Latest DAA-C01 Pass Guaranteed Exam Dumps Certification Sample Questions [Q19-Q43]

Latest DAA-C01 Pass Guaranteed Exam Dumps Certification Sample Questions

New DAA-C01 Test Materials & Valid DAA-C01 Test Engine

NEW QUESTION # 19
You have a table 'CUSTOMER DATA' with a column 'phone_number" (VARCHAR) that contains phone numbers in various formats (e.g., '123-456-7890', '1234567890', '+11234567890'). You need to standardize the phone numbers to a format of '1234567890' (no hyphens or country code). Which Snowflake SQL statement, using scalar string functions, will achieve this standardization while gracefully handling potentially invalid phone numbers (e.g., too short or containing letters) by returning NULL for invalid entries?

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

Answer: A

Explanation:
Option C is the correct answer. It first uses 'REGEXP REPLACE(phone_number, '[AO-91+', to remove all non-numeric characters from the phone number. Then, it uses a 'CASE statement to check if the resulting string has a length of 10 (a valid phone number length after standardization). If it does, the standardized phone number is returned; otherwise, NULL is returned. Option A only removes non-numeric characters but doesn't handle invalid lengths. Option B and D is doing same ,using 'IFF and adds unnecessary complexity by using 'IS NUMERIC' which is redundant since 'REGEXP REPLACE ensures only numbers exist. Option E filters on 'WHERE' Clause that reduces the record which are having length of 10, But Question needs to return NULL for invalid entries .

NEW QUESTION # 20
You are using Snowpipe to continuously load data from an AWS S3 bucket into a Snowflake table called 'ORDERS. The data is in JSON format. You observe that Snowpipe is occasionally missing records, even though the S3 event notifications are being correctly sent to the Snowflake-managed SQS queue. Upon investigation, you discover that some JSON records are larger than the maximum size supported by Snowpipe for a single record (16MB). You need to implement a solution to handle these oversized JSON records without losing data,. Which of the following approaches is the most efficient and reliable?

• A. Disable Snowpipe and switch to a batch loading approach using the COPY INTO command with automatic data compression. The COPY INTO command handles larger files more efficiently than Snowpipe.
• B. Use the 'VALIDATE function in Snowflake to identify oversized JSON records. Then, manually extract and split those records into smaller files and load them separately.
• C. Configure the S3 bucket to automatically split oversized JSON files into smaller files before they are sent to the SQS queue. Snowpipe will then process these smaller files independently.
• D. Increase the 'MAX FILE SIZE parameter of the Snowpipe configuration to accommodate the larger JSON records. Snowflake automatically handles oversized records by splitting them internally.
• E. Implement a pre-processing step using an AWS Lambda function triggered by S3 events to split the oversized JSON records into smaller, valid-sized chunks before they are ingested by Snowpipe. Update the Snowpipe COPY statement to handle the new chunked data format.

Answer: E

Explanation:
The correct answer is B. Snowpipe has a limitation of 16MB per record. The most reliable solution is to pre-process the oversized records before they reach Snowpipe. Using an AWS Lambda function is a serverless and scalable way to split these records. Option A is incorrect because 'MAX FILE_SIZE pertains to the size of the files, not individual records within those files. Option C is not feasible as S3 doesn't natively split JSON files. Option D is inefficient as it involves manual intervention. Option E defeats the purpose of continuous data loading with Snowpipe. By splitting oversized records before Snowpipe ingests them, you ensure that no data is lost, and Snowpipe can continue to operate as designed.

NEW QUESTION # 21
What is the primary purpose of implementing data processing solutions?

• A. Limit data processing to batch operations
• B. Respond effectively to processing failures
• C. Simplify data access for users
• D. Automate data analysis entirely

Answer: B

Explanation:
Data processing solutions help respond effectively to processing failures for uninterrupted operations.

NEW QUESTION # 22
You are analyzing sales data from different regions stored in a Snowflake table named 'sales_data'. The table includes columns: 'transaction_id' (VARCHAR), 'region' (VARCHAR), 'sale_date' (DATE), and 'sale_amount' (NUMBER). You discover the following data quality issues: The 'region' column contains inconsistent entries such as 'North', 'north', 'NOrth ', and ' South'. The 'sale_amount' column has some values that are stored as strings (e.g., '100.50') instead of numbers, causing errors in aggregation. There are duplicate records identified by the same 'transaction id'. Which set of SQL statements, executed in the given order, provides the MOST effective and efficient way to address these data quality issues in Snowflake?

