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Reliable DAA-C01 Test Labs - Valid DAA-C01 Study Plan

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Reliable DAA-C01 Test Labs - Valid DAA-C01 Study Plan

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q180-Q185):NEW QUESTION # 180
An e-commerce company suspects that website performance issues (slow loading times) are negatively impacting conversion rates, particularly for mobile users in specific geographic regions. You have access to the following Snowflake tables: 'WEB SESSIONS' 'session_id', , 'device_type', 'location' , , 'session_start_time' 'TRANSACTIONS': 'transaction_id' , 'session_id', Which SQL query is the MOST appropriate for quantifying the relationship between page load time and conversion rate, specifically for mobile users in the 'California' location?

  • A. Option C
  • B. Option A
  • C. Option D
  • D. Option B
  • E. Option E
Answer: A
Explanation:
Option C is the most appropriate. It groups page load times into bins (using 'NTILE), allowing you to see how conversion rate changes across different ranges of page load times. This is much more informative than a single average (Option A). Option B provides the conversion rate for each distinct , which is likely to be very granular and difficult to interpret. Option D calculates the correlation coefficient, which is useful but doesn't provide the detailed view of conversion rate across different page load time ranges offered by option C. Option E just groups the time based on User ld, which is useless.

NEW QUESTION # 181
You are tasked with ingesting data from a variety of external sources (JSON, CSV, Parquet) into Snowflake to build a unified customer profile. The sources contain inconsistencies in data types, null handling, and naming conventions. Your goal is to create a robust ingestion pipeline that identifies data quality issues and prepares the data for analysis. Which combination of Snowflake features and approaches would be most effective for achieving this?
  • A. Employ a third-party ETL tool integrated with Snowflake, configure data cleansing and transformation rules within the ETL tool, and load the transformed data into Snowflake using its optimized connectors.
  • B. Build a custom data ingestion application using the Snowflake Connector for Python, implementing complex data transformations and quality checks within the application before loading the data into Snowflake. Store raw data as is for future use.
  • C. Utilize Dynamic Tables with appropriate transformation logic defined to handle inconsistencies and maintain data quality during ingestion. Leverage Snowflake's data governance features in conjunction with Dynamic Tables for comprehensive data management
  • D. Leverage Snowflake's data governance features with data profiling, implement data quality rules in a data catalog, and use a combination of Snowpipe and COPY INTO with data transformations within the COPY INTO statement.
  • E. Use Snowpipe with schema evolution enabled and rely solely on Snowflake's automatic data type conversion. Implement data quality checks in downstream views.
Answer: A,B,C,D
Explanation:
Options B, C, D, and E represent robust approaches. B combines data governance and built-in transformations. C allows for custom logic using the Python connector. D uses a specialized ETL tool. E leverages Dynamic Tables for automated transformations and data quality maintenance. A is insufficient as it relies too heavily on automatic conversion and delays quality checks. The best approach would involve data profiling to understand the data, implementing data quality rules at the point of ingestion or through dynamic tables, and leveraging a transformation approach suited to the complexity of the transformations.

NEW QUESTION # 182
When handling CSV, JSON, and Parquet data types for consumption, what advantages do Parquet files typically offer over the others?
  • A. Parquet files provide better compression and query performance
  • B. JSON files offer more flexibility in schema changes
  • C. CSV files are more efficient in handling nested data structures
  • D. Parquet files are not suitable for large datasets
Answer: A
Explanation:
Parquet files often provide better compression and query performance compared to CSV and JSON due to their columnar storage format, enhancing efficiency in handling large datasets.

NEW QUESTION # 183
You are using Snowpipe to continuously load data from an external stage (AWS S3) into a Snowflake table named 'RAW DATA. You notice that the pipe is frequently encountering errors due to invalid data formats in the incoming files. You need to implement a robust error handling mechanism that captures the problematic records for further analysis without halting the pipe's operation. Which of the following approaches is the MOST effective and Snowflake-recommended method to achieve this?
  • A. Utilize Snowpipe's 'VALIDATION_MODE' parameter set to to identify and handle invalid records. This requires modification of the COPY INTO statement to redirect errors to an error table.
  • B. Implement a custom error logging table and modify the Snowpipe's COPY INTO statement to insert error records into this table using a stored procedure called upon failure.
  • C. Configure Snowpipe's 'ON_ERROR parameter to 'CONTINUE' and rely on the 'SYSTEM$PIPE_STATUS' function to identify files with errors. Then, manually query those files for problematic records.
  • D. Implement Snowpipe's 'ERROR _ INTEGRATION' object, configuring it to automatically log error records to a designated stage location in JSON format for later analysis. This requires updating the pipe definition.
  • E. Disable the Snowpipe and manually load data using a COPY INTO statement with the 'ON_ERROR = 'SKIP_FILE" option, then manually inspect the skipped files.
Answer: D
Explanation:
Snowflake's 'ERROR INTEGRATION' feature, when configured with a pipe, automatically logs details of records that fail during ingestion to a specified stage. This provides a structured and readily accessible log of errors without interrupting the data loading process. Option A is not a native feature. Option B, while potentially usable, doesn't directly integrate with pipes as the PRIMARY mechanism. Option C involves more manual intervention and doesn't offer structured error logging. Option E defeats the purpose of automated loading via Snowpipe.

NEW QUESTION # 184
You are tasked with identifying potential data sources for a new marketing analytics dashboard. The dashboard needs to provide insights into customer behavior across various touchpoints. Which of the following would be the MOST appropriate data sources to consider?
  • A. Salesforce data containing customer interactions and sales opportunities.
  • B. Website clickstream data stored in AWS S3 buckets in Parquet format.
  • C. IoT sensor data containing temperature readings.
  • D. Database containing HR employee data.
  • E. Social media activity data ingested via a third-party API and stored in a relational database.
Answer: A,B,E
Explanation:
Options A, B, and C are the most relevant data sources for a marketing analytics dashboard focused on customer behavior. Website clickstream data (A) provides insights into user interactions on the website. Social media activity data (B) offers insights into customer sentiment and engagement. Salesforce data (C) provides information about customer interactions and sales opportunities. HR employee data (D) and IoT sensor data (E) are less relevant to customer behavior and marketing analytics.

NEW QUESTION # 185
......
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