Dumps of DEA-C02 Cover all the requirements of the Real Exam [Q83-Q105]

Dumps of DEA-C02 Cover all the requirements of the Real Exam

Correct Practice Tests of DEA-C02 Dumps with Practice Exam

NEW QUESTION # 83
You are designing a data sharing solution where the consumer account needs real-time access to a secure view that aggregates data from several tables in your provider account. The consumer should not be able to see the underlying tables. Which of the following approaches offers the MOST secure and efficient way to implement this data sharing while minimizing the risk of data leakage and performance impact on your provider account?

• A. Create a standard view that joins the tables and share the view using a data share. Implement row-level security policies on the underlying tables.
• B. Create a secure view that joins the tables and share only the secure view using a data share.
• C. Create a shared database and grant SELECT privilege on the underlying tables directly to the consumer's role.
• D. Create a UDF that encapsulates the data aggregation logic and share the UDF's result using a data share, calling the UDF on demand.
• E. Create a materialized view on top of the tables, refresh it periodically, and share the materialized view.

Answer: B

Explanation:
Secure views are specifically designed for data sharing while protecting the underlying data sources. Sharing the secure view ensures that the consumer only sees the aggregated data and cannot access the underlying tables directly. Options A and D expose the underlying tables, increasing the risk of data leakage. Option C introduces latency due to the materialized view refresh. Option E adds unnecessary complexity and potential performance overhead.

NEW QUESTION # 84
You have a Snowflake table named 'ORDERS clustered on 'ORDER DATE. After a significant data load, you want to evaluate the effectiveness of the clustering. Which of the following SQL queries, using Snowflake system functions, will provide insights into the clustering depth and overlap of micro-partitions in the 'ORDERS' table, specifically helping you identify whether re-clustering is necessary? Assume that the table

• A.
• B.
• C.
• D.
• E.

Answer: A

Explanation:
The query SELECT avg_depth, avg_overlap FROM is the correct approach. The function, when given the table name and the clustering key column(s), returns information about the clustering state. Using 'TABLE()' allows you to extract 'avg_deptm and 'avg_overlap', which are key metrics for assessing clustering effectiveness. 'avg_depth' indicates how well the data is clustered (lower is better), and 'avg_overlap' indicates the degree of overlap between micro-partitions (lower is better). A high 'avg_depth' or 'avg_overlap' suggests the need for re-clustering. Option A returns a JSON which is difficult to process to get the required metrics. Option B is missing the clustering key. Option C returns JSON and not the desired output. Option E is not valid SQL syntax in Snowflake.

NEW QUESTION # 85
You have data residing in AWS S3 in Parquet format, which is updated daily with new columns being added occasionally. The data is rarely accessed, but when it is, it needs to be queried using SQL within Snowflake. You want to minimize storage costs within Snowflake while ensuring the data can be queried without requiring manual table schema updates every time a new column is added to the S3 data'. Which approach is MOST suitable?

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

Answer: B

Explanation:
Option B is the most suitable because external tables with AUTO REFRESH enabled and properly configured file format (Parquet in this case) support schema evolution automatically. When new columns are added to the S3 data, Snowflake will detect these changes and update the external table's metadata accordingly. This eliminates the need for manual schema updates and minimizes storage costs because the data remains in S3. AUTO_REFRESH needs a configured notification integration. Option A will require manual ALTER TABLE ADD COLUMN commands which are not scalable, option C introduces unnecessary complexity with Spark. Option D using COPY INTO does not support external storage locations. Option E requires manual refresh.

NEW QUESTION # 86
You are responsible for ensuring data consistency across multiple Snowflake tables involved in a financial reporting system. You've noticed discrepancies in aggregate calculations between a 'TRANSACTIONS" table and a summary table 'MONTHLY REPORTS'. The 'TRANSACTIONS' table is frequently updated via streams and tasks. Which combination of the following strategies would be MOST effective in identifying and resolving these inconsistencies in near real-time?

• A. Implement a Snowflake task that periodically recalculates the 'MONTHLY_REPORTS' table from the 'TRANSACTIONS table and compares the results with the existing data, logging any discrepancies. Use a smaller warehouse size to minimize cost.
• B. Utilize Snowflake's Time Travel feature to compare the ' TRANSACTIONS' table and 'MONTHLY _ REPORTS' table at a specific point in time and identify the changes that led to the discrepancies.
• C. Use Snowflake's row access policies to restrict access to the 'TRANSACTIONS' table, forcing users to only access the 'MONTHLY REPORTS table.
• D. Implement data validation checks within the data pipeline (streams and tasks) that update the 'TRANSACTIONS' table to reject transactions that violate predefined business rules.
• E. Create a Snowflake alert that triggers when the difference in the total 'SALE_AMOUNT between the 'TRANSACTIONS' table and 'MONTHLY REPORTS' exceeds a predefined threshold within a specified time window.

