Online DAA-C01 Test Brain Dump Question and Test Engine [Q98-Q119]

Online DAA-C01 Test Brain Dump Question and Test Engine

Real Snowflake DAA-C01 Exam Dumps with Correct 198 Questions and Answers

NEW QUESTION # 98
When performing forecasting, which factors are essential for accurate predictions? (Select all that apply)

A. Relying solely on historical data without considering external influences
B. Incorporating statistical methods for prediction accuracy
C. Using basic arithmetic functions exclusively for forecasting
D. Examining trends and anomalies in historical data

Answer: B,D

Explanation:
Incorporating statistical methods and considering trends/anomalies are crucial for accurate predictions in forecasting.

NEW QUESTION # 99
In Snowsight, why is it significant to create various chart types (e.g., bar charts, scatter plots, heat grids) for data visualization?

A. Snowsight doesn't support multiple chart types for visualization.
B. It restricts users to specific chart types for simplicity.
C. Different chart types offer varied data representation for better analysis.
D. It limits data presentation options for complex datasets.

Answer: C

Explanation:
Diverse chart types offer varied data representation, facilitating better analysis in Snowsight.

NEW QUESTION # 100
You are using Snowpipe to continuously load JSON data from an external stage. Occasionally, some JSON records are malformed and cause the pipe to fail. You want to configure the pipe to skip these invalid records and continue loading valid data, while also capturing the error details for later analysis. Which approach provides the most efficient and appropriate solution for this scenario?

A. Use the = 'SKIP_FILE" option in the 'COPY INTO' statement used by the Snowpipe definition.
B. Use the 'VALIDATION MODE = RETURN ALL ERRORS parameter in the 'COPY INTO' statement and then filter the data based on the errors returned.
C. Implement custom error handling in your application code to pre-validate JSON records before uploading them to the stage.
D. Use the 'ON ERROR = 'CONTINUE'' option in the 'COPY INTO' statement used by the Snowpipe definition in conjunction with the 'VALIDATE function to capture error details.
E. Configure the Snowpipe definition to use the 'VALIDATE(O)' function within the 'COPY INTO' statement.

Answer: D

Explanation:
Option E is the most efficient and complete solution. SON ERROR = 'CONTINUE" allows Snowpipe to skip bad records and continue processing. Using it in conjunction with the 'VALIDATE function within the 'COPY INTO' statement enables capturing error information for analysis. This combines error skipping with error logging. Options A, B, C, and D are either less efficient (requiring pre-processing or post- processing of data), or do not provide a comprehensive solution for both skipping and capturing error information. Using (option B) is too coarse-grained as it skips entire files, even with only a few errors. Using 'VALIDATION MODE without ERROR=CONTINUE will still stop the pipe on errors.

NEW QUESTION # 101
A retail company is analyzing sales data to optimize product placement and promotional campaigns. They have sales figures, customer demographics, and promotional campaign details stored in Snowflake. Which visualization technique and Snowflake feature combination would BEST help them identify nuanced correlations between customer age, product category, and the success rate of different promotional campaigns, allowing for interactive exploration and drill-down capabilities?

A. Use Snowflake's built-in SQL to generate a correlation matrix between customer age and sales, and present it as a table in a static report. Implement UDF to calculate more customized Correlation coefficeint like Kendall+s Tau if data has non-normal distribution.
B. Utilize Snowflake's Data Marketplace to access pre-built dashboards focusing on retail analytics, adapting them to visualize the company's data without further customization.
C. Develop an interactive dashboard using Streamlit connected to Snowflake, implementing cross-filtering between visualizations of customer age distribution, product category sales, and promotional campaign ROI. Use stored procedures in Snowflake to pre-calculate aggregated data and improve dashboard performance. Utilize heatmap to find correlation between Customer Age and Sales for promotion campaigns
D. Export the data to a local CSV file and use Python's Pandas library to generate scatter plots showing the relationship between age, product category, and promotional campaign success. Create simple Histogram with no drilldown capabilities
E. Create a static bar chart in Tableau showing average sales per age group for each product category. Use Snowflake's aggregate functions to calculate averages.

