads it into a reporting table. The pipeline uses a series of Snowflake tasks orchestrated with a root task and child tasks. Performance monitoring shows inconsistent execution times for the transformation tasks. Which of the following strategies would provide the MOST granular insights into the performance bottlenecks within the pipeline and allow for targeted optimization?

• A. Enable query profiling for all queries executed within the transformation tasks using 'ALTER SESSION SET QUERY PROFILE = 'ON" , then analyze the query profiles for performance bottlenecks after each task run.
• B. Leverage Snowflake's event tables like QUERY HISTORY and TASK HISTORY in the ACCOUNT USAGE schema joined with custom metadata tags to correlate specific transformation steps to execution times and resource usage. Also set up alerting based on defined performance thresholds.
• C. Implement a custom logging mechanism within the transformation tasks to record execution times for each stage of the transformation process, and store these logs in a Snowflake table for analysis.
• D. Use Snowflake's Resource Monitors to track overall warehouse consumption and assume that high consumption during transformation tasks indicates a bottleneck within those tasks.
• E. Rely solely on the Snowflake web UI's Task History view to identify slow-running tasks.

Answer: B

Explanation:
Option E is the most effective because it combines the structured data from Snowflake's account usage views (QUERY HISTORY, TASK_HISTORY) with custom metadata tags that provide context about each task's role in the data pipeline. This allows for granular analysis and correlation of transformation steps to performance metrics. Setting up alerts further proactively notifies the data engineer of performance deviations. Option B is good but harder to scale and maintain. Option D is less scalable and can be harder to interpret. The other options lack sufficient detail or provide only a high-level overview.

NEW QUESTION # 102
A large e-commerce company stores clickstream data in an AWS S3 bucket. The data is partitioned by date and consists of Parquet files. They need to analyze this data in Snowflake without physically moving it into Snowflake's internal storage. However, the data frequently changes, and they need to ensure queries reflect the latest updates to the files without significant latency. Which of the following approaches would be MOST suitable, considering cost, performance, and data freshness?

• A. Create a standard external table directly on the S3 bucket. Refresh the external table metadata using SALTER EXTERNAL TABLE ... REFRESH' on a daily schedule.
• B. Create an Iceberg table backed by the S3 bucket. Snowflake will automatically manage the metadata and handle incremental updates efficiently.
• C. Create a series of views on top of the S3 bucket using 'READ_PARQUET function, updating view definitions whenever underlying files change.
• D. Create a standard external table with the 'AUTO REFRESH' parameter set to 'TRUE'. This will automatically refresh the metadata whenever changes are detected in S3.
• E. Create an external table using a Snowflake-managed catalog. Configure a Snowpipe to automatically refresh the metadata as new files are added to the S3 bucket.

Answer: B

Explanation:
Iceberg tables are the most suitable option in this scenario. They address the limitations of standard external tables regarding metadata management and incremental updates. 'AUTO REFRESH' for external tables isn't ideal for frequent changes as it still relies on scanning the file system. A Snowflake-managed catalog with Snowpipe could work, but Iceberg is more purpose-built for handling the data evolution in place. READ_PARQUET in views is inefficient as it requires parsing the data every time the view is queried. Iceberg provides ACID properties and optimized query performance on data residing in external storage. The key benefit is the automatic metadata management provided by Snowflake for Iceberg tables.

NEW QUESTION # 103
Consider the following scenario: You are ingesting JSON data from an external stage into Snowflake. The JSON data contains an array of objects, where each object represents a product with attributes like 'product id', 'name', and 'price'. However, sometimes the 'price' field is missing entirely from some product objects. You want to load this data into a Snowflake table with columns 'product_id', 'name', and 'price' (defined as NUMBER). How can you handle the missing 'price' field gracefully during the COPY INTO operation, ensuring that missing prices are represented as NULL in the Snowflake table without causing errors?

• A. Define the 'price' column in the Snowflake table as VARCHAR. During data loading use the NULLIFEMPTY function within the COPY INTO statement: 'TRANSFORMATION=
• B. Define the 'price' column in the Snowflake table as NUMBER and use a transformation within the COPY INTO statement to handle missing prices: 'TRANSFORMATION = (price =
• C. Define the 'price' column in the Snowflake table as VARIANT. After the data is loaded, create a view that extracts the price using the 'GET' function and converts it to NUMBER using 'TO NUMBER. Handle NULL values in the view using 'price')), NULL, TO 'price')))'.
• D. Define the 'price' column in the Snowflake table as NUMBER and use the DEFAULT NULL clause. The COPY INTO statement will automatically insert NULL values for missing fields.
• E. Pre-process the JSON data before loading into Snowflake, adding a 'price': null field to any product object missing the price.