• A.
• B.
• C.
• D.
• E.

Answer: A

Explanation:
Option E presents the most efficient and effective solution. It combines all three data cleaning steps into a single operation using a CTE. First, standardizes the region name with trim and lowercase. Second, remove duplicate records based on transaction ID. And most important, it correctly handles the 'sale_amount' conversion using TRY_TO_NUMBER inside the CTE to avoid errors and ensures accurate aggregations down stream. This approach minimizes the number of table scans and UPDATE operations, improving performance. Option A fails on how to remove duplicates correctly using TRY_TO_NUMBER to convert the sale amount correctly and data type changes are not possible via ALTER statements if strings are present. Options B, C, and D does not combine all in one single CTE operations and are slower.

NEW QUESTION # 23
You are preparing to load a large dataset from Parquet files stored in an Azure Blob Storage container into Snowflake using Snowsight. The dataset contains personally identifiable information (PII) and you need to ensure that only authorized users can access this data,. You want to use Snowflake's data masking policies to protect the PII. Which of the following options represents the correct sequence of steps and considerations for achieving this, specifically using Snowsight for the loading and initial policy application phases?

• A. 1. Create an Azure external stage pointing to the Blob Storage container. 2. Create a file format object specifying PARQUET as the file type. 3. Load the Parquet files into a staging table with all columns as VARCHAR using Snowsight. 4. Define masking policies on the staging table columns. 5. Create the final table and transfer data from staging, applying the masking policies on the target table as the data moves.
• B. 1. Create an Azure external stage pointing to the Blob Storage container. 2. Load the Parquet files into a staging table with all columns as VARCHAR using Snowsight. 3. Create a file format object specifying PARQUET as the file type. 4. Define masking policies on the staging table columns. 5. Create the final table and transfer data from staging, applying the masking policies on the target table as the data moves.
• C. 1. Create an Azure external stage pointing to the Blob Storage container. 2. Create a file format object specifying PARQUET as the file type. 3. Create the target table with the desired schema, including data types. 4. Define masking policies on the target table columns. 5. Load the Parquet files directly into the target table using Snowsight.
• D. 1. Create an Azure external stage pointing to the Blob Storage container. 2. Create a file format object specifying PARQUET as the file type. 3. Define masking policies on the target table columns. 4. Create the target table with the desired schema, referring to masking policies. 5. Load the Parquet files directly into the target table using Snowsight.
• E. 1. Create an Azure external stage pointing to the Blob Storage container. 2. Create a file format object specifying PARQUET as the file type. 3. Create the target table with the desired schema, including data types. 4. Load the Parquet files directly into the target table using Snowsight. 5. Define masking policies on the target table columns after loading.

Answer: C

Explanation:
Option E is the most efficient and recommended approach. First, you need to set up the connection to Azure Blob Storage (the external stage) and define the file format for Parquet files. Then, you create the target table with the correct schema to receive the data. The critical step is to define the masking policies before loading the data. This ensures that the masking policies are in place when the data is loaded into the target table. Finally, load the data using Snowsight. Applying masking policies after loading the data (as in option B) is less efficient and leaves a window of opportunity where PII is exposed. Defining policies and then referring to them (as in option C) isn't the typical workflow. Option A is more complex and introduces a VARCHAR staging table, which isn't optimal for Parquet data.

NEW QUESTION # 24
What option would allow a Data Analyst to efficiently estimate cardinality on a data set that contains trillions of rows?

• A. Count(Distinct *)/Count(*)
• B. HLL(*)
• C. Count(Distinct *)
• D. SYSTEM$ESTIMATE

Answer: B

Explanation:
When working with "Big Data" at the scale of trillions of rows, calculating an exact count of unique values using COUNT(DISTINCT column) is extremely resource-intensive. This is because Snowflake must keep track of every unique value encountered to ensure no duplicates are counted, leading to high memory usage and long execution times (often referred to as "spilling to disk").
To solve this, Snowflake provides HyperLogLog (HLL) functions. HLL(*) (or specifically HLL_ACCUMULATE and HLL_ESTIMATE) allows an analyst to estimate the cardinality (the number of unique elements) with a very small, known margin of error (typically around 1%). This is significantly faster and uses far fewer credits than an exact count because it uses a probabilistic algorithm rather than a state- heavy tracking mechanism.
Evaluating the Options:
* Option A is technically correct for small datasets but is highly inefficient for trillions of rows, directly contradicting the "efficiently" requirement of the question.
* Option C is a distractor; while Snowflake has various SYSTEM$ functions, SYSTEM$ESTIMATE is not a standard function for cardinality.
* Option D is a formula that doesn't target cardinality but rather a ratio (density).
* Option B is the correct answer. The HLL family of functions is the industry standard within Snowflake for high-performance cardinality estimation on massive datasets.

