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Snowflake DEA-C01 Exam | Practice DEA-C01 Exam - Authoritative Website in Offering Valid DEA-C01 Mock Exam

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q96-Q101):

NEW QUESTION # 96
Mark the correct statements about Cache?

  • A. Materialized views are faster than tables because of their "cache" (i.e. the query results for the view); in addition, if data has changed, they can use their "cache" for data that hasn't changed and use the base table for any data that has changed.
  • B. Materialized views are more flexible than, but typically slower than, cached results.
  • C. Warehouse cache is dropped when the warehouse is suspended, which may result in slower initial performance for some queries after the warehouse is resumed.
  • D. The size of the warehouse cache is determined by the compute resources in the ware-house.
  • E. For persisted query results of all sizes, the cache expires after 24 hours.

Answer: A,B,C,D,E

Explanation:
Explanation
How Does Warehouse Caching Impact Queries?
Each warehouse, when running, maintains a cache of table data accessed as queries are processed by the warehouse. This enables improved performance for subsequent queries if they are able to read from the cache instead of from the table(s) in the query. The size of the cache is determined by the compute resources in the warehouse (i.e. the larger the warehouse and, therefore, more compute re-sources in the warehouse), the larger the cache.
This cache is dropped when the warehouse is suspended, which may result in slower initial perfor-mance for some queries after the warehouse is resumed. As the resumed warehouse runs and pro-cesses more queries, the cache is rebuilt, and queries that are able to take advantage of the cache will experience improved performance.
Keep this in mind when deciding whether to suspend a warehouse or leave it running. In other words, consider the trade-off between saving credits by suspending a warehouse versus maintaining the cache of data from previous queries to help with performance.
Using Persisted Query Results
When a query is executed, the result is persisted (i.e. cached) for a period of time. At the end of the time period, the result is purged from the system.
Snowflake uses persisted query results to avoid re-generating results when nothing has changed (i.e. "retrieval optimization"). In addition, you can use persisted query results to post-process the results (e.g. layering a new query on top of the results already calculated).
For persisted query results of all sizes, the cache expires after 24 hours.
Both materialized views and cached query results provide query performance benefits:
Materialized views are more flexible than, but typically slower than, cached results.
Materialized views are faster than tables because of their "cache" (i.e. the query results for the view); in addition, if data has changed, they can use their "cache" for data that hasn't changed and use the base table for any data that has changed.
Regular views do not cache data, and therefore cannot improve performance by caching.


NEW QUESTION # 97
If using a JavaScript UDF in a masking policy, Data Engineer needs to ensure the data type of the column, UDF, and masking policy match irrespective of case-sensitivity?

  • A. TRUE
  • B. FALSE

Answer: B

Explanation:
Explanation
Please note JavaScript is case sensitive but if we are using a JavaScript UDF in a masking policy, ensure the data type of the column, UDF, and masking policy match.


NEW QUESTION # 98
A Data Engineer is writing a Python script using the Snowflake Connector for Python. The Engineer will use the snowflake. Connector.connect function to connect to Snowflake The requirementsare:
*Raise an exception if the specified database schema or warehouse does not exist
*improve download performance
Whichparameters of the connect function should be used? (Select TWO).

  • A. client_session_keep_alivs
  • B. client_prefetch_threads
  • C. arrow_nunber_to_decimal
  • D. validate_default_parameters
  • E. authenticator

Answer: B,D

Explanation:
Explanation
The parameters of the connect function that should be used are client_prefetch_threads and validate_default_parameters. The client_prefetch_threads parameter controls the number of threads used to download query results from Snowflake. Increasing this parameter can improve download performance by parallelizing the download process. The validate_default_parameters parameter controls whether an exception should be raised if the specified database, schema, or warehouse does not exist or is not authorized. Setting this parameter to True can help catch errors early and avoid unexpected results.


NEW QUESTION # 99
What are Common Query Problems a Data Engineer can identified using Query Profiler?

  • A. Queries Too Large to Fit in Memory
  • B. "Exploding" Joins i.e Joins resulting due to a "Cartesian product"
  • C. Inefficient Pruning
  • D. Ineffective Data Sharing

Answer: A,B,C

Explanation:
Explanation
"Exploding" Joins
One of the common mistakes SQL users make is joining tables without providing a join condition (resulting in a "Cartesian product"), or providing a condition where records from one table match multiple records from another table. For such queries, the Join operator produces significantly (often by orders of magnitude) more tuples than it consumes.
This can be observed by looking at the number of records produced by a Join operator in the profile interface, and typically is also reflected in Join operator consuming a lot of time.
Queries Too Large to Fit in Memory
For some operations (e.g. duplicate elimination for a huge data set), the amount of memory available for the compute resources used to execute the operation might not be sufficient to hold intermediate results. As a result, the query processing engine will start spilling the data to local disk. If the local disk space is not sufficient, the spilled data is then saved to remote disks.
This spilling can have a profound effect on query performance (especially if remote disk is used for spilling).
Spilling statistics can be checked in Query Profile Interface.
Inefficient Pruning
Snowflake collects rich statistics on data allowing it not to read unnecessary parts of a table based on the query filters. However, for this to have an effect, the data storage order needs to be correlat-ed with the query filter attributes.
The efficiency of pruning can be observed by comparing Partitions scanned and Partitions total sta-tistics in the TableScan operators. If the former is a small fraction of the latter, pruning is efficient. If not, the pruning did not have an effect.
Of course, pruning can only help for queries that actually filter out a significant amount of data. If the pruning statistics do not show data reduction, but there is a Filter operator above TableScan which filters out a number of records, this might signal that a different data organization might be beneficial for this query.


NEW QUESTION # 100
A company extracts approximately 1 TB of data every day from data sources such as SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. Some of the data sources have undefined data schemas or data schemas that change.
A data engineer must implement a solution that can detect the schema for these data sources.
The solution must extract, transform, and load the data to an Amazon S3 bucket. The company has a service level agreement (SLA) to load the data into the S3 bucket within 15 minutes of data creation.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use Amazon EMR to detect the schema and to extract, transform, and load the data into the S3 bucket. Create a pipeline in Apache Spark.
  • B. Use AWS Glue to detect the schema and to extract, transform, and load the data into the S3 bucket. Create a pipeline in Apache Spark.
  • C. Create a PySpark program in AWS Lambda to extract, transform, and load the data into the S3 bucket.
  • D. Create a stored procedure in Amazon Redshift to detect the schema and to extract, transform, and load the data into a Redshift Spectrum table. Access the table from Amazon S3.

Answer: B


NEW QUESTION # 101
......

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