[Oct 13, 2025] DAA-C01 Practice Exam Dumps - 99% Marks In Snowflake Exam [Q41-Q65]

[Oct 13, 2025] DAA-C01 Practice Exam Dumps - 99% Marks In Snowflake Exam

Updated Verified DAA-C01 Q&As - Pass Guarantee or Full Refund

NEW QUESTION # 41
You are building a dashboard to monitor website traffic. You have the following requirements: 1. Display the number of unique visitors per day. 2. Allow users to filter the data by device type (desktop, mobile, tablet). 3. Show a trend line of unique visitors over time. 4. The dashboard must refresh every 15 minutes with the latest data,. 5. The dashboard must be performant even with a large volume of dat a. Given the following table definition:

Which of the following approaches would be the MOST efficient and scalable solution in Snowflake? Select all that apply.

• A. Create a materialized view to pre-aggregate the number of unique visitors per day and device type. Set up a Snowflake task to refresh the materialized view every 15 minutes. The dashboard queries the materialized view.
• B. Create a standard Snowflake view that calculates the number of unique visitors per day and device type. The dashboard queries the view directly, filtering by device type. No task or stream is used.
• C. Create a stored procedure to calculate the number of unique visitors per day and device type. Schedule the stored procedure to run every 15 minutes and update a table. The dashboard queries this table.
• D. Use a Snowflake stream to capture changes to the 'website_traffic' table. Create a task to process the stream every 15 minutes and update a summary table with the number of unique visitors per day and device type. The dashboard queries the summary table.
• E. Use the dashboard tool's built-in data transformation capabilities to calculate the number of unique visitors per day and device type on the fly, directly from the 'website traffic' table.

Answer: A,D

Explanation:
Materialized views (option A) and Streams with tasks (Option B) are the most efficient options for handling large datasets and real- time updates. Materialized views pre-compute the aggregates, which significantly speeds up query performance. A stream and task combination provides an incremental data processing approach, only processing new data every 15 minutes. This prevents full table scans and improves efficiency. A standard view (option C) will perform the calculation every time it's queried, leading to poor performance with large datasets. Using the dashboard tool's transformation capabilities (option D) is generally less efficient than leveraging Snowflake's compute power. Stored procedures (option E) can work but are generally less efficient than materialized views in this scenario.

NEW QUESTION # 42
Which statement accurately describes the usage of materialized views in data analysis?

• A. Materialized views are only accessible through stored procedures.
• B. They offer a precomputed, persisted snapshot of data, improving query performance.
• C. Materialized views are restricted to storing small subsets of data.
• D. Materialized views update in real-time, reflecting instantaneous changes in the database.

Answer: B

Explanation:
Materialized views provide precomputed snapshots of data, enhancing query performance by reducing computation overhead.

NEW QUESTION # 43
A company ingests sensor data into a Snowflake table named READINGS with columns (VARCHAR), 'reading_time' (TIMESTAMP NTZ), and 'raw_value' (VARCHAR). The 'raw_value' column contains numeric data represented as strings, but sometimes includes non-numeric characters (e.g., '123.45', 'N/A', '500'). You need to calculate the average of the numeric raw_value' readings for each within the last hour, excluding invalid readings. Which of the following Snowflake SQL statements will correctly accomplish this, handling potential conversion errors and filtering for valid data?

• A. SELECT sensor_id, 'N/A'))) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1 , CURRENT TIMESTAMP()) GROUP BY sensor_id;
• B. SELECT sensor_id, FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT _ TIMESTAMP()) GROUP BY sensor id;
• C. SELECT sensor_id, AVG(CASE WHEN THEN ELSE NULL END) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT TIMESTAMP()) GROUP BY sensor_id;
• D. SELECT sensor_id, raw_value, NULL))) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT TIMESTAMP()) GROUP BY sensor_id;
• E. SELECT sensor_id, FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1 , AND TRY_TO IS NOT NULL GROUP BY sensor_id;

Answer: E

Explanation:
Option B is the correct answer because 'TRY TO NUMBER attempts to convert the 'raw_value' to a number, returning NULL if the conversion fails. The 'AND TRY_TO_NUMBER(raw_value) IS NOT NULL' clause then filters out these NULL values, ensuring only valid numeric readings are included in the average calculation. Option A will throw an error if it encounters a non-numeric value. Option C, while functionally correct, utilizes which can be less reliable for specific locale formats compared to Option D is unnecessarily complex and less readable. Option E only handles 'N/A', not other potential invalid values.