NEW QUESTION # 25
When performing a diagnostic analysis, which actions aid in collecting related data to identify anomalies? (Select all that apply)

• A. Analyzing data in isolation
• B. Ignoring statistical trends for focused analysis
• C. Collecting data from various sources
• D. Focusing solely on recent data for insights

Answer: C

Explanation:
Collecting data from various sources aids in comprehensive analysis, assisting in identifying anomalies.

NEW QUESTION # 26
What considerations are essential when identifying the volume of data to be collected in a collection system? (Select all that apply)

• A. Speed of data retrieval
• B. Frequency of data analysis
• C. Data redundancy requirements
• D. Available storage capacity

Answer: B,D

Explanation:
Identifying the volume of data involves considering available storage capacity and the frequency of data analysis.

NEW QUESTION # 27
When maintaining reports and dashboards, why is it essential to build automated and repeatable tasks?

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

Answer: D

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

NEW QUESTION # 28
You are designing a system to ingest data from a high-volume sensor network. The sensors send data in a custom binary format to an on-premise message queue (e.g., RabbitMQ). The data needs to be converted to a structured format (e.g., JSON) before being loaded into Snowflake. Choose the most effective approach to ensure data integrity, scalability, and near-real-time ingestion.

• A. Create a Snowflake external function that connects to the message queue and converts the binary data to JSON during the COPY INTO process.
• B. Develop a custom application that subscribes to the message queue, converts the binary data to JSON, and then uses the Snowflake JDBC driver to insert the data directly into Snowflake.
• C. Use an on-premise gateway to expose the RabbitMQ as a REST API, then use a Snowflake external function to call the exposed API.
• D. Use a third-party data integration platform that supports connecting to message queues, converting binary data, and loading data into Snowflake.
• E. Deploy a stream processing engine (e.g., Apache Kafka Streams, Apache Flink) on-premise to consume messages from the queue, convert the binary data to JSON, and then write the JSON data to a cloud storage location (e.g., S3, Azure Blob Storage, GCS). Configure Snowpipe to load the JSON data into Snowflake.

Answer: D,E

Explanation:
Options B and D provide robust and scalable solutions. Option B leverages a dedicated data integration platform, which often provides pre-built connectors for message queues, binary data conversion capabilities, and optimized Snowflake integration. Option D utilizes a stream processing engine, offering the scalability and fault tolerance necessary for high-volume data streams. The stream processing engine can perform the binary-to-JSON conversion and write the structured data to cloud storage for Snowpipe to ingest. Option A lacks scalability and fault tolerance. Option C might be limited by the external function execution time and concurrency. Option E creates an unnecesssary intermediate REST API which can affect performance and also does not inherently solve the binary conversion problem.

NEW QUESTION # 29
You're developing a data quality process in Snowflake that relies on identifying duplicate records within a large table named 'TRANSACTIONS. You need to generate a hash value for each row based on several key columns ('transaction_id', 'customer_id' , amount', to efficiently compare rows and detect duplicates. However, some of these columns may contain NULL values, which you want to handle consistently during the hash generation. Which of the following approaches, utilizing Snowflake's system functions, will MOST reliably generate a consistent hash value for duplicate rows, even when some of the key columns contain NULLs? (Select TWO)

• A. Use the 'SHA2(CONCAT(NVL(transaction_id, 'I), NVL(customer_id, "), NVL(amount, "), NVL(transaction_date, "))) function, replacing NULLs with empty strings using the NVL function before concatenation.
• B. Use the WS('I', transaction_id, customer_id, amount, function. This concatenates strings with a separator, handling NULLs implicitly by skipping them in concatenation, leading to inconsistencies.
• C. Use the customer_id, amount, transaction_date)' function directly, as Snowflake automatically handles NULLs in hashing functions.
• D. Use the II customer_id II amount II transaction_date)' function. Snowflake implicitly converts NULL to a default value during string concatenation.
• E. Use the AS VARCHAR), AS VARCHAR), AS VARCHAR), NVL(CAST(transaction_date AS VARCHAR), function, explicitly converting each value to a string and replacing NULLs with empty strings using NVL.