Answer: B,D,E

Explanation:
Options B, C and D are the most useful. Option B leverages Time Travel to compare data at a specific point. Option C creates an alert to check the difference in total amount. Option D prevents inconsistent data from ever entering the table. Option A attempts to fix an issue, rather than preventing it and a small warehouse will take too long to complete. Option E restricts table access, but does not resolve inconsistent data, which is not what the prompt requests.

NEW QUESTION # 87
You are designing a continuous data pipeline to load data from AWS S3 into Snowflake. The data arrives in near real-time, and you need to ensure low latency and minimal impact on your Snowflake warehouse. You plan to use Snowflake Tasks and Streams. Which of the following approaches would provide the most efficient and cost-effective solution for this scenario, considering data freshness and resource utilization?

• A. Create a Stream on the target table and a Snowflake Task that runs every minute. The task executes a MERGE statement to apply changes from the Stream to the target table, filtering the Stream data using the 'SYSTEM$STREAM GET TABLE TIMESTAMP function to process only newly arrived data since the last task execution. Use 'WHEN SYSTEM$STREAM HAS to run the Task.
• B. Create a Pipe object in Snowflake using Snowpipe and configure the S3 bucket for event notifications to the Snowflake-provided SQS queue. Monitor the Snowpipe status using 'SYSTEM$PIPE STATUS and address any errors by manually retrying failed loads with 'ALTER PIPE REFRESH;'
• C. Create a single, root Snowflake Task that triggers every 5 minutes, executing a COPY INTO command to load all new data from the S3 bucket into a staging table, followed by a MERGE statement to update the target table. Use 'VALIDATE ( STAGE NAME '0'.////' before COPY INTO.
• D. Create a Stream on the target table and a Snowflake Task. The task executes a COPY INTO command into a staging table when the Stream has data and then a MERGE statement. Schedule the task to run continuously with 'WHEN SYSTEM$STREAM HAS but limit the 'WAREHOUSE SIZE' to
• E. Configure an AWS SQS queue to receive S3 event notifications whenever a new file is uploaded. Use a Lambda function triggered by the SQS queue to invoke a Snowflake stored procedure. This stored procedure executes a COPY INTO command to load the specific file into Snowflake. Use 'ON ERROR = CONTINUE' during COPY INTO.

Answer: B

Explanation:
Snowpipe is specifically designed for continuous data ingestion with minimal latency. It leverages event notifications and serverless compute resources, making it more efficient than polling-based approaches (Task + Stream) or Lambda function invocations. The use of 'SYSTEM$PIPE STATUS' for monitoring and 'ALTER PIPE ... REFRESH' for manual retries provides better control and error handling compared to manual COPY INTO commands and MERGE statements. Option A is inefficient, B is complex, C might have performance issues due to high concurrency and E requires more coding and Stream-related management.

NEW QUESTION # 88
You have a Snowflake table, 'raw_data', which contains a column 'data url' storing URLs pointing to CSV files with varying schemas. Each CSV file represents sales data, but the column names and data types can differ. You need to create a process to automatically discover the schema of each CSV file, load the data into Snowflake, and standardize the column names to 'order id', 'product id', 'quantity', and 'price'. Which of the following approaches best addresses this requirement, considering scalability and minimal manual intervention?