Answer: B

Explanation:
Option B is the most effective approach using Snowflake's built-in features. TRY TO NUMBER will return NULL if 'price' is missing, ensuring that NULL is inserted into the NUMBER column, avoiding errors. Option A does not work, DEFAULT NULL is not respected on column mappings. C does not work since VARCHAR and NULLIFEMPTY do not meet all the requirments. Option D works, but is not the most efficient. Option E requires unnecessary pre-processing.

NEW QUESTION # 104
You have a requirement to continuously load data from a cloud storage location into a Snowflake table. The source data is in Avro format and is being appended to the cloud storage location frequently. You want to automate this process using Snowpipe. You've already created the Snowpipe and the associated stage and file format. However, you notice that some files are being skipped during the ingestion process, and data is missing in your Snowflake table. What is the MOST likely reason for this issue, assuming all necessary permissions and configurations (stage, file format, pipe definition) are correctly set up?

• A. The data files in cloud storage are not being automatically detected by Snowpipe.
• B. The Snowpipe is paused due to exceeding the daily quota.
• C. Snowflake does not support Avro format for Snowpipe.
• D. The file format definition in Snowflake is incompatible with the Avro schema.
• E. The cloud storage event notifications are not properly configured to trigger Snowpipe.

Answer: E

Explanation:
Option D is the most likely reason. Snowpipe relies on cloud storage event notifications (e.g., SQS for AWS S3, Event Grid for Azure Blob Storage, Pub/Sub for Google Cloud Storage) to trigger data ingestion. If these notifications are not properly configured, Snowpipe will not be notified of new files being added to the storage location, resulting in skipped files. Option A is possible, but less likely if the pipe was just created and initial data loading is failing. Option B is incorrect; Snowpipe detects files based on event notifications, not by continuously scanning the storage location. Option C could be an issue, but the question states the file format is defined. Option E is incorrect; Snowpipe does support Avro format.

NEW QUESTION # 105
A Snowflake data warehouse is experiencing performance degradation. A critical reporting dashboard, which relies on a complex SQL query, is taking significantly longer to execute. You've examined the query profile and identified that a user-defined function (UDF) called 'PARSE ADDRESS, written in Python, is consuming a large portion of the execution time. The 'PARSE ADDRESS UDF is used to parse address strings into individual components (street, city, state, zip). What are the most effective strategies to improve the performance of this query, assuming it's difficult to rewrite the query without the UDF?

• A. Utilize external functions with a more performant compute service (e.g. AWS Lambda) if the environment allows.
• B. Create a materialized view that pre-calculates the output of the 'PARSE ADDRESS UDF for frequently queried address values and joins with the original table.
• C. Increase the virtual warehouse size to compensate for the UDF's overhead.
• D. convert the Python UDF 'PARSE_ADDRESS' into a Java UDE
• E. Rewrite the Python UDF 'PARSE ADDRESS in SQL using Snowflake's built-in string functions.

Answer: A,B,E

Explanation:
Options A, C and E are the best choices. SQL UDFs (A) are generally much faster than Python UDFs because they execute natively within Snowflake's engine. Materialized views (C) can significantly improve performance by pre-computing the UDF's output for commonly used values. External functions (E) allow leveraging a more efficient processing environment. While a Java UDF might be slightly faster than a Python UDF (B), the performance gain is unlikely to be substantial enough to justify the effort. Simply increasing the warehouse size (D) is a brute-force approach that doesn't address the underlying inefficiency of the Python UDF. The most effective solution is often to eliminate the Python UDF entirely if possible.

NEW QUESTION # 106
You are designing a data loading process for a high-volume streaming data source. The data arrives as Avro files in an AWS S3 bucket. You need to load this data into a Snowflake table with minimal latency and operational overhead. Which of the following combinations of Snowflake features and configurations would be MOST suitable for this scenario? (Select TWO)

• A. Configure an external table pointing to the S3 bucket and query the Avro files directly from Snowflake.
• B. Create a custom Spark application that reads Avro files from S3, transforms the data, and then writes it to Snowflake using the Snowflake Spark connector.
• C. Implement Snowpipe with auto-ingest configured to listen for S3 event notifications whenever a new Avro file is added to the bucket.
• D. Use the 'COPY INTO' command with a scheduled task that runs every 5 minutes to load new files from the S3 bucket.
• E. Use a Kafka connector to stream data directly from the Kafka topic to Snowflake.

Answer: C,E

Explanation:
Explanation: Options B and C offer the best combination of low latency and operational efficiency for streaming data. Snowpipe with auto-ingest provides near real-time loading triggered by S3 events. A Kafka connector provides a direct data stream to Snowflake. Option A introduces latency due to the scheduled task interval and doesn't scale well for high-volume streams. Option D adds operational overhead with Spark application management. Option E is suitable for ad-hoc querying but not ideal for continuous data loading.