Answer: C

Explanation:
Option B is the most appropriate because it leverages an interactive dashboard (Streamlit) connected to Snowflake, enabling cross- filtering and drill-down capabilities. Using stored procedures in Snowflake to pre-calculate aggregated data enhances dashboard performance. Option A provides a static view, lacking interactivity. Option C relies on a static table, which is not ideal for exploratory analysis. Option D involves exporting data outside Snowflake, which is inefficient and less secure. Option E might not provide the specific visualizations or level of customization needed for detailed correlation analysis. The use of the heatmap allows a clearer correlation view.

NEW QUESTION # 102
You are tasked with creating a Snowsight dashboard to monitor the daily sales performance of an e-commerce platform. The sales data is stored in a table named 'SALES_DATR with columns 'SALE_DATE' (DATE), 'PRODUCT_ID' ONT), 'SALES AMOUNT' (FLOAT), and 'CUSTOMER REGION' (VARCHAR). You need to display a trendline chart showing the total sales amount for each day over the last 30 days, and also a table showing the top 5 regions by total sales amount. Which of the following steps are most efficient to achieve this in Snowsight? (Select TWO)

A. Create a single tile with two tabs. The first tab displays the trendline chart using a time series and the second tab displays the region-based sales table. Both tabs use the same underlying SQL query but filter the results differently.
B. Create two separate tiles in the Snowsight dashboard. One tile should use a time series chart visualizing 'SELECT SALE_DATE, SUM(SALES_AMOUNT) FROM SALES_DATA WHERE SALE_DATE DATEADD(day, -30, GROUP BY SALE_DATE ORDER BY SALE_DATE;' and the other tile should use a table visualizing 'SELECT CUSTOMER REGION, SUM(SALES AMOUNT) FROM SALES DATA GROUP BY CUSTOMER REGION ORDER BY DESC LIMIT 5;'
C. Use the 'Notebook' feature in Snowsight to first perform the aggregations required to obtain the data for the visualizations, and then create visualization tiles by referencing the calculated dataframes. No direct SQL is used in the tiles.
D. Use two separate worksheets in Snowsight. In the first worksheet, create the time series chart. In the second worksheet, create the region-based sales table. Then, embed one worksheet inside the other to create a combined dashboard.
E. Create a single tile using a combination chart in Snowsight. Use 'SALE DATE as the x-axis and 'SUM(SALES AMOUNT)' as the y-axis, specifying a trendline. Then, add a second series to display region-based sales in a separate panel below.

Answer: B,C

Explanation:
Option A is correct because it directly and efficiently creates the desired visualizations in Snowsight using separate tiles. Option C is also correct. Using Snowsight's Notebook feature allows for pre-aggregation, which can be useful in complex scenarios where directly visualizing from the raw data would impact dashboard performance. Options B, D, and E are either not directly supported or less efficient methods within the current Snowsight dashboarding capabilities.

NEW QUESTION # 103
Which action aids in performing a diagnostic analysis on historical data to identify reasons/causes of anomalies?

A. Ignoring statistical trends in historical data
B. Collecting related data and demographics
C. Analyzing data solely from the past month
D. Focusing on isolated data points

Answer: B

Explanation:
Collecting related data and demographics is crucial in understanding the reasons/causes of anomalies in historical data.

NEW QUESTION # 104
You have a Snowflake table 'CUSTOMER DATA' containing customer information. You want to enrich this data using two separate data shares from the Snowflake Marketplace. Share A provides demographic information, and Share B provides credit risk scores. Both shares contain views named 'CUSTOMER ENRICHMENT with a common column 'CUSTOMER ID'. Due to compliance requirements, you need to ensure that only customers with a credit risk score above a certain threshold (e.g., 700) are enriched with demographic data'. Which of the following approaches ensures that the customer data is enriched securely, efficiently, and in compliance with the credit risk threshold?

