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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q42-Q47):

NEW QUESTION # 42
You are analyzing customer order data in Snowflake. A column 'ORDER DATE' is stored as VARCHAR. You notice inconsistencies: some dates are in 'YYYY-MM-DD' format, others in 'MM/DD/YYYY', and some have missing values represented by 'N/A'. You need to standardize the 'ORDER DATE' column into a DATE format and handle missing values. What is the most efficient and robust Snowflake SQL statement to achieve this, ensuring no data is lost and that invalid dates are replaced with NULL?

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

Answer: B

Explanation:
Option B is the most robust and efficient because it uses REGEXP LIKE to validate the date formats before attempting to convert them using TRY_TO_DATE. It also directly creates a temporary table to perform the transformation, minimizing the risk of data loss during the process. Then the old table is replaced with the content of the temp table.

NEW QUESTION # 43
A data analyst needs to ingest data from various sources into Snowflake, cleanse it, and load it into target tables. Which of the following actions are MOST crucial for ensuring data quality and consistency during the ingestion and preparation phases?

A. Enforcing data type constraints on Snowflake table columns.
B. Implementing data validation checks using Snowflake user-defined functions (UDFs) or stored procedures to detect and handle invalid data.
C. Implementing data profiling techniques to identify data quality issues and inconsistencies before loading data into target tables and creating detailed data dictionaries.
D. Storing all data in a single large table to avoid data silos.
E. Using Snowflake's data masking policies to protect sensitive data during ingestion.

Answer: A,B,C

Explanation:
Options A, B, and E are most crucial for data quality and consistency. Enforcing data type constraints (A) ensures that only data of the correct type is loaded into the tables. Implementing data validation checks (B) using UDFs or stored procedures allows for detecting and handling invalid data, preventing it from corrupting the data warehouse. Data profiling (E) helps to understand data quality issues before the load and guides data cleansing efforts. Storing all data in a single table (C) is generally not recommended as it can lead to performance issues and make it harder to manage data. While data masking (D) is important for data security, it is not directly related to data quality and consistency during ingestion and preparation. Data dictionaries promote data quality and usability.

NEW QUESTION # 44
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. Restart the data replication process.
B. Analyze the query history in the target region to identify slow-running transformation queries that might be bottlenecking the replication process.
C. 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.
D. Monitor the CPU utilization of the virtual machines running the Snowflake service in both regions.
E. Run traceroute commands between the source and target regions to measure network latency.

Answer: B,C,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 # 45
You are tasked with ingesting data from a REST API that provides daily sales reports in JSON format. The API has rate limits (100 requests per minute) and returns a large dataset (approximately 5GB per day). The data needs to be processed within 2 hours of its availability. You want to leverage Snowflake external functions and tasks. Which approach balances efficiency, cost, and adherence to rate limits?

A. Call API directly from a scheduled task without considering Rate Limit. Persist the data using COPY INTO command.
B. Create a Snowflake external function that directly connects to the API and loads data into a staging table. Use Snowpipe with auto-ingest to continuously load the data as it arrives. Ignore the API Rate limits; assume they will be handled by API itself.
C. Create a single Snowflake task that calls an external function which iterates through all API pages sequentially in a single execution to retrieve and load all the data. Rely on Snowflake's automatic scaling to handle the load.
D. Create a Snowflake task that calls an external function. This external function calls an intermediate service (e.g., AWS Lambda, Azure Function) which is responsible for fetching the data in batches, respecting the API rate limits, and storing the data in cloud storage. Snowpipe then loads the data from cloud storage into Snowflake.
E. Create a Snowflake task that triggers an external function which retrieves only the metadata (e.g., total records, page count) from the API. Then, create a dynamic number of child tasks, each responsible for retrieving a subset of the data, using the metadata to respect the API rate limits.

Answer: D

Explanation:
Option D is the most balanced approach. It leverages an intermediate service to handle the complexities of API interaction (rate limits, pagination), decouples the data retrieval from Snowflake compute, and uses Snowpipe for efficient bulk loading. This approach addresses I both the rate limits and processing time requirements effectively.

NEW QUESTION # 46
Your team is building a data pipeline to ingest data from a REST API that returns JSON payloads. Due to API rate limits, you need to implement a backoff strategy to avoid overwhelming the API. You are using Python and the 'requests' library for data ingestion. Which code snippet BEST demonstrates a robust backoff strategy with exponential backoff and jitter?

Answer: A

Explanation:
Option B provides the most robust backoff strategy. It implements exponential backoff (wait time increases exponentially with each retry) and adds jitter (a random amount of time) to the wait time to avoid synchronized retries from multiple clients. also handles error codes correctly. Option A doesn't have a backoff strategy. Option C has a simple retry mechanism but lacks exponential backoff and a limit on retries. Option D lacks error handling and retry logic. Option E uses recursion, which is generally not recommended and does not feature exponential backoff.

NEW QUESTION # 47
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