Answer: A,E

Explanation:
Options B and E are the most reliable. Option B concatenates the value of the columns as a string to create a seed for SHA2, ensuring to convert the NULL to empty string, which is necessary so that SHA2 does not return NULL in the face of NULL column values. Option E also uses SHA2 to encrypt after concatenating all the column values but it casts all those columns to varchar, which is necessary for the data preparation and data ingestion as they might be of different datatype. The first option is wrong because Snowflake's HASH function automatically returns NULL if any of the input are NULL. Option C uses the 'II' operator to concatenate values and Snowflake will return NULL in case any value is null. Option D concatenates strings with a separator, handling NULLs implicitly by skipping them in concatenation, leading to inconsistencies

NEW QUESTION # 30
What steps are typically involved in troubleshooting query performance issues in Snowflake?
(Select all that apply)

• A. Reviewing query history and usage logs
• B. Modifying warehouse configurations
• C. Examining Query Profile attributes
• D. Analyzing system hardware for faults

Answer: A,B,C

Explanation:
Troubleshooting query performance often involves reviewing query history, adjusting warehouse configurations, and examining Query Profile attributes for optimization.

NEW QUESTION # 31
How do stored procedures contribute to data analysis efficiency in SQL compared to UDFs?

• A. Stored procedures allow limited data accessibility for improved security.
• B. UDFs enhance query performance more effectively than stored procedures.
• C. They enable the execution of repetitive tasks, enhancing efficiency.
• D. Stored procedures hinder customization in data operations.

Answer: C

Explanation:
Stored procedures enhance efficiency by enabling the execution of repetitive tasks.

NEW QUESTION # 32
What distinguishes Materialized views from Secure views in the context of data analysis?

• A. Secure views provide a precomputed snapshot of data, unlike Materialized views.
• B. Materialized views restrict data access for security purposes, unlike Secure views.
• C. Materialized views enhance data security, while Secure views offer improved query performance.
• D. Secure views provide enhanced data security without precomputing data.

Answer: D

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

NEW QUESTION # 33
You have a Snowflake table 'order details' with columns 'order id', 'customer id', 'order date', and 'order amount'. You need to calculate the 3-month moving average of 'order_amount' for each customer, but only for those customers who have placed at least 5 orders. Which of the following SQL statements will correctly achieve this? (Assume the current date is '2024-01-01 ')

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

Answer: B

Explanation:
Option E is the correct and most clear solution. It calculates the 3-month moving average, filters customers who have placed at least 5 orders, and leverages the power and clarity of Snowflake syntax. The QUALIFY clause effectively filters for customers with at least 5 orders. The 'RANGE BETWEEN INTERVAL '3 MONTH' PRECEDING AND CURRENT ROW accurately calculates the moving average over a 3- month window based on A, B and C calculate a simple moving average of the last 3 rows regardless of date, while D is syntactically invalid as HAVING cannot be used with window function in this way.

NEW QUESTION # 34
When planning for data volume collection, what is an important consideration to ensure scalability and performance?

• A. The physical location of data
• B. The data processing capabilities
• C. The expected growth rate of data
• D. The types of data visualization tools used

Answer: C

NEW QUESTION # 35
You are working with a Snowflake table 'raw_data' containing a column of type TEXT that stores log messages in various formats, including JSON and CSV. You need to extract specific data points from these log entries, treating JSON entries differently from CSV entries. Specifically: For JSON log entries (identified by starting with '{l), extract the value of the 'user_id' key. For CSV log entries (identified by starting with a number), extract the second field (assuming comma-separated values). Which of the following queries is the most efficient and correct way to achieve this data extraction?

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

Answer: D

Explanation:
Option B provides the most efficient and correct solution. 1, 1) = '{' ' efficiently checks if the log entry starts with '{' without needing the full power of STARTS_WITH or PARSE_JSON. For JSON entries, it correctly uses to extract the 'user_id' directly from the VARIANT representation. For CSV entries, it uses ',', 2)' which is specifically designed to extract a single part from a delimited string, and is more efficient than using SPLIT. Option A will not treat the JSON objects in the 'log_entry' column as valid variants and needs to be parsed using PARSE_JSON explicitly to get correct answer. Option C uses GET _ PATH which cannot be used without parsing the JSON first. Option D will error out because you cannot use JSON dot notation on a TEXT column directly. Option E uses SPLIT TO TABLE which is more costly than SPLIT PART.

NEW QUESTION # 36
How do materialized views differ from regular views in terms of data storage and computation?

• A. Materialized views restrict data storage for better computation.
• B. Materialized views simplify complex data structures for better computation.
• C. Regular views provide precomputed snapshots, unlike materialized views.
• D. Regular views provide precomputed snapshots for improved query performance.