• A. Leverage a combination of Snowflake Scripting and External functions: create external function that infer the schema of the CSV, create temporary table based on identified schema, fetch the CSV data using SYSTEM$URL GET using snowflake scripting, copy the data into the temporary table, tranform the data into required structure, ingest into target table and finally drop the temporary table
• B. Create a Python-based external function that downloads the CSV file from the URL using a library like 'pandas', infers the schema using 'pandas.read_csv' , maps the discovered column names to the standardized names, and returns the data as a JSON string. Then, create a Snowflake table with a VARIANT column, call the external function for each URL, and load the returned JSON data into the table. Create a view on top of it.
• C. Create a Snowflake external table that points to the external stage. Define a single file format to be used by external table. Define a pipe that uses 'COPY INTO' to ingest data into external table from the files found at the file URLs.
• D. Use Snowpipe with auto-ingest to continuously load the CSV files into a VARIANT column in a staging table. Create a series of views on top of the staging table, each view attempting to extract data based on different potential schema variations. Union all the views together to create a single consolidated view.
• E. Create a stored procedure that iterates through each URL in 'raw_data' , downloads the CSV file using 'SYSTEM$URL_GET , parses the CSV header to determine the column names, manually maps the discovered column names to the standardized names, creates a temporary table with the discovered schema, loads the data into the temporary table, transforms the data to use the standardized column names, and then inserts the transformed data into a final target table. Drop the temporary table after successful insertion.

Answer: A,B

Explanation:
Option C is the most suitable approach. It leverages the power of Python and the 'pandas' library within an external function to handle the complexities of schema discovery and standardization. The external function isolates the data transformation logic, making the Snowflake SQL code cleaner. Option E is also valid as it encapsulates the schema discovery and dynamic table creation in Snowflake Scripting. Options A is error prone and not scalable. Option B uses 'VARIANT column, but requires creation of a lot of views. Option D is incorrect since External Tables do not support data coming from URLs but rather from external stages.

NEW QUESTION # 89
You are designing a data pipeline that involves unloading large amounts of data (hundreds of terabytes) from Snowflake to AWS S3 for archival purposes. To optimize cost and performance, which of the following strategies should you consider? (Select ALL that apply)

• A. Utilize the 'MAX FILE SIZE parameter in the 'COPY INTO' command to control the size of individual files unloaded to S3. Smaller files generally improve query performance in S3.
• B. Enable client-side encryption with KMS in S3 and specify the encryption key in the 'COPY INTO' command to enhance security.
• C. Use a large Snowflake warehouse size to parallelize the unload operation and reduce the overall unload time.
• D. Partition the data during the unload operation based on a high-cardinality column to maximize parallelism in S3.
• E. Choose a file format such as Parquet or ORC with compression enabled to reduce storage costs and improve query performance in S3.

Answer: B,C,E

Explanation:
Using a larger warehouse size allows Snowflake to parallelize the unload operation, reducing the time it takes to unload large datasets. Enabling client-side encryption with KMS ensures that the data is encrypted both in transit and at rest in S3, enhancing security. Choosing a columnar file format like Parquet or ORC with compression significantly reduces storage costs and improves query performance when the data is later accessed in S3. Partitioning based on a high-cardinality column can lead to a large number of small files, which can negatively impact query performance in S3. While 'MAX FILE_SIZE is useful, smaller files don't always improve query performance and can even be detrimental.

NEW QUESTION # 90
You are developing a Python script to perform bulk data updates in a Snowflake table. The script needs to update a large number of rows based on values from a Pandas DataFrame. Which of the following approaches is the most efficient and scalable way to achieve this using the Snowflake Python connector, minimizing the number of database operations?

• A. Construct a single, large 'UPDATE statement with multiple 'CASE WHEN' clauses to update all rows in a single operation.
• B. Iterate through the rows of the Pandas DataFrame and execute an 'UPDATE statement for each row using 'cursor.execute()'.
• C. Use with the option to insert the updated data into a staging table, then use a 'MERGE' statement to update the target table from the staging table.
• D. Create a temporary table in Snowflake, load the DataFrame into the temporary table using , and then use a single 'UPDATE' statement with a 'JOIN' to the temporary table.
• E. Use 'SnowflakeCursor.executemany()' with a list of tuples containing the update values.

Answer: C,E

Explanation:
Options B and D are the most efficient. Option B leverages staging tables and a MERGE statement, which is a highly optimized way to perform bulk updates in Snowflake. This minimizes the number of individual operations and takes advantage of Snowflake's internal optimization. Option D uses 'executemany()' , which sends multiple parameterized queries to Snowflake in a single network round trip, significantly improving performance compared to executing individual UPDATE statements. Option A is the least efficient, as it involves a separate database operation for each row. Option C might be feasible for a small number of updates but becomes unwieldy and inefficient for large datasets. Option E introduces unnecessary complexity with temporary tables; MERGE is a better solution.