A. Create a task that periodically runs a query that joins 'CUSTOMER DATA with both shared views (CUSTOMER ENRICHMENT from Share A and Share B), filtering based on the credit risk threshold. Insert the results into a new enriched table.
B. Create a single view that joins 'CUSTOMER_DATA' with both shared views ('CUSTOMER_ENRICHMENT from Share A and Share B) using a common table expression (CTE) to filter records from Share B to include only customers with a credit risk score above the defined threshold.
C. Create two separate views, one for each data share, and then join them based on in a final view, filtering for the credit risk threshold in the final view.
D. Create a stored procedure that iterates through the 'CUSTOMER_DATA' table, retrieves demographic and credit risk information for each customer, applies the credit risk threshold, and inserts the enriched data into a new table.
E. Replicate data from both data shares and perform enrichment and credit risk filtering on the replicated data.

Answer: B

Explanation:
Option C is the most secure, efficient, and compliant approach. Using a single view with a CTE allows you to encapsulate the credit risk filtering logic within the view definition, ensuring that only customers meeting the threshold are enriched. This avoids exposing sensitive credit risk information to unauthorized users. Views provide row-level security for the table. Options A requires two steps and is less efficient, Option B is less efficient and harder to maintain than a view, and Options D is not recommended for sharing scenarios. Option E will require an additional task creation. Using single view with CTE will simplify the query to implement it with minimum code.

NEW QUESTION # 105
How do materialized views differ from secure views in data analysis?

A. Secure views provide precomputed snapshots, unlike materialized views.
B. Materialized views restrict data access for improved security.
C. Materialized views offer enhanced data security while allowing selective data access.
D. Secure views precompute data, unlike materialized views.

Answer: D

Explanation:
Secure views offer enhanced data security without precomputing data, distinguishing them from materialized views.

NEW QUESTION # 106
Why are materialized views valuable in data analysis compared to regular views?

A. They offer real-time updates reflecting instantaneous database changes.
B. Regular views simplify complex data structures for ease of analysis.
C. Materialized views provide precomputed snapshots, improving query performance.
D. Materialized views restrict data access, enhancing security.

Answer: C

Explanation:
Materialized views provide precomputed snapshots, improving query performance.

NEW QUESTION # 107
You are tasked with cleaning and transforming a dataset containing customer information in Snowflake. This dataset includes columns such as 'CUSTOMER ID, 'NAME', 'EMAIL', 'PHONE NUMBER', and 'ADDRESS'. Your goal is to implement several data quality rules: 1) Ensure all phone numbers are in a consistent '+1-XXX-XXX-XXXX' format (where X is a digit). 2) Remove any leading or trailing whitespace from the 'NAME and 'ADDRESS' columns. 3) Replace invalid email addresses (identified by failing a specific regex pattern) with NULL. Which combination of Snowflake SQL statements would efficiently and accurately address these requirements?

Answer: A

Explanation:
Option D correctly addresses all requirements by performing all transformations within a single CREATE OR REPLACE TABLE statement. This ensures atomicity and avoids multiple update operations. It correctly trims NAME and ADDRESS, replaces invalid emails with NULL, and formats phone numbers if they have exactly 10 digits after removing non-numeric characters, otherwise assigning NULL. The correct option must format phone number as well as replace with NULL, while not replacing with NULL would mean keep original. Option E keeps original value not NULL, if phone number doesn't match criteria.

NEW QUESTION # 108
You are tasked with building a report to analyze customer churn. The data includes customer demographics, purchase history, website activity, and support interactions. You want to provide interactive filtering capabilities within the report, allowing users to explore the data based on various criteria'. Which Snowflake feature(s) offer the most effective way to implement interactive filtering within your reporting solution without directly exposing underlying tables?

A. Using Snowflake's Data Marketplace to directly share the raw tables with the reporting tool.
B. Creating static reports using a scheduling tool to generate PDFs.
C. Creating secure views and stored procedures, then implementing parameterized queries within the reporting tool that call the stored procedures with user-defined parameters.
D. Granting direct read access to all the underlying tables to the reporting tool's service account.
E. Using Snowflake's external functions to call a custom API that handles filtering logic.

Answer: C

Explanation:
Secure views and stored procedures (option B) provide a controlled interface for accessing the data. Secure Views protect the underlying schema. Stored procedures encapsulate the filtering logic and can validate the provided parameters. This is crucial for both security and data governance. Other options are either insecure (A, D) or don't provide interactive filtering (E). While external functions could potentially work, they add unnecessary complexity compared to stored procedures.