NEW QUESTION # 44
You have a large dataset in Snowflake containing customer order information stored in a table named 'ORDERS' with columns 'ORDER_ID' ONT), 'CUSTOMER_ID' ONT), 'ORDER_DATE (DATE), 'TOTAL_AMOUNT' (FLOAT), and 'DISCOUNT_APPLIED' (BOOLEAN). You need to use Snowsight dashboards to analyze customer spending behavior and identify potential outliers. Which of the following visualizations, combined with appropriate SQL queries, would be MOST effective in identifying customers with unusually high or low order values? (Select TWO)

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

Answer: B,C

Explanation:
Options A and C are the most effective. A Box Plot (A) is ideal for identifying outliers in a distribution. By visualizing the distribution of total order amounts per customer, you can easily spot customers with unusually high or low spending. A Scatter Plot (C) directly shows the relationship between customer ID and total spending, making it easy to visually identify outliers based on their position relative to other data points. Option B is more suitable for trend analysis over time, and options D and E are useful but don't directly highlight individual customer outliers in terms of order value.

NEW QUESTION # 45
What role do secure views play in data analysis practices?

• A. Secure views limit access to data, hindering analysis.
• B. Secure views offer enhanced data security while allowing selective data access.
• C. They don't impact data security but significantly enhance query performance.
• D. They prevent the creation of materialized views.

Answer: B

Explanation:
Secure views enhance data security by allowing selective data access while benefiting analysis.

NEW QUESTION # 46
You are analyzing website traffic data in Snowflake to identify potential bot activity. You have a table 'WEB EVENTS' with columns 'event_timestamp' (TIMESTAMP NTZ), 'user_id' (VARCHAR), and 'ip_address' (VARCHAR). Which combination of SQL techniques and Snowflake features would be MOST effective in detecting and flagging suspicious bot-like behavior, considering high query performance and scalability?

• A. Use a UDF (User-Defined Function) written in Python to perform complex behavioral analysis on user event sequences, checking for patterns like rapid page transitions or form submissions within unrealistic timeframes. Apply this UDF to the 'WEB EVENTS' table.
• B. Implement a stored procedure that iterates through each unique IP address in the table, calculating the average time between events for each 12 Flag IP addresses where the average time between events is significantly below a pre-defined threshold.
• C. Create a scheduled task that periodically runs a query to analyze the ratio of human-generated events to server-generated events. If the ratio drops below a certain threshold, flag the time period as suspicious.
• D. Calculate event frequency per user and IP address using window functions (e.g., 'COUNT() OVER (PARTITION BY user_id, ip_address ORDER BY Then, identify users/lPs with abnormally high event rates within short time intervals using appropriate threshold criteria.
• E. Join the table with a publicly available list of known bot IP addresses. Flag any events originating from those IP addresses as potential bot activity. Supplement this with simple frequency counts of events per user.

Answer: D,E

Explanation:
Options B and C offer a good balance of effectiveness and efficiency. Option B uses window functions, a powerful feature within Snowflake for analyzing data within a context (user and IP address). Option C uses a pre-defined list of bots and it is not resource intensive. Option A, while potentially accurate, can be computationally expensive due to the use of a UDF and might affect the overall cluster performance. Option D is better suited to detect DDoS attacks. Option E is inefficient as it iterates through the resultset

NEW QUESTION # 47
A telecommunications company wants to segment its customers based on their usage patterns for targeted marketing campaigns. You have access to a table named 'CUSTOMER USAGE with the following columns: 'CUSTOMER ONT), 'DATA USAGE GB' (FLOAT), 'VOICE CALL MINUTES (INT), and (INT). Which of the following Snowflake features or techniques would be MOST appropriate for performing customer segmentation and determining distinct customer clusters?