NEW QUESTION # 91
You have a Python UDF in Snowflake designed to enrich customer data by calling an external API to retrieve additional information based on the customer ID. Due to API rate limits, you need to implement a mechanism to cache API responses within the UDF to avoid exceeding the limits. The UDF is defined as follows:

Which caching mechanism can be implemented MOST effectively WITHIN the Python UDF to minimize API calls while adhering to Snowflake's UDF limitations?

• A. Leverage external caching services like Redis by making API calls to Redis from the UDF to store and retrieve cached API responses. This would require configuring Snowflake to connect with external systems.
• B. Use the 'functools.lru_cache' decorator to cache the results of the 'get_customer details' function within the UDF's scope. This will automatically cache the most recently used API responses.
• C. Create a global dictionary within the UDF to store the API responses, using the customer ID as the key. Before calling the API, check if the customer ID exists in the dictionary; if it does, return the cached response. This approach will keep cached values during the session.
• D. Persist the API responses in a temporary table within Snowflake. The UDF will first query the temporary table for the customer ID; if found, return the cached data. Otherwise, call the API and store the response in the temporary table for future use.
• E. Utilize Snowflake's built-in caching mechanisms (result caching) by ensuring the UDF is deterministic and only depends on its input parameters. Snowflake will automatically cache the results of the UDF for subsequent calls with the same input.

Answer: B

Explanation:
Using 'functools.lru_cache' (Option A) is the most efficient and straightforward solution. It provides a built-in caching mechanism within the Python UDF's scope without requiring external dependencies or complex manual caching logic. Option B is not the best, as it will cause issues in multithreaded environment where this is not thread safe and could cause data inconsistency. Option C is related to Snowflake result cache which is independent of UDF cache needs and concerns. The temp table (option D) adds overhead by querying external tables within the UDF, making API execution slower rather than faster. And Option E needs external connections which increase infrastructure complexity.

NEW QUESTION # 92
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 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.
• B. 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.
• 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 an Iceberg table backed by the S3 bucket. Snowflake will automatically manage the metadata and handle incremental updates efficiently.
• E. Create a standard external table directly on the S3 bucket. Refresh the external table metadata using SALTER EXTERNAL TABLE ... REFRESH' on a daily schedule.

Answer: D

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 # 93
You are tasked with creating a Snowpark Java stored procedure to calculate a complex, custom rolling average for a time series dataset. This rolling average requires access to external libraries for statistical calculations. Which of the following steps are necessary to successfully deploy and execute this stored procedure?

• A. Grant the necessary privileges on the stage and the database to the role executing the stored procedure.
• B. Package the Java code and all necessary external libraries into a single JAR file.
• C. Create a stored procedure in Snowflake, specifying the fully qualified path to the JAR file in the stage, the handler class, and the return type.
• D. Upload the JAR file to a Snowflake stage.
• E. All of the above.

Answer: E

Explanation:
All the steps mentioned are necessary. The Java code and its dependencies must be packaged into a JAR (A), which is then uploaded to a Snowflake stage (B). The stored procedure needs to be created with a reference to the JAR file and the handler (C), and finally, appropriate permissions must be granted (D). Therefore, option E is the correct answer.

NEW QUESTION # 94
You are developing a Secure UDF in Snowflake to encrypt sensitive customer data'. The UDF should only be accessible by authorized roles. Which of the following steps are essential to properly secure the UDF?

• A. Using masking policies instead of Secure UDFs is the recommended approach for data security
• B. Setting the 'SECURITY INVOKER clause when creating the UDF to execute the UDF with the privileges of the caller.
• C. Using the 'SECURE keyword when creating the UDF to prevent viewing the UDF definition.
• D. Ensuring that the UDF is owned by a role with appropriate permissions and limiting access to this role.
• E. Granting the EXECUTE privilege on the UDF only to the roles that require access.

Answer: C,D,E

Explanation:
Secure UDFs protect the code definition. Granting EXECUTE privilege controls access to the UDE Ownership control is critical for managing permissions. SECURITY INVOKER, when used inappropriately can lead to security breaches if not properly managed and it executes with the privileges of the caller, potentially bypassing intended access restrictions. Masking policies are useful, but don't cover the core security functionality of secure UDFs, which hide the function's code itself.

NEW QUESTION # 95
You need to define a UDF in Snowflake that takes a date as input and returns the next business day (Monday-Friday). If the input date is a Friday, the UDF should return the following Monday. If the input date is a Saturday or Sunday, the function should return the following Monday as well. Which of the following UDF definitions correctly implements this logic?

