Free PDF Quiz Snowflake - DEA-C01 - Updated SnowPro Advanced: Data Engineer Certification Exam Relevant Answers

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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Data Transformation: The SnowPro Advanced: Data Engineer exam evaluates skills in using User-Defined Functions (UDFs), external functions, and stored procedures. It assesses the ability to handle semi-structured data and utilize Snowpark for transformations. This section ensures Snowflake engineers can effectively transform data within Snowflake environments, critical for data manipulation tasks.
Topic 2	Security: The Security topic of the DEA-C01 test covers the principles of Snowflake security, including the management of system roles and data governance. It measures the ability to secure data and ensure compliance with policies, crucial for maintaining secure data environments for Snowflake Data Engineers and Software Engineers.
Topic 3	Storage and Data Protection: The topic tests the implementation of data recovery features and the understanding of Snowflake's Time Travel and micro-partitions. Engineers are evaluated on their ability to create new environments through cloning and ensure data protection, highlighting essential skills for maintaining Snowflake data integrity and accessibility.
Topic 4	Data Movement: Snowflake Data Engineers and Software Engineers are assessed on their proficiency to load, ingest, and troubleshoot data in Snowflake. It evaluates skills in building continuous data pipelines, configuring connectors, and designing data sharing solutions.
Topic 5	Performance Optimization: This topic assesses the ability to optimize and troubleshoot underperforming queries in Snowflake. Candidates must demonstrate knowledge in configuring optimal solutions, utilizing caching, and monitoring data pipelines. It focuses on ensuring engineers can enhance performance based on specific scenarios, crucial for Snowflake Data Engineers and Software Engineers.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
What is a characteristic of the operations of streams in Snowflake?

A. Each committed and uncommitted transaction on the source table automatically puts a change record in the stream.
B. Whenever a stream is queried, the offset is automatically advanced.
C. When a stream is used to update a target table the offset is advanced to the current time.
D. Querying a stream returns all change records and table rows from the current offset to the current time.

Answer: D

Explanation:
Explanation
A stream is a Snowflake object that records the history of changes made to a table. A stream has an offset, which is a point in time that marks the beginning of the change records to be returned by the stream. Querying a stream returns all change records and table rows from the current offset to the current time. The offset is not automatically advanced by querying the stream, but it can be manually advanced by using the ALTER STREAM command. When a stream is used to update a target table, the offset is advanced to the current time only if the ON UPDATE clause is specified in the stream definition. Each committed transaction on the source table automatically puts a change record in the stream, but uncommitted transactions do not.

NEW QUESTION # 41
A Data Engineer is investigating a query that is taking a long time to return The Query Profile shows the following:

What step should the Engineer take to increase the query performance?

A. increasethe size of the virtual warehouse.
B. Rewrite the query using Common Table Expressions (CTEs)
C. Add additional virtual warehouses.
D. Change the order of the joins and start with smaller tables first

Answer: A

Explanation:
Explanation
The step that the Engineer should take to increase the query performance is to increase the size of the virtual warehouse. The Query Profile shows that most of the time was spent on local disk IO, which indicates that the query was reading a lot of data from disk rather than from cache. This could be due to a large amount of data being scanned or a low cache hit ratio. Increasing the size of the virtual warehouse will increase the amount of memory and cache available for the query, which could reduce the disk IO time and improve the query performance. The other options are not likely to increase the query performance significantly. Option A, adding additional virtual warehouses, will not help unless they are used in a multi-cluster warehouse configuration or for concurrent queries. Option C, rewriting the query using Common Table Expressions (CTEs), will not affect the amount of data scanned or cached by the query. Option D, changing the order of the joins and starting with smaller tables first, will not reduce the disk IO time unless it also reduces the amount of data scanned or cached by the query.

NEW QUESTION # 42
A Data Engineer is evaluating the performance of a query in a development environment.