• A. Implementing a K-Means clustering algorithm using Snowflake's Python User-Defined Functions (UDFs) and storing the cluster assignments in a new column within the 'CUSTOMER USAGE table.
• B. Creating a series of complex SQL queries with multiple 'CASE statements to manually define customer segments based on predefined thresholds for data usage, voice calls, and SMS count.
• C. Using Snowflake's built-in 'QUALIFY' clause combined with window functions to rank customers based on individual usage metrics and categorize them based on rank percentiles.
• D. Using the 'APPROX COUNT DISTINCT function to estimate the number of distinct usage patterns without performing actual clustering.
• E. Utilizing Snowflake's external functions to call a machine learning model hosted on a platform like AWS SageMaker or Azure Machine Learning to perform the clustering and return the segment assignments.

Answer: A,E

Explanation:
Options B and E are the most appropriate. Option B leverages Snowflake's UDF capabilities for in-database processing, allowing for potentially complex custom clustering algorithms. Option E allows integration with external machine learning platforms to take advantage of pre- built, optimized machine learning models. Option A is not appropriate because it just provides a count of distinct patterns, not the clustering itself. Option C is not scalable or maintainable for complex segmentation. Option D provides ranking, but not clustering or segmentation in the sense intended by the question.

NEW QUESTION # 48
Consider a scenario where you're analyzing website user behavior data in Snowflake. You have a table named 'user sessionS with a column containing semi-structured data (VARIANT type) describing user interactions during a session. You need to create a UDF that accepts and extracts all the distinct event types that occurred within that session. The UDF should return an array of unique event type strings. This array will be used later to identify users who have participated in a specific combination of events. Which of the following approaches can effectively achieve this using Snowflake's SQL extensibility features?

• A. Using an external function with a remote service to process 'session_data' and return JSON array with distinct event types. The external function setup must include API integration with the third party service and Role based access control. (Single Correct Answer)
• B. All of the above can satisfy the question. (Single Correct Answer)
• C. Implementing a Java UDF that parses the session data, extracts the event types, uses Java's HashSet to guarantee uniqueness, and returns the HashSet as a comma-separated string. (Single Correct Answer)
• D. Using a JavaScript UDF that iterates through the session data, extracts the event types, adds them to a JavaScript Set to ensure uniqueness, and then converts the Set to an array. This array is returned as a result. (Single Correct Answer)
• E. Creating a SQL UDF that utilizes Snowflake's ARRAY AGG and DISTINCT functions to aggregate all event types from the session data into an array, ensuring uniqueness. (Single Correct Answer)

Answer: D

Explanation:
The most efficient and appropriate solution is A. A JavaScript UDF allows for direct manipulation of the session data (VARIANT) and leveraging JavaScript's Set object for efficient uniqueness enforcement before returning the result as an array. This minimizes data transformation overhead within Snowflake's SQL engine. B: While SQL UDFs are an option, processing nested data and enforcing uniqueness within SQL can be less efficient compared to JavaScript's built-in capabilities. C: Using java, while possible, add overhead to the processing since Java UDF requires to setup class definitions and imports which can be an overkill for this use case. D: This is an external API integration which has extra overhead and latency. E: Is incorrect because all approaches have tradeoffs and can be implemented in certain instances based on requirements.

NEW QUESTION # 49
A key aspect of performing exploratory ad-hoc analyses is:

• A. Flexibility in querying and data exploration
• B. Limiting data sources
• C. Following a strict data model
• D. Relying solely on predefined hypotheses

Answer: A

NEW QUESTION # 50
A Snowflake table 'SALES_DATA' contains a 'TRANSACTION_ID' (VARCHAR), 'AMOUNT (VARCHAR), and 'TRANSACTION DATE (VARCHAR) column. Some 'TRANSACTION_ID' values are alphanumeric, others are purely numeric. The 'AMOUNT' column sometimes contains currency symbols ('$', ' ') or commas, and 'TRANSACTION DATE' is in 'MM/DD/YYYY' format. You need to perform the following transformations: 1. Extract only numeric 'TRANSACTION ID's. 2. Convert "AMOUNT' to a numeric type for calculations, removing currency symbols and commas. 3. Convert 'TRANSACTION DATE to a DATE type. Which of the following SQL queries effectively accomplishes these data type transformations in Snowflake?