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

Answer: A

Explanation:
Option E correctly handles all the scenarios. It first checks if the day is a Friday (DAYOFWEEK = 5) and adds 3 days. Then, it checks if the day is a Saturday or Sunday (DAYOFWEEK IN (6, 7)) and calculates the correct number of days to add to reach Monday (8 - DAYOFWEEK(input_date)). If it's any other day, it simply returns the input date as the question only asks to move to the next business day and not the next date if its already a business day. Option A is wrong because it always adds 1 day to Monday-Thursday which does not meet the requirement, the same goes to option B as well. Option C is wrong because Monday - Thursday will return the same date. Option D doesn't return next business day, but the next day.

NEW QUESTION # 96
You have a data pipeline that aggregates web server logs hourly. The pipeline loads data into a Snowflake table 'WEB LOGS' which is partitioned by 'event_time'. You notice that queries against this table are slow, especially those that filter on specific time ranges. Analyze the following Snowflake table definition and query pattern and select the options to diagnose and fix the performance issue: Table Definition:

• A. The table is already partitioned by 'event_time' , so there is no need for further optimization.
• B. Increase the warehouse size to improve query performance.
• C. Add a search optimization strategy to the table on the 'event_time' column.
• D. Change the table to use clustering on 'event_time' instead of partitioning to improve query performance for range filters.
• E. Create a materialized view that pre-aggregates the 'status_code' by hour to speed up the aggregation query.

Answer: C,D,E

Explanation:
Partitioning in Snowflake is primarily for data management and micro-partition elimination on exact matches, not range queries. Clustering (B) reorders the data for better performance with range-based queries. A materialized view (C) pre-computes the aggregation, significantly speeding up the specific query. A search optimization strategy (E) can improve performance without requiring a full table scan. Increasing warehouse size (D) may help to some extent but is not the most targeted optimization. Option A is incorrect because partitioning alone doesn't solve the range query performance issue.

NEW QUESTION # 97
A data pipeline ingests clickstream data from various sources into a raw Snowflake table CRAW CLICKS). A transformation job then processes this data and loads it into a more structured 'CLICK EVENTS table, performing filtering, cleaning, and data enrichment. The data engineering team notices significant performance bottlenecks during this transformation process, leading to data freshness issues.
The team wants to optimize this process, considering the following:

• A. Replace the transformation job with a series of smaller, more specialized jobs, each running on a separate virtual warehouse optimized for the specific task, and orchestrate these jobs using a data pipeline tool.
• B. Optimize the transformation queries by identifying and rewriting inefficient SQL patterns, ensuring appropriate use of joins, filtering conditions, and data type conversions.
• C. Implement a change data capture (CDC) mechanism on the source systems to only ingest changed data into 'RAW CLICKS, reducing the overall data volume and the amount of data processed by the transformation job.
• D. Create a materialized view on top of 'RAW CLICKS' that pre-computes the necessary transformations and aggregations, allowing the 'CLICK EVENTS' table to be populated directly from the materialized view.
• E. Use a larger virtual warehouse for the transformation job and partition the 'RAW CLICKS table on the ingestion timestamp to improve data pruning and reduce the amount of data processed during the transformation.

Answer: B,C,D

Explanation:
Implementing CDC reduces the amount of data processed. Materialized views pre-compute results, avoiding repeated transformations. Optimizing SQL queries improves the efficiency of the transformation logic. Using a larger warehouse helps, but doesn't address the fundamental issue of processing unnecessary data. Breaking the transformation job into smaller jobs may add overhead and complexity without a clear performance benefit. Partitioning is a database term and clustering is snowflake, the question relates to snowflake.

NEW QUESTION # 98
You are configuring a Snowflake Data Clean Room for two healthcare providers, 'ProviderA' and 'ProviderB', to analyze patient overlap without revealing Personally Identifiable Information (PII). Both providers have patient data in their respective Snowflake accounts, including a 'PATIENT ID' column that uniquely identifies each patient. You need to create a secure join that allows the providers to determine the number of shared patients while protecting the raw 'PATIENT ID' values. Which of the following approaches is the most secure and efficient way to achieve this using Snowflake features? Select TWO options.

