Enhance your career with DEA-C01 PDF Dumps - True Snowflake Exam Questions
New (2025) Download free DEA-C01 PDF for Snowflake Practice Tests
NEW QUESTION # 44
Does sensitive data in Snowflake is modified in an existing table while applying Masking policies?
- A. NO
- B. YES
Answer: A
Explanation:
Explanation
Snowflake supports masking policies as a schema-level object to protect sensitive data from unau-thorized access while allowing authorized users to access sensitive data at query runtime. This means that sensitive data in Snowflake is not modified in an existing table (i.e. no static masking). Rather, when users execute a query in which a masking policy applies, the masking policy condi-tions determine whether unauthorized users see masked, partially masked, obfuscated, or tokenized data.
NEW QUESTION # 45
A financial company wants to implement a data mesh. The data mesh must support centralized data governance, data analysis, and data access control. The company has decided to use AWS Glue for data catalogs and extract, transform, and load (ETL) operations.
Which combination of AWS services will implement a data mesh? (Choose two.)
- A. Use AWS Lake Formation for centralized data governance and access control.
- B. Use Amazon RDS for data storage. Use Amazon EMR for data analysis.
- C. Use Amazon S3 for data storage. Use Amazon Athena for data analysis.
- D. Use AWS Glue DataBrew for centralized data governance and access control.
- E. Use Amazon Aurora for data storage. Use an Amazon Redshift provisioned cluster for data analysis.
Answer: A,C
NEW QUESTION # 46
An airline company is collecting metrics about flight activities for analytics. The company is conducting a proof of concept (POC) test to show how analytics can provide insights that the company can use to increase on-time departures.
The POC test uses objects in Amazon S3 that contain the metrics in .csv format. The POC test uses Amazon Athena to query the data. The data is partitioned in the S3 bucket by date.
As the amount of data increases, the company wants to optimize the storage solution to improve query performance.
Which combination of solutions will meet these requirements? (Choose two.)
- A. Preprocess the .csv data to JSON format by fetching only the document keys that the query requires.
- B. Add a randomized string to the beginning of the keys in Amazon S3 to get more throughput across partitions.
- C. Preprocess the .csv data to Apache Parquet format by fetching only the data blocks that are needed for predicates.
- D. Use an S3 bucket that is in the same account that uses Athena to query the data.
- E. Use an S3 bucket that is in the same AWS Region where the company runs Athena queries.
Answer: C,E
Explanation:
https://docs.aws.amazon.com/athena/latest/ug/performance-tuning.html
NEW QUESTION # 47
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. Create indexes to ensure sorted access to data
- C. Move the query to a larger virtual warehouse
- D. Increase the max cluster count
- E. Add a LIMIT to the ORDER BY If possible
Answer: C,E
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 # 48
A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?
- A. Turn on concurrency scaling in the settings during the creation of any new Redshift cluster.
- B. Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.
- C. Turn on concurrency scaling for the daily usage quota for the Redshift cluster.
- D. Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.
Answer: B
Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/concurrency-scaling-queues.html
NEW QUESTION # 49
A company needs to build a data lake in AWS. The company must provide row-level data access and column-level data access to specific teams. The teams will access the data by using Amazon Athena, Amazon Redshift Spectrum, and Apache Hive from Amazon EMR.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use Amazon S3 for data lake storage. Use Apache Ranger through Amazon EMR to restrict data access by rows and columns. Provide data access by using Apache Pig.
- B. Use Amazon S3 for data lake storage. Use S3 access policies to restrict data access by rows and columns. Provide data access through Amazon S3.
- C. Use Amazon S3 for data lake storage. Use AWS Lake Formation to restrict data access by rows and columns. Provide data access through AWS Lake Formation.
- D. Use Amazon Redshift for data lake storage. Use Redshift security policies to restrict data access by rows and columns. Provide data access by using Apache Spark and Amazon Athena federated queries.