Answer: C

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

NEW QUESTION # 37
A scorecard tile on a Snowsight dashboard shows a comparison between the industry average employee age (which is 34) and a company's average employee age (which is 38). The scorecard tile looks like this:
Comparison with industry average

How should this tile be interpreted?

• A. 38 is the comparison and 12% is the average age change in the last year.
• B. 38 is the value and 12% is the percent difference from the comparison.
• C. 38 is the value and 12% is the standard deviation.
• D. 38 is the comparison and 12% is the projected industry growth.

Answer: B

Explanation:
In Snowsight, the Scorecard chart type is specifically designed to highlight a single key metric (the "Value") and optionally compare it against a static or dynamic benchmark (the "Comparison"). This visualization is a core component of the Data Presentation and Data Visualization domain, as it provides an immediate "at-a- glance" status for high-level KPIs.
1. Interpreting the Scorecard Components:
* Primary Value: The large, bold number (38) represents the actual value calculated by the underlying query. In this scenario, it is the company's average employee age.
* Secondary Metric (The Percentage): When a scorecard is configured with a comparison value, Snowsight automatically calculates the percentage difference between the primary value and that comparison.
* Visual Indicators: The green upward arrow indicates that the primary value is higher than the comparison value. If the value were lower, the arrow would point downward and typically appear in red.
2. The Mathematical Calculation:
The 12% shown in the exhibit is the result of the percentage change formula:

Evaluating the Options:
* Options A and D are incorrect because they misidentify the large number (38) as the "comparison." In Snowflake's UI, the large number is always the primary metric being tracked, not the target it is being measured against.
* Option B is incorrect because the percentage in a scorecard represents relative difference, not a statistical measure like standard deviation.
* Option C is the 100% correct interpretation. It correctly identifies 38 as the current company value and 12% as the calculated difference from the industry benchmark of 34. This level of visual literacy is expected of a SnowPro Advanced: Data Analyst to ensure dashboard insights are communicated accurately to stakeholders.

NEW QUESTION # 38
A ride-sharing company wants to analyze the density of ride requests in different city areas. They have a table 'RIDES' with a 'LOCATION' (GEOGRAPHY) column representing the pickup location of each ride. They want to divide the city into a grid of hexagonal cells and count the number of rides originating in each cell. Which sequence of steps would achieve this, assuming the 'city_boundary' is defined and accessible as a GEOGRAPHY object?

• A. 1. Generate a grid of hexagonal GEOGRAPHY objects covering the 'city_boundary' using 'ST_SPHERE_GRID. 2. Use 'ST_CONTAINS' to determine which rides fall within each hexagonal cell. 3. Aggregate the ride counts for each cell.
• B. 1. Generate a grid of rectangular GEOGRAPHY objects covering the 'city_boundary' using 'ST GRID. 2. Use 'ST CONTAINS' to determine which rides fall within each rectangular cell. 3. Aggregate the ride counts for each cell.
• C. 1. Generate a grid of hexagonal GEOGRAPHY objects covering the 'city_boundary' using 'ST HEXGRI 2. Use 'ST_WITHIN' to determine which hexagonal cell each ride falls within. 3. Aggregate the ride counts for each cell.
• D. 1. Generate a grid of hexagonal GEOGRAPHY objects covering the 'city_boundary' using 'ST_HEXGRID. 2. Use 'ST CONTAINS to determine which hexagonal cell contains each ride. 3. Aggregate the ride counts for each cell.
• E. 1. Generate a grid of hexagonal GEOGRAPHY objects covering the 'city_boundary' using 'ST_HEXGRID. 2. Use 'ST_INTERSECTS' to determine which rides intersect with each hexagonal cell. 3. Aggregate the ride counts for each cell.

Answer: D

Explanation:
'ST HEXGRID is the correct function for generating a hexagonal grid. is used to check if a hexagonal cell contains a ride's location. Aggregating the ride counts for each cell provides the density information.

NEW QUESTION # 39
Consider the following chart.

What can be said about the correlation for sales over time between the two categories?

• A. There is a negative correlation.
• B. There is a positive correlation.
• C. There is no correlation. (Selected)
• D. There is a non-linear correlation.