• A. Create a hash of the 'PATIENT_ID' column in both ProviderA's and ProviderB's accounts using a consistent hashing algorithm (e.g., SHA256) and a secret salt known only to both providers. Share the hashed values through a secure view and perform a JOIN operation on the hashed values.
• B. Implement tokenization of the 'PATIENT_ID' column in both ProviderA's and ProviderB's accounts. Share the tokenized values through a secure view and perform a JOIN operation on the tokens. Use a third party to deanonymize the tokens afterwards.
• C. Leverage Snowflake's differential privacy features to add noise to the patient ID data, share the modified dataset and perform a JOIN.
• D. Share the raw 'PATIENT_ID' columns between ProviderA and ProviderB using secure data sharing, and then perform a JOIN operation in either ProviderA's or ProviderB's account.
• E. Utilize Snowflake's Secure Aggregate functions (e.g., APPROX_COUNT_DISTINCT) on the 'PATIENT_ID' column without sharing the underlying data. Each provider calculates the approximate distinct count of patient IDs, and the results are compared to estimate the overlap.

Answer: A,B

Explanation:
Options B and C represents valid approach. B provides good utility and is consistent. C does the same using a third-party service, which also works. Option A exposes the raw PII data which is not acceptable. Option D only gets an approximate, not an exact figure. While useful, the other solutions are much better. Option E is incorrect, it sounds good, but is not real. Therefore the correct answer is B and C.

NEW QUESTION # 99
You have a table named 'EVENT LOGS with columns including 'EVENT ID, 'EVENT TIMESTAMP', 'USER ID, 'EVENT_TYPE, and 'EVENT DATA (which stores JSON data). Users frequently query the table filtering by specific key-value pairs within the 'EVENT DATA column. Which of the following approaches will BEST improve query performance when filtering on values inside the JSON column, considering the use of search optimization?

• A. Convert the ' EVENT_DATX column to a VARCHAR column and enable search optimization on it.
• B. Extract the frequently queried key-value pairs from the 'EVENT_DATR JSON into separate virtual columns and enable search optimization on these virtual columns.
• C. Create a materialized view that extracts the key-value pairs from the ' EVENT_DATX column and enable search optimization on the materialized view's columns.
• D. Increase the warehouse size.
• E. Enable search optimization directly on the 'EVENT DATA' column.

Answer: B

Explanation:
Extracting the frequently queried key-value pairs into separate virtual columns and then enabling search optimization on these columns is the most effective approach (Option B). Snowflake's search optimization works best on columns with well-defined data types. Direct search optimization on a JSON column (Option A) is not directly supported and will not provide the desired performance benefits. Materialized views (Option C) are an option, but virtual columns are generally more lightweight for this scenario. Converting to VARCHAR (Option D) is not the correct approach for JSON data and would prevent proper JSON parsing and filtering. Increasing the warehouse size (Option E) might improve overall performance but doesn't specifically address the JSON filtering bottleneck.

NEW QUESTION # 100
You have a Snowflake table 'ORDERS' with billions of rows storing order information. The table includes columns like 'ORDER ID', 'CUSTOMER ID', 'ORDER DATE, 'PRODUCT_ID', and 'ORDER AMOUNT'. Analysts frequently run queries filtering by 'ORDER DATE' and 'CUSTOMER ID to analyze customer ordering trends. The performance of these queries is slow. Assuming you've already considered clustering and partitioning, which of the following strategies would BEST improve query performance, specifically targeting these filtering patterns? Assume the table is large enough for search optimization to be beneficial.

• A. Enable search optimization on the 'ORDER_ID column.
• B. Enable search optimization on the 'ORDER_DATE' column.
• C. Enable search optimization on the 'PRODUCT ID column.
• D. Create a materialized view that pre-aggregates the data based on 'ORDER_DATE and "CUSTOMER_ID
• E. Enable search optimization on both the 'ORDER DATE and 'CUSTOMER IDS columns.

Answer: E

Explanation:
Enabling search optimization on both 'ORDER_DATE and will directly benefit queries filtering by these columns. Search optimization is designed to significantly speed up point lookups and range scans. A materialized view (option D) might help, but it introduces the overhead of maintaining the view and might not be as flexible as search optimization for ad-hoc queries. Options A and E are incorrect since they focus on columns not frequently used in the specified filtering criteria.

NEW QUESTION # 101
You have a requirement to create a UDF in Snowflake that transforms data based on a complex set of rules defined in an external Python library. The library requires specific dependencies. You also need to ensure the UDF is secure and that the code is not visible to unauthorized users. Which of the following steps MUST be taken to achieve this?