NEW QUESTION # 109
A company receives daily CSV files containing customer order data'. Each file contains a header row and is compressed using GZIP.
The files are landed in an AWS S3 bucket. The company wants to automate the data ingestion into a Snowflake table named 'orders table'. The requirements are: 1. Automated ingestion: New files should be automatically ingested as they arrive in the S3 bucket. 2. Data validation: Records with invalid dates or missing product IDs should be rejected and logged for review. 3. Data transformation: The column (string format 'YYYY-MM-DD') needs to be converted to a DATE data type, and a new column 'order _ year' needs to be derived from the 'order_date'. Which combination of Snowflake features and configurations provides the MOST efficient and reliable solution to meet these requirements?

A. Create a Snowpipe that points to the S3 bucket with a COPY INTO statement that performs the date conversion using TO DATE() and extracts the order year using YEAR(). Configure the COPY INTO statement with "ON_ERROR = 'CONTINUE" and a validation table to log rejected records.
B. Create a Snowpipe that points to the S3 bucket with a COPY INTO statement that utilizes a user-defined function (UDF) written in Python to perform complex data validation and transformation before loading the data into the 'orders_table'. Set 'ON_ERROR = 'SKIP_FILEP to avoid loading erroneous data.
C. Create an external table pointing to the S3 bucket. Use a stream on the external table to track changes and a task to periodically move the new data into the 'orders_table' while performing the necessary transformations and validations. This could also be achieved using Dynamic Tables.
D. Create a Snowpipe that points to the S3 bucket. Use a COPY INTO statement with 'VALIDATE(O)' and a BEFORE trigger to invoke a stored procedure that validates the data against a set of rules. Use a stored procedure to transform the data into 'orders_table' .
E. Create a Snowpipe that points to the S3 bucket with a COPY INTO statement that includes 'ON ERROR = 'SKIP_FILE". Use a downstream task to periodically validate and transform the data in the 'orders_table'.

Answer: A,C

Explanation:
Options B and C offer the best combination of features to address the requirements effectively. Option B leverages Snowpipe's COPY INTO statement to directly convert the date, calculate order year, and handle errors by continuing the load and logging invalid records into a validation table. This maximizes efficiency and ensures that valid data is ingested quickly. ON ERROR = 'CONTINUE' is better than SKIP FILE since it is preferable to ingest valid data in file even some has issues. Option C uses external tables combined with streams and tasks or dynamic tables which is an alternative to Snowpipe and COPY INTO and also provides automatic ingestion and transformation capabilities. Option A is less effective because it does not provide a mechanism to capture and log errors from the copy process; skipping files provides no insight to the validity of data. Option D is not the most efficient. While UDFs can handle complex transformations, relying solely on them for all validation and transformation steps can lead to performance bottlenecks and introduce maintenance overhead; Also setting ON_ERROR = 'SKIP_FILE' isn't a great pattern if you want to ingest partial data. Option E's BEFORE trigger might add significant overhead since Snowflake triggers have limitations.

NEW QUESTION # 110
How does operationalizing data contribute to maintaining reports and dashboards for business requirements?

A. It limits data accessibility, hindering dashboard usage.
B. Operationalizing data ensures consistent and efficient usage.
C. Operationalizing data complicates dashboard sharing.
D. It restricts data updates, affecting dashboard accuracy.

Answer: B

Explanation:
Operationalizing data ensures consistent and efficient usage of reports and dashboards.

NEW QUESTION # 111
You are building a Data Vault model in Snowflake. You have identified a Hub for Customers, a Link table relating Customers to Addresses, and several Satellite tables storing descriptive attributes of both Customers and Addresses. A new business requirement emerges: you need to efficiently query the model to find all Customers who have lived at the same Address as another Customer at any point in time. Which of the following approaches is MOST efficient and scalable for implementing this query in Snowflake, without significantly impacting the Data Vault's core principles?