NEW QUESTION # 107
You are using Snowpark Python to perform data transformation on a large dataset stored in a Snowflake table named customer transactions'. This table contains columns such as 'customer id', 'transaction date', 'transaction amount', and product_category'. Your task is to identify customers who have made transactions in more than one product category within the last 30 days. Which of the following Snowpark Python snippets is the most efficient way to achieve this, minimizing data shuffling and maximizing query performance?

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

Answer: A

Explanation:
Option E is the most efficient. It filters the transactions within the last 30 days, uses a window function to calculate the distinct count of product categories per customer, and then filters to identify customers with more than one category. The use of window functions allows the aggregation to happen within Snowflake, minimizing data transfer and leveraging Snowflake's processing power. distinct at the end makes it unique.

NEW QUESTION # 108
A data engineering team uses Snowflake to analyze website clickstream data stored in AWS S3. The data is partitioned by year and month in the S3 bucket. They need to query the data frequently for reporting purposes but don't want to ingest the entire dataset into Snowflake due to storage costs and infrequent full dataset analysis. Which approach is the MOST efficient and cost-effective way to enable querying of this data in Snowflake?

• A. Create a Snowflake external stage pointing to the S3 bucket, define an external table on the stage, and use partitioning metadata to optimize queries.
• B. Use Snowflake's COPY INTO command to ingest data directly from S3 into a Snowflake table on a scheduled basis.
• C. Create a Snowflake internal stage, copy the necessary files into the stage, and then load the data into a Snowflake table.
• D. Create a Snowpipe pointing to the S3 bucket and ingest the data continuously into a Snowflake table.
• E. Load all the data into a Snowflake table and create a materialized view on top of the table to pre-aggregate the data for reporting.

Answer: A

Explanation:
Using an external table pointing to the S3 bucket is the most efficient and cost-effective approach. It allows you to query the data directly in S3 without ingesting it into Snowflake, saving on storage costs. Partitioning metadata further optimizes query performance by allowing Snowflake to only scan relevant partitions based on the query criteria.

NEW QUESTION # 109
You're designing a near real-time data pipeline for clickstream data using Snowpipe Streaming. The data volume is extremely high, with bursts exceeding 1 million events per second. Your team reports intermittent ingestion failures and latency spikes. Considering the constraints of Snowpipe Streaming, which of the following strategies would be MOST effective in mitigating these issues, assuming the data format is optimized and network latency is minimal?

• A. Implement a message queue (e.g., Kafka) in front of Snowpipe Streaming to buffer incoming events and smooth out the traffic spikes.
• B. Increase the number of Snowflake virtual warehouses to handle the increased load.
• C. Implement client-side retry logic with exponential backoff and jitter to handle transient errors and avoid overwhelming the service.
• D. Switch from Snowpipe Streaming to Classic Snowpipe, as it is more resilient to high data volumes.
• E. Reduce the size of each micro-batch being sent to Snowpipe Streaming to minimize the impact of individual failures.

Answer: A,C

Explanation:
B and C are correct. Implementing client-side retry logic with exponential backoff (B) prevents overwhelming the service during transient errors. Using a message queue like Kafka (C) buffers the data, smoothing out traffic spikes and providing better resilience. A is less effective as scaling warehouses won't directly address client-side issues like retry logic and buffering. D can help but is not as effective as a buffering mechanism or robust retry strategy. E is incorrect as Snowpipe Streaming is designed for lower latency than classic Snowpipe.

NEW QUESTION # 110
A financial services company is implementing Snowflake. They have a table 'CUSTOMER DATA' containing sensitive information like 'CREDIT CARD NUMBER, 'SSN', and 'ADDRESS'. They need to ensure that: 1) Analysts can only see the last four digits of the 'CREDIT CARD NUMBER. 2) Data scientists require full access to the 'ADDRESS' but should not see the 'SSN'. 3) A dedicated compliance role should be able to view all data in its original format for auditing purposes. Which of the following is the MOST efficient and secure approach to implement this using Snowflake's data masking and RBAC?