• A. Create a Snowflake Anaconda environment specifying the required Python library dependencies. Then, create a Python UDF, reference the Anaconda environment, and use the 'SECURE' keyword.
• B. Create a Python UDF and directly upload the Python library code into the UDF's body. Snowflake automatically manages dependencies for UDFs.
• C. Create an external function pointing to an AWS Lambda function or Azure Function that hosts the Python code and its dependencies. Secure the external function using API integration and role-based access control.
• D. Package all the Python libaries code into one file, then create an Javascript UDF and load/execute the python code inside the Javascript UDF.
• E. Upload the Python library and its dependencies as internal stages. Create a Java UDF that executes the Python code using the 'ProcessBuilder' class. Mark the Java UDF as 'SECURE'

Answer: A

Explanation:
Using Snowflake Anaconda environments allows you to manage Python dependencies for UDFs. Creating a Python UDF referencing the environment and using the 'SECURE keyword ensures both dependency management and code protection. Uploading libraries as internal stages and using Java UDFs is an unnecessarily complex approach. Snowflake does not automatically manage dependencies; they must be explicitly specified through Anaconda. Creating a Python inside a Javascript UDF is not a supported pattern

NEW QUESTION # 102
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 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.
• B. 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.
• C. The file format specified in the Snowpipe definition does not match the actual format of the files being placed in the S3 bucket.
• D. 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.
• E. There is an insufficient warehouse size configured for the Snowpipe. Increase the warehouse size for optimal performance.

Answer: B,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 # 103
You are tasked with designing a data pipeline to load data from an Azure Blob Storage container into Snowflake using an external stage. The data is in CSV format, compressed using GZIP. The container contains millions of small CSV files. To optimize the data loading process and minimize cost, which of the following strategies would you implement, considering both stage configuration and COPY INTO options? Choose TWO that apply.

• A. Create a pipe object with 'AUTO INGEST = TRUE to automatically ingest new files as they are added to the Azure Blob Storage container. This ensures near real-time data ingestion.
• B. Use the 'VALIDATION MODE = RETURN ERRORS option in the 'COPY INTO' statement to identify and correct any data quality issues during the load. This ensures that only clean data is loaded into Snowflake.
• C. Consolidate the small CSV files in the Azure Blob Storage container into larger files before loading them into Snowflake. This reduces the overhead of processing numerous small files.
• D. Leverage Snowflake's Snowpipe with a REST API endpoint to trigger data loads whenever new files are available in the Azure Blob Storage container.
• E. Use the 'MATCH BY COLUMN NAME = CASE INSENSITIVE option with a copy transformation in the 'COPY INTO' statement to ensure that the column order in the CSV files doesn't affect the data load.

Answer: C,D

Explanation:
Consolidating small files into larger files (option E) significantly improves COPY INTO performance by reducing overhead. Using Snowpipe with a REST API endpoint (option C) allows for efficient, triggered data loading. 'VALIDATION MODE (option A) is useful for data quality but doesn't directly address optimization for millions of small files. 'AUTO INGEST (option B) is specific to AWS S3 and Google Cloud Storage, not Azure Blob Storage event notifications. Column matching (option D) addresses schema flexibility, not optimization.

NEW QUESTION # 104
You accidentally truncated a large table named 'SALES DATA' in your 'REPORTING DB" database. You realize this happened 2 days ago, and your account has the default Time Travel retention of 1 day. You need to recover this table with minimal downtime. Analyze the situation and determine the best course of action, considering cost and recovery time.

• A. Increase the account-level to 2 days and then use the UNDROP TABLE SALES_DATA' command.
• B. Immediately contact Snowflake Support to initiate a restore from Fail-safe, understanding that this process may take several hours or even days.
• C. Create a clone of the table using the 'AT clause and a timestamp from 1 day ago. This would prevent any additional cost.
• D. Raise a support ticket requesting data recovery from failsafe. Since data retention period has expired.
• E. Because the data retention period has expired, the table is unrecoverable using Snowflake's built-in features; you must restore from an external backup solution if available.

Answer: B

Explanation:
Since the Time Travel window (1 day) has passed, the 'UNDROP' command and cloning using or 'BEFORE clauses will not work (eliminating options B and D). While option C might be true if no external backups exist, the most appropriate first step is to contact Snowflake support (option A). This is because, even though Time Travel has expired, Fail-safe might still contain the data, offering a potential recovery path, although it is a longer process and not guaranteed. Option E is also a valid answer.

NEW QUESTION # 105
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