A. Develop a stored procedure that iterates through all Customer records, comparing their Address histories, and stores the results in a temporary table.
B. Create a new Satellite table on the Hub_Customer that stores an array of Customer Hashkeys that have been associated with a given Address.
C. Use Snowflake's search optimization service on relevant columns (e.g., Address Hashkey in the Address Satellite) to accelerate the query.
D. Create a new Link table directly connecting Customers who share the same Address history, and populate it with a complex SQL query involving multiple joins on Hubs, Links, and Satellites.
E. Create a materialized view that pre-computes all Customer pairs sharing Address history. Refresh the view periodically or on-demand.

Answer: E

Explanation:
A materialized view is the most efficient and scalable option. It pre-computes the result, making subsequent queries very fast. Creating a new Link table within the Data Vault would violate its principle of representing facts as they occur. Search optimization service can help, but might not be as efficient as a pre-computed result. A stored procedure iterating through all records is highly inefficient. Adding an array to a Satellite table will cause potential data integrity issues and performance bottlenecks as the array grows, while also deviating from the data vault principles.

NEW QUESTION # 112
Your organization stores clickstream data in Parquet files in an external stage 's3://your-bucket/clickstreamP. The data includes nested JSON structures representing user activity. You need to create a Snowflake table to query this data efficiently, extracting specific fields from the nested JSON. The challenge is to optimize query performance by leveraging Parquet's columnar storage and schema evolution capabilities. Which of the following approaches offers the BEST combination of performance and flexibility for querying the data in Snowflake, considering potential schema changes in the Parquet files over time?

A. Create an external table with 'AUTO_REFRESH = TRUE and query the Parquet files directly using to access nested fields. Create a view on top of the external table to simplify queries.
B. Load the Parquet data into a VARIANT column in a Snowflake table using 'COPY INTO'. Create a view on top of this table, using JSON EXTRACT_PATH_TEXT to extract specific fields into separate columns.
C. Create an external table with 'AUTO_REFRESH = TRUE and specify a schema on read using 'FILE_FORMAT = (TYPE = PARQUET, STRIP OUTER ARRAY = TRUE, = TRUE)'. Query the external table directly.
D. Create a Snowflake table with clearly defined columns matching the Parquet schema. Use 'COPY INTO' with a transformation to load the data. Update the table definition whenever the Parquet schema changes.
E. Create an external table pointing to the Parquet files. Define a materialized view on top of the external table, extracting the required fields. Refresh the materialized view periodically.

Answer: E

Explanation:
Materialized Views offer the best performance as they pre-compute and store the results, leveraging Snowflake's caching. They also adapt to schema changes in underlying Parquet files (within limits). External tables alone can be slow because of on-the-fly processing. Loading into VARIANT loses the advantage of Parquet's columnar structure. Predefined columns are rigid and don't handle schema evolution well. Creating standard views on external tables also does not provide the pre-computed benefits of the materialized views.

NEW QUESTION # 113
You are building a dashboard in Power BI that connects to Snowflake. The dashboard needs to display the trend of daily active users (DAU) for the past year. The 'USER_ACTIVITY table in Snowflake contains columns: 'USER ONT), 'ACTIVITY DATE (DATE), and 'ACTIVITY TYPE (VARCHAR). Due to the large size of the 'USER ACTIVITY table, query performance is critical. Which of the following strategies will BEST optimize the query executed by Power BI against Snowflake to calculate DAU?

A. Import the entire 'USER ACTIVITY table into Power BI using Power BI Desktop's data import functionality and calculate DAU within Power BI'S data model.
B. Directly query the 'USER_ACTIVITY table from Power BI using a DAX measure to calculate distinct user counts per day. Rely on Power BI'S query folding capabilities to optimize the query sent to Snowflake.
C. Using a scheduled task in Snowflake, regularly create a summary table containing daily active users, then connect Power BI to that summary table.
D. Create a Snowflake view that calculates the DAU for each day using 'COUNT(DISTINCT USER_ID)' and 'GROUP BY ACTIVITY_DATE' , then connect Power BI to this view.
E. Create a materialized view in Snowflake that pre-calculates the DAU for each day, then connect Power BI to this materialized view.