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

Answer: B

Explanation:
Option E is the most comprehensive and robust solutiom - It uses 'REGEXP LIKE to filter out non-numeric and the CASE statement, which is important because 'TRANSACTION_lD's will have both numeric and alphanumeric values. - The 'AMOUNT' column correctly uses 'REGEXP_REPLACE and 'TRY_CAST to handle multiple currency symbols and converts the values to DECIMAL. -is used which handles incorrect data in DATE conversion and return NULL in case of invalid 'TRANSACTION_DATE. Option A is incorrect because IS_INTEGER is not a standard built-in Snowflake function. Option B can cause errors if the TRANSACTION_ID cannot be converted to INTEGER after being checked with REGEXP LIKE. Option C's CAST statements can cause errors if there's any data that cannot be correctly CAST.

NEW QUESTION # 51
You have a Snowflake environment where different data analysts run a variety of ad-hoc queries against the same set of tables. You've noticed inconsistent query performance, with some queries running quickly and others taking much longer despite having similar logic. To better manage costs and optimize performance, which of the following strategies would be MOST effective in leveraging virtual warehouse caching and resource management in Snowflake? (Select TWO)

• A. Configure the 'AUTO SUSPEND parameter on all virtual warehouses to be a very short duration (e.g., 60 seconds) to minimize costs when the warehouse is idle.
• B. Create separate virtual warehouses for different groups of analysts or types of queries to isolate workloads and prevent resource contention.
• C. Implement a single, large virtual warehouse shared by all data analysts to maximize resource utilization and caching benefits.
• D. Use resource monitors to limit the credit usage of individual virtual warehouses or user groups to control costs and prevent runaway queries.
• E. Disable result caching globally at the account level to ensure that all queries always retrieve the most up-to-date data.

Answer: B,D

Explanation:
Creating separate virtual warehouses (B) allows you to isolate workloads, preventing resource contention and ensuring consistent performance for different groups of analysts or types of queries. Resource monitors (D) help control costs by limiting credit usage, preventing runaway queries from consuming excessive resources. Sharing a single, large warehouse (A) can lead to resource contention. Short 'AUTO_SUSPEND (C) can lead to frequent warehouse startups, negating caching benefits. Disabling result caching (E) defeats a key performance optimization mechanism.

NEW QUESTION # 52
In diagnostic analysis, what importance do demographics and relationships hold in identifying anomalies? (Select all that apply)

• A. Considering relationships among data variables
• B. Identifying demographic variations linked to anomalies
• C. Ignoring data relationships for focused analysis
• D. Analyzing only recent demographic data for anomalies

Answer: A,B

Explanation:
Identifying demographic variations and considering relationships are crucial in identifying anomalies during diagnostic analysis.

NEW QUESTION # 53
Which actions are pertinent in identifying demographics and relationships during diagnostic analysis? (Select all that apply)

• A. Examining anomalies in isolation
• B. Ignoring data relationships for focused analysis
• C. Analyzing statistical trends
• D. Collecting related data

Answer: C,D

Explanation:
Analyzing statistical trends and collecting related data are crucial in identifying demographics and relationships during diagnostic analysis.

NEW QUESTION # 54
When maintaining reports and dashboards, why is it essential to build automated and repeatable tasks?

• A. Automated tasks increase dashboard management complexity.
• B. Automated tasks reduce manual efforts, ensuring consistency.
• C. They ensure inconsistency in reports and dashboards.
• D. Repeatable tasks hinder data updates in dashboards.

Answer: B

Explanation:
Automated tasks reduce manual efforts, ensuring consistency in reports and dashboards.

NEW QUESTION # 55
You are loading data from a series of CSV files into Snowflake using Snowsight. The files have varying column orders and some files are missing certain columns. You need to ensure that all data is loaded into a consistent table schema, handling missing columns gracefully.
Which of the following strategies is MOST effective in Snowsight to achieve this?

• A. Define a file format with 'SKIP_HEADER = 1 ' and load all CSV files into a single table with all possible columns defined as VARCHAR. After loading, use SQL queries with and to convert the data to the appropriate data types.
• B. Load the data into a staging table with a single VARIANT column. Then, use SQL with 'CASE statements and 'GET ' function to extract data from the VARIANT column into the target table with the desired schema.
• C. Pre-process the CSV files before loading using a scripting language (e.g., Python) to standardize the column order and add missing columns with NULL values. Then, load the pre-processed files into Snowflake using Snowsight.
• D. Define a file format with 'SKIP HEADER = 1', "FIELD OPTIONALLY ENCLOSED BY = and "NULL _ IF = (", 'NULL')'. Create a single table with all columns defined and use Snowsight's 'Load Data' wizard to load the CSV files. Columns not present in a given CSV file will automatically be populated with NULL.
• E. Create multiple file formats, one for each unique CSV file structure. Use Snowsight's 'Load Data' wizard to load each set of files with the corresponding file format. Use UNION ALL to combine the data from multiple tables into a single view.