Answer: C

Explanation:
In Data Analysis, correlation refers to a statistical relationship between two variables. When analyzing a time- series chart like the one provided, a Data Analyst looks for patterns in how the two categories-"Enterprise" (blue line) and "Pro Edition" (yellow line)-move in relation to one another over the X-axis (Year).
A Positive Correlation would be indicated if both lines generally moved in the same direction at the same time (e.g., when Enterprise sales increase, Pro Edition sales also increase). A Negative Correlation (or inverse correlation) would be shown if the lines moved in opposite directions consistently (e.g., when one peaks, the other troughs).
Looking closely at the provided exhibit, the fluctuations for both editions are highly erratic and appear independent of each other. For instance, around the year 2008, the Pro Edition (yellow) shows a significant peak while the Enterprise edition (blue) experiences a sharp decline. Conversely, in other sections of the chart, they both dip or rise simultaneously by chance, but there is no sustained, predictable pattern of movement. The peaks and valleys do not align in a way that suggests one variable's movement is tied to the other.
Statistically, this lack of a discernible relationship indicates a Correlation Coefficient near zero. In the context of the Snowflake Snowpro Advanced: Data Analyst exam, identifying "No Correlation" is a key skill for interpreting Snowsight visualizations. It tells the analyst that the factors driving sales for the Enterprise tier are likely distinct from those driving the Pro Edition, and they should be analyzed as independent segments rather than interdependent variables. Therefore, based on the visual evidence of random, non- synchronous movement across the timeline, the only supported conclusion is that there is no correlation.

NEW QUESTION # 40
You are investigating why a Snowflake data replication process between two regions is experiencing significant lag. You need to collect data to determine if the issue stems from network latency, insufficient warehouse resources in the target region, or data transformation bottlenecks. Select the data collection methods that will provide the MOST relevant insights.

• A. Monitor the replication lag metrics (e.g., DATABASE REPLICATION_LAG, TABLE REPLICATION_LAG) exposed through Snowflake system functions and the web interface for both the source and target regions.
• B. Run traceroute commands between the source and target regions to measure network latency.
• C. Monitor the CPU utilization of the virtual machines running the Snowflake service in both regions.
• D. Restart the data replication process.
• E. Analyze the query history in the target region to identify slow-running transformation queries that might be bottlenecking the replication process.

Answer: A,B,E

Explanation:
Options A, B, and C provide specific data points relevant to the identified potential causes. Monitoring replication lag metrics (A) directly quantifies the lag. Traceroute (B) measures network latency. Analyzing query history (C) identifies transformation bottlenecks. Restarting the process (D) might temporarily resolve the issue but doesn't address the root cause. Snowflake manages the underlying infrastructure; therefore, monitoring VM CPU utilization (E) is not something that a data analyst has access to or is needed for the diagnostic in this case. The Snowflake service runs and manages the queries.

NEW QUESTION # 41
How can automated and repeatable tasks contribute to maintaining reports and dashboards in meeting business requirements?

• A. Repeatable tasks hinder data updates in dashboards.
• B. They solely increase the complexity of dashboard management.
• C. They limit the scalability of dashboards and reports.
• D. Automated tasks ensure consistency and reduce manual effort.

Answer: D

Explanation:
Automated tasks ensure consistency and reduce manual effort in maintaining reports and dashboards.

NEW QUESTION # 42
You are building a sales performance dashboard in Snowflake for a retail company. The data includes sales transactions, product information, and customer demographics. You need to enable users to drill down from regional sales summaries to individual store sales and then to customer-level details within the dashboard. Which of the following Snowflake features and dashboard design principles are CRUCIAL for achieving this interactive drill-down capability with optimal performance?

• A. Using parameterized views in Snowflake and configuring the dashboard to pass parameters dynamically based on user selections. Ensuring proper clustering keys are defined on relevant tables.
• B. Creating a stored procedure in Snowflake that dynamically generates SQL queries based on user interactions within the dashboard.
• C. Creating multiple dashboards, one for each level of granularity (region, store, customer), and linking them together with navigation buttons.
• D. Relying solely on the dashboard's built-in filtering capabilities and avoiding any pre-aggregation or optimization in Snowflake.
• E. Exporting the data to an external BI tool and leveraging its drill-down features. Data can be exported to the external tool daily.

Answer: A

Explanation:
Parameterized views allow you to create flexible queries that adapt to user selections. Clustering keys ensure efficient filtering and data retrieval for drill-down operations. Creating multiple dashboards (B) is less efficient and user-friendly. Relying solely on dashboard filtering (C) can lead to performance issues. Exporting data to an external BI tool (D) introduces latency. Dynamic SQL generation (E) can be complex and prone to errors.

NEW QUESTION # 43
......

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