Answer: E

Explanation:
Creating a materialized view that pre-calculates the DAU for each day is the most effective approach for optimizing query performance. Materialized views store the results of the query, so Power BI only needs to retrieve the pre-calculated DAU values, avoiding the expensive 'COUNT(DISTINCTY operation on the entire 'USER_ACTIVITY table. Option A might not fold completely and still execute poorly. Option B is better than A but does not provide performance like a Materialized View. Option D brings all the data into Power 31, which is not scalable or efficient. Option E is a valid option but has additional management overhead compared to materialized views.

NEW QUESTION # 114
You have two tables in Snowflake: 'ORDERS and 'CUSTOMERS. The 'ORDERS' table contains information about customer orders, including 'ORDER ID', 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. The 'CUSTOMERS table contains information about customers, including 'CUSTOMER ID', 'CUSTOMER NAME' , and 'CUSTOMER ADDRESS'. You need to create a view that joins these two tables based on 'CUSTOMER ID and includes only orders placed in the last 30 days. You also want to ensure that the view leverages the primary key information defined on the 'CUSTOMERS' table (even though Snowflake doesn't enforce it) for potential query optimizations. Which of the following SQL statements is the MOST efficient and best practice approach, considering Snowflake's optimizer?

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

Answer: C

Explanation:
Using 'CREATE OR REPLACE SECURE VIEW' is the best practice. 'CREATE OR REPLACE' allows you to update the view definition if needed without dropping and recreating it, minimizing disruption. A 'SECURE VIEW' prevents users from seeing the underlying query logic or accessing the base tables directly, enhancing security. The explicit JOIN syntax is preferred over the older implicit join syntax (option B). Snowflake's optimizer will leverage defined primary key relationships to optimize the join, even though it doesn't enforce them. Explicitly referencing schema isn't needed unless dealing with ambiguous names across schemas. The WHERE clause correctly filters for orders in the last 30 days.

NEW QUESTION # 115
You have been tasked with creating a reporting solution for a marketing campaign. The source data includes customer information (name, email, location), campaign details (campaign ID, name, start date, end date), and interaction data (customer ID, campaign ID, interaction type, timestamp). The business users want to analyze campaign performance based on customer segments, interaction types, and time periods. The data volume is expected to be moderately large (millions of records), and the reporting requirements are relatively stable. Which of the following data modeling and transformation strategies would be most efficient and maintainable?

A. Load the raw data into a single VARIANT column and use SQL with FLATTEN function for all reporting queries.
B. Create a star schema with a fact table for interactions and dimension tables for customers, campaigns, and time. Use Snowflake's clustering feature to optimize query performance.
C. Create a fully denormalized table containing all customer, campaign, and interaction data. Use SQL views to create different perspectives for reporting.
D. Load the data into separate tables for customers, campaigns, and interactions, without defining any relationships. Rely on ad-hoc joins in SQL queries for reporting.
E. Create a snowflake schema with normalized dimension tables for customers and campaigns. Use materialized views to pre-aggregate data for common reporting queries.

Answer: B

Explanation:
Option B offers the most efficient and maintainable solution. A star schema provides a good balance between query performance and data maintainability. The fact table stores the interactions, while the dimension tables provide context for customers, campaigns, and time. Snowflake's clustering feature can be used to physically organize the data on disk based on common query patterns, further improving performance. A fully denormalized table (Option A) can lead to data redundancy and update anomalies. Option C makes reporting complex and inefficient. Option D adds complexity without significant performance benefits in this scenario. Option E is inefficient for this use case. Given that we expect stable reporting requirements and moderately large data, it is best to flatten the data using a well-defined data model.

NEW QUESTION # 116
A Snowflake data warehouse contains a table 'CUSTOMER TRANSACTIONS with columns 'CUSTOMER ID, 'TRANSACTION DATE', 'AMOUNT', and 'PRODUCT CATEGORY'. Analysts frequently run queries that aggregate transaction amounts by product category for specific customer segments. The following query pattern is common:

Which of the following strategies, when implemented together, would BEST optimize the performance of this query pattern, considering both result caching and data access patterns?