Answer: D

Explanation:
Option D is the most effective strategy. By defining the file format with 'SKIP_HEADER = , FIELD_OPTIONALLY_ENCLOSED_BY ' and 'NULL_IF = (", 'NULL')', Snowflake can handle missing columns by populating them with NULL values during the load process. Creating a single table with all columns defined ensures data consistency. Option A works, but the type conversion after loading is less efficient and more error-prone. Option B requires managing multiple file formats and using UNION ALL, which can be complex. Option C using VARIANT will work, but adds extra complexity. Option E requires an external preprocessing step, which is less desirable.

NEW QUESTION # 56
A data analyst is optimizing query performance for a large reporting dashboard that accesses a Snowflake table 'SALES DATA' with millions of rows. The dashboard includes several complex calculations and aggregations based on 'SALE DATE and 'PRODUCT ID' The analyst observes that the dashboard load time is unacceptably slow, even after implementing standard query optimization techniques. Considering Snowflake's caching mechanisms and query profile, which of the following actions would MOST effectively improve the dashboard's performance while minimizing cost?

• A. Partition the 'SALES_DATX table by 'SALE_DATE to reduce the amount of data scanned during query execution. This avoids unnecessary scans.
• B. Implement query tags and monitor Snowflake query history using the 'QUERY HISTORY view to identify resource-intensive queries and optimize them using query rewriting or indexing techniques.
• C. Increase the virtual warehouse size to a larger configuration (e.g., from X-Small to Large) to ensure sufficient compute resources. This directly speeds up individual query execution.
• D. Implement result caching by ensuring that the underlying queries are deterministic and have not been modified. No action is needed; Snowflake automatically manages result caching.
• E. Create a materialized view that pre-calculates the aggregations needed by the dashboard. Refresh the materialized view periodically (e.g., daily) to maintain data freshness.

Answer: E

Explanation:
Materialized views offer a significant performance boost by pre-calculating and storing the results of complex aggregations. This reduces the computational load during dashboard refreshes. While increasing virtual warehouse size (A) provides more resources, it's often more cost-effective to optimize queries. Result caching (B) is automatic but depends on query determinism and recent execution. Partitioning (D) is not directly applicable to Snowflake. Query tags and history (E) are helpful for analysis but don't directly speed up dashboard load times.

NEW QUESTION # 57
You're building a Snowflake forecasting model to predict website traffic. Your dataset contains 'VISIT DATE (DATE), 'PAGE VIEWS (NUMBER), and 'PROMOTION FLAG' (BOOLEAN, indicating whether a promotion was active that day). You suspect that promotional periods significantly impact traffic, but need to account for days after a promotion that show residual impact. Which of the following strategies can you employ to improve your forecasting model to handle promotion and their lagging effects. Select two correct options.

• A. Use a simple moving average on the 'PAGE VIEWS' column over a 7-day period, ignoring the 'PROMOTION FLAG' entirely, as Snowflake's forecasting will automatically learn the promotional effects through the averaged data.
• B. Remove the 'PROMOTION FLAG' column entirely, as promotions introduce too much noise in the data and make accurate forecasting impossible.
• C. Use the 'HOLIDAY_DETECTION' parameter in the model creation statement. Snowflake will automatically detect promotions as holidays and incorporate them into the forecast.
• D. Create a new feature called 'DAYS SINCE PROMOTION' that calculates the number of days since the last promotion. Include this feature in the model's INPUT.
• E. Create multiple lagged features for 'PROMOTION FLAG'. For example, 'PROMOTION FLAG LAGI' would be the 'PROMOTION FLAG' value from the previous day, from two days ago, and so on. Include these lagged features in the model's INPUT.

Answer: D,E

Explanation:
Options A and C are correct. Option A helps the model directly capture the time elapsed since a promotion, allowing it to learn the decaying effect. Option C captures the lagged effects of promotions by including ' PROMOTION_FLAG' values from previous days as separate features. Option B is incorrect because simple moving average is a bad approach that may not be able to learn complex patterns of promotion effects on forecasting data, moreover promotional periods will be ignored. Option D is incorrect as promotions are valuable signals, not noise. Option E is incorrect because Snowflake's 'HOLIDAY DETECTION' feature automatically deals with typical public holidays, not self defined promotional campaigns.