Based on the Query Profile what are some performance tuning options the Engineer can use? (Select TWO)

A. Use a multi-cluster virtual warehouse with the scaling policy set to standard
B. Move the query to a larger virtual warehouse
C. Increase the max cluster count
D. Add a LIMIT to the ORDER BY If possible
E. Create indexes to ensure sorted access to data

Answer: B,D

Explanation:
Explanation
The performance tuning options that the Engineer can use based on the Query Profile are:
Add a LIMIT to the ORDER BY If possible: This option will improve performance by reducing the amount of data that needs to be sorted and returned by the query. The ORDER BY clause requires sorting all rows in the input before returning them, which can be expensive and time-consuming. By adding a LIMIT clause, the query can return only a subset of rows that satisfy the order criteria, which can reduce sorting time and network transfer time.
Create indexes to ensure sorted access to data: This option will improve performance by reducing the amount of data that needs to be scanned and filtered by the query. The query contains several predicates on different columns, such as o_orderdate, o_orderpriority, l_shipmode, etc. By creating indexes on these columns, the query can leverage sorted access to data and prune unnecessary micro-partitions or rows that do not match the predicates. This can reduce IO time and processing time.
The other options are not optimal because:
Use a multi-cluster virtual warehouse with the scaling policy set to standard: This option will not improve performance, as the query is already using a multi-cluster virtual warehouse with the scaling policy set to standard. The Query Profile shows that the query is using a 2XL warehouse with 4 clusters and a standard scaling policy, which means that the warehouse can automatically scale up or down based on the load. Changing the warehouse size or the number of clusters will not affect the performance of this query, as it is already using the optimal resources.
Increase the max cluster count: This option will not improve performance, as the query is not limited by the max cluster count. The max cluster count is a parameter that specifies the maximum number of clusters that a multi-cluster virtual warehouse can scale up to. The Query Profile shows that the query is using a 2XL warehouse with 4 clusters and a standard scaling policy, which means that the warehouse can automatically scale up or down based on theload. The default max cluster count for a 2XL warehouse is 10, which means that the warehouse can scale up to 10 clusters if needed. However, the query does not need more than 4 clusters, as it is not CPU-bound or memory-bound. Increasing the max cluster count will not affect the performance of this query, as it will not use more clusters than necessary.

NEW QUESTION # 43
A data engineer must use AWS services to ingest a dataset into an Amazon S3 data lake. The data engineer profiles the dataset and discovers that the dataset contains personally identifiable information (PII). The data engineer must implement a solution to profile the dataset and obfuscate the PII.
Which solution will meet this requirement with the LEAST operational effort?

A. Use the Detect PII transform in AWS Glue Studio to identify the PII. Obfuscate the PII. Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.
B. Ingest the dataset into Amazon DynamoDB. Create an AWS Lambda function to identify and obfuscate the PII in the DynamoDB table and to transform the data. Use the same Lambda function to ingest the data into the S3 data lake.
C. Use the Detect PII transform in AWS Glue Studio to identify the PII. Create a rule in AWS Glue Data Quality to obfuscate the PII. Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.
D. Use an Amazon Kinesis Data Firehose delivery stream to process the dataset. Create an AWS Lambda transform function to identify the PII. Use an AWS SDK to obfuscate the PII. Set the S3 data lake as the target for the delivery stream.

Answer: C

NEW QUESTION # 44
A company uses Amazon EMR as an extract, transform, and load (ETL) pipeline to transform data that comes from multiple sources. A data engineer must orchestrate the pipeline to maximize performance.
Which AWS service will meet this requirement MOST cost effectively?

A. AWS Step Functions
B. Amazon EventBridge
C. AWS Glue Workflows
D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

Answer: A

Explanation:
Glue Workflows is for Glue job orchestration. C is for orchestration with different AWS services.

NEW QUESTION # 45
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FREE PDF QUIZ SNOWFLAKE - DEA-C01 - UPDATED SNOWPRO ADVANCED: DATA ENGINEER CERTIFICATION EXAM RELEVANT ANSWERS

Free PDF Quiz Snowflake - DEA-C01 - Updated SnowPro Advanced: Data Engineer Certification Exam Relevant Answers