• A. Create separate views for analysts and data scientists, applying masking policies within the views, and grant access to these views based on their respective roles. Additionally, grant the compliance role direct access to the base table.
• B. Replicate the CUSTOMER_DATA table three times, once for each user group (Analysts, Data Scientist and Compliance). Mask sensitive information by altering the data with the respective masking function.
• C. Create dynamic data masking policies on each sensitive column in the 'CUSTOMER_DATA table, associating these policies with specific roles using Snowflake's tag-based masking. Grant roles only the privileges needed to select the columns based on their requirements.
• D. Create masking policies on the 'SSN' , and 'ADDRESS' columns. Use conditional masking expressions based on the CURRENT ROLE() function to determine what data to show to each role (analysts, data scientists, compliance).
• E. Use data encryption for the entire 'CUSTOMER_DATA table and provide decryption keys to specific roles based on their access requirements. Provide the compliance role with the master key.

Answer: D

Explanation:
Conditional masking using the CURRENT ROLE() function within masking policies is the most efficient and secure approach. It allows a single table to be used while dynamically controlling data visibility based on the user's role. Views (Option A) can introduce maintenance overhead. Encryption (Option C) is generally used for data at rest and in transit and is not the correct solution for masking. Tag-based masking can add complexity when direct role-based masking is simpler. Replicating the table (Option E) would consume a huge amount of resource, and increase data duplication issues.

NEW QUESTION # 111
You are building a data pipeline using Snowflake Tasks to orchestrate a series of transformations. One of the tasks, 'task _ transform data', depends on the successful completion of another task, 'task extract_data'. However, occasionally fails due to transient network issues. You want to implement a retry mechanism for 'task_extract data' without impacting the overall pipeline execution time significantly. Which of the following approaches is the most appropriate and efficient way to achieve this within the Snowflake Task framework?

• A. Use the 'AFTER keyword in the 'CREATE TASK' statement for 'task_transform_data' to only execute if succeeds on its first attempt. If fails, the entire pipeline will stop, ensuring data consistency.
• B. Create a new root-level task that checks the status of 'task_extract_data'. If it failed, the root-level task will execute a copy of the 'task_extract data' task. After this, it updates the 'task_transform_data"s 'AFTER' condition to depend on the new task that retries extraction.
• C. Implement a TRY...CATCH block within the task definition to catch any exceptions. Inside the CATCH block, use SYSTEM$WAIT to pause for a few seconds, then re- execute the core logic of the task. Repeat this process a limited number of times before failing the task permanently.
• D. Modify the task definition to call a stored procedure. The stored procedure implements a loop with a retry counter. Inside the loop, execute the data extraction logic. If an error occurs, catch the exception, wait for a few seconds, and retry the extraction. After a specified number of retries, raise an exception to signal task failure.
• E. Configure the task with an error notification integration that sends alerts upon failure. Manually monitor these alerts and manually resume the task if it fails. Use 'ALTER TASK task extract data RESUME;'

Answer: D

Explanation:
Implementing the retry logic within a stored procedure called by the task (B) provides the most controlled and efficient way to handle transient errors. The stored procedure can handle the retry attempts, waiting periods, and error handling without requiring manual intervention or significantly impacting the overall pipeline. A is less desirable because SYSTEM$WAIT is generally discouraged in task definitions. C relies on manual intervention and doesn't automate the retry. D doesn't address the need for retries. E is overly complex and unnecessary. It also defeats the purpose of using 'AFTER' keyword.

NEW QUESTION # 112
A Snowflake data pipeline utilizes Snowpipe to ingest JSON data from cloud storage into a raw staging table 'RAW DATA' Subsequently, a series of transformation tasks are executed to cleanse, transform, and load the data into fact and dimension tables. You've noticed significant performance degradation in the transformation tasks, especially when dealing with large JSON payloads and deeply nested structures. Which of the following optimization techniques, applied at different stages of the pipeline, would MOST likely improve the overall performance of the data transformation tasks?

• A. Using the file format option when defining the Snowpipe integration to remove the outer array from the JSON data before ingestion.
• B. Partitioning the 'RAW DATA' staging table based on the ingestion timestamp to reduce the amount of data scanned during transformation.
• C. Increasing the virtual warehouse size used by the transformation tasks to provide more compute resources.
• D. Replacing the transformation tasks with external functions implemented in Python using Snowpark, leveraging the power of Pandas DataFrames for JSON processing.
• E. Employing Snowflake's 'LATERAL FLATTEN' function with appropriate 'PATH' expressions to efficiently extract the required attributes from the JSON data during transformation.

Answer: B,C,E

Explanation:
Options A, C, and D address different aspects of performance optimization. Increasing the virtual warehouse size (A) provides more resources for the transformation tasks. Using SLATERAL FLATTEN' effectively (C) optimizes JSON parsing. Partitioning the ' RAW_DATR table (D) reduces the data scanned, improving query performance. While (B) can be helpful, it depends on the JSON structure. Snowpark UDFs (E) can introduce overhead due to the serialization and deserialization between Snowflake and the external environment.

NEW QUESTION # 113
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