NEW QUESTION # 58
How do materialized views differ from regular views in terms of data access and storage?

• A. Materialized views provide precomputed snapshots, unlike regular views.
• B. Regular views offer better storage optimization compared to materialized views.
• C. Regular views enhance data accessibility more effectively than materialized views.
• D. Materialized views restrict data access for improved security.

Answer: A

Explanation:
Materialized views provide precomputed snapshots, differentiating them from regular views.

NEW QUESTION # 59
You have a CSV file loaded into a Snowflake table named 'raw data'. The file contains customer order data, but some rows have missing values in the 'order date' column. You need to create a new table, 'cleaned data' , that contains only valid records and handles missing 'order date' values by substituting them with the date '1900-01-01'. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowflake features?

• A.
• B.
• C.
• D.
• E.

Answer: B

Explanation:
Option E is the most efficient and correct. 'COALESCE' efficiently handles NULL replacement, and ensures the replacement value is the correct data type (DATE). It also explicitly selects all other columns. Option A only filters out rows with null order_date. Options B, C and D creates a new column, . It does not also implicitly take all columns, which would make this more appropriate.

NEW QUESTION # 60
How can incorporating visualizations in reports and dashboards facilitate better data comprehension and analysis for business use scenarios?

• A. Visualizations don't impact data comprehension or analysis significantly.
• B. Presenting data visually increases complexity in analysis.
• C. They enhance data comprehension, aiding effective analysis.
• D. Visualizations limit data exploration and analysis capabilities.

Answer: C

Explanation:
Visualizations enhance data comprehension, aiding effective analysis in business use scenarios.

NEW QUESTION # 61
You're tasked with creating a Snowsight dashboard to monitor the performance of different ETL pipelines. The dashboard needs to display the average run time and the number of errors for each pipeline over the last 7 days. The data is stored in a table called 'ETL LOGS' with columns 'end_time', and 'error_flag' (boolean). You need to present this information in a way that users can easily compare the performance of different pipelines. Which of the following SQL queries, used as the basis for a Snowsight tile, would be MOST appropriate for this dashboard?

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

Answer: C

Explanation:
Option A is the most accurate. It correctly calculates the average run time in seconds using start_time, end_time)' and the error count using a conditional aggregation 'SUM(CASE WHEN error_flag THEN 1 ELSE 0 END)'. Option B is incorrect because - start_timey will not return runtime in seconds, it returns a fractional number of days. Option C uses IFF and datediff which is acceptable. Option D only counts the total logs and doesn't filter if there is an error. Option E is also technically correct by converting the boolean to a number to sum. Option A uses standard SQL which might be more preferable.

NEW QUESTION # 62
Which considerations are part of best practice for ensuring data integrity structures in Snowflake?
(Select all that apply)

• A. Using primary keys for tables
• B. Implementing redundant constraints
• C. Establishing parent-child table joins
• D. Ensuring data normalization

Answer: A,C

Explanation:
Data integrity practices in Snowflake involve using primary keys for tables and establishing effective parent-child table joins.

NEW QUESTION # 63
What complexities might arise when identifying and resolving data import errors in Snowflake?
(Select all that apply)

• A. Analyzing error logs for resolution
• B. Handling only specific error types
• C. Identifying error sources
• D. Resolving data inconsistencies

Answer: A,C,D

Explanation:
Identifying and resolving data import errors involves challenges related to identifying error sources, resolving inconsistencies, and analyzing logs for resolution, which might be complex depending on the nature of the errors.

NEW QUESTION # 64
How does leveraging clones aid in handling specific use-cases and maintaining data integrity in Snowflake?

• A. Clones facilitate real-time data updates
• B. Clones allow isolated testing and analysis without impacting original data
• C. Clones enforce data consistency across multiple warehouses
• D. Clones restrict data access for specific user roles

Answer: B

Explanation:
Clones in Snowflake enable isolated testing and analysis without affecting original data, supporting specific use-cases while maintaining data integrity by providing a separate environment for manipulation.

NEW QUESTION # 65
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