A. Tune the virtual warehouse size to be as small as possible while still meeting performance requirements. Ensure the statistics on the table are up to date.
B. create a materialized view that pre-aggregates 'SUM(AMOUNT)' by 'PRODUCT_CATEGORY, 'CUSTOMER_ID , and 'TRANSACTION_DATE. Regularly refresh the materialized view.
C. Create indexes on 'CUSTOMER and 'TRANSACTION DATE columns of the CUSTOMER TRANSACTIONS table.
D. Create a view that encapsulates the 'WHERE' clause conditions (filtering by 'CUSTOMER ID and 'TRANSACTION DATE). Enable automatic query rewrite.
E. Cluster the 'CUSTOMER TRANSACTIONS' table by 'CUSTOMER and then 'TRANSACTION DATE. Create a materialized view preaggregating 'SUM(AMOUNT)' by PRODUCT_CATEGORY', and ' TRANSACTION_DATE

Answer: E

Explanation:
Clustering 'CUSTOMER TRANSACTIONS' by 'CUSTOMER and 'TRANSACTION DATE improves data access performance by organizing data physically based on the frequently used filter criteria. Creating a materialized view that pre-aggregates the results by PRODUCT CATEGORY, 'CUSTOMER ID', and 'TRANSACTION DATE' allows Snowflake to serve the results directly from the materialized view, significantly reducing the compute cost and improving query performance. Snowflake does not use traditional indexes; clustering provides similar benefits.

NEW QUESTION # 117
You are tasked with creating a data model for a global e-commerce company in Snowflake. They have data on customers, products, orders, and website events. They need to support complex analytical queries such as 'What are the top 10 products purchased by customers in the US who have visited the website more than 5 times in the last month?' The data volumes are very large, and query performance is critical. Which of the following data modeling techniques and Snowflake features, used in combination, would be MOST effective?

A. A star schema with fact and dimension tables, combined with clustering the fact table on a composite key of customer ID and product ID.
B. A data vault model, combined with Snowflake's search optimization service on the hub tables.
C. A fully normalized relational model with primary and foreign key constraints, combined with Snowflake's automatic query optimization.
D. A star schema with fact and dimension tables, combined with materialized views to pre-aggregate data and clustering on dimension keys in the fact table.
E. A wide, denormalized table containing all customer, product, order, and event data, combined with Snowflake's zero-copy cloning for data backups.

Answer: A,D

Explanation:
Options B and E are the most effective. A star schema (B and E) is well-suited for analytical workloads. Clustering the fact table on customer and product IDs (B) helps improve query performance when filtering on those dimensions. Materialized views (E) provide pre-aggregated data for common queries, further boosting performance. Normalization (A) can lead to too many joins. Data Vault (C) is complex and may not be necessary. A wide, denormalized table (D) can be difficult to manage and maintain, and zero-copy cloning is for backup, not performance. Clustering on dimension keys in the fact table works best when coupled with a star schema and the keys are frequently used as a filter.

NEW QUESTION # 118
Consider the following SQL query:

Analyzing the Query Profile, you observe that the 'WHERE' clause is not effectively filtering the data'. Which of the following actions could improve performance, assuming the 'order date' column is NOT currently clustered or indexed?

A. Cluster the 'orders' table on the 'order date' column.
B. Create a secondary index on the 'order_date' column.
C. Increase the virtual warehouse size.
D. Create a materialized view with the 'WHERE clause condition and relevant columns.
E. Partition the 'orders' table by 'order_date' .

Answer: A,D

Explanation:
Clustering on 'order_date' will physically organize the data on disk according to the date, allowing Snowflake to efficiently retrieve the relevant data based on the 'WHERE clause. Creating a materialized view pre-computes the result set based on the filter, so the query would only need to retrieve the top 10 from that smaller dataset. Secondary indexes are not supported in Snowflake. Partitioning is not a feature in Snowflake. Increasing the warehouse size might help with processing power, but it won't directly improve data access efficiency.

NEW QUESTION # 119
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Valid DAA-C01 Test Answers & Snowflake DAA-C01 Exam PDF: https://braindumps.free4torrent.com/DAA-C01-valid-dumps-torrent.html

Online DAA-C01 Test Brain Dump Question and Test Engine [Q98-Q119]

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