Answer: C
Explanation:
https://docs.aws.amazon.com/lake-formation/latest/dg/cbac-tutorial.html
NEW QUESTION # 50
A Data Engineer is building a pipeline to transform a 1 TD tab e by joining it with supplemental tables The Engineer is applying filters and several aggregations leveraging Common TableExpressions (CTEs) using a size Medium virtual warehouse in a single query in Snowflake.
After checking the Query Profile, what is the recommended approach to MAXIMIZE performance of this query if the Profile shows data spillage?
- A. Switch to a multi-cluster virtual warehouse
- B. Enable clustering on the table
- C. Increase the warehouse size
- D. Rewrite the query to remove the CTEs.
Answer: C
Explanation:
Explanation
The recommended approach to maximize performance of this query if the Profile shows data spillage is to increase the warehouse size. Data spillage occurs when the query requires more memory than the warehouse can provide and has to spill some intermediate results to disk. This can degrade the query performance by increasing the disk IO time. Increasing the warehouse size can increase the amount of memory available for the query and reduce or eliminate data spillage.
NEW QUESTION # 51
What are characteristics of Snowpark Python packages? (Select THREE).
Third-party packages can be registered as a dependency to the Snowpark session using the session, import () method.
- A. Third-party supported Python packages are locked down to prevent hitting
- B. Querying information__schema .packages will provide a list of supported Python packages and versions
- C. Python packages can only be loaded in a local environment
- D. Python packages can access any external endpoints
- E. The SQL command DESCRIBE FUNCTION will list the imported Python packages of the Python User-Defined Function (UDF).
Answer: B,D,E
Explanation:
Explanation
The characteristics of Snowpark Python packages are:
Third-party packages can be registered as a dependency to the Snowpark session using the session.import() method.
The SQL command DESCRIBE FUNCTION will list the imported Python packages of the Python User-Defined Function (UDF).
Querying information_schema.packages will provide a list of supported Python packages and versions.
These characteristics indicate how Snowpark Python packages can be imported, inspected, and verified in Snowflake. The other options are not characteristics of Snowpark Python packages. Option B is incorrect because Python packages can be loaded in both local and remote environments using Snowpark. Option C is incorrect because third-party supported Python packages are not locked down to prevent hitting external endpoints, but rather restricted by network policies and security settings.
NEW QUESTION # 52
Snowpipe API provides a REST endpoint for defining the list of files to ingest that Informs Snow-flake about the files to be ingested into a table. A successful response from this endpoint means that Snowflake has recorded the list of files to add to the table. It does not necessarily mean the files have been ingested. What is name of this Endpoint?
- A. REST endpoints --> loadHistoryScan
- B. REST endpoints --> ingestfiles
- C. REST endpoints--> insertfiles
- D. REST endpoints --> insertReport
Answer: C
Explanation:
Explanation
The Snowpipe API provides a REST endpoint for defining the list of files to ingest.
Endpoint: insertFiles
Informs Snowflake about the files to be ingested into a table. A successful response from this end-point means that Snowflake has recorded the list of files to add to the table. It does not necessarily mean the files have been ingested. For more details, see the response codes below.
In most cases, Snowflake inserts fresh data into the target table within a few minutes.
To Know more about SnowFlake Rest API used for Data File ingestion, do refer:
https://docs.snowflake.com/en/user-guide/data-load-snowpipe-rest-apis.html#data-file-ingestion
NEW QUESTION # 53
Snowpipe loads data from files as soon as they are available in a stage. Automated data loads lever-age event notifications for cloud storage to inform Snowpipe of the arrival of new data files to load. Which Cloud hosted platform provides cross cloud support for automated data loading via Snow-pipe?
- A. None of the Above currently provide cross cloud support for Snowpipe.
- B. AWS
- C. AZURE
- D. GCP
Answer: B
Explanation:
Explanation
Cross-cloud support only available to accounts hosted on Amazon Web Services currently.
NEW QUESTION # 54
Which Function would Data engineer used to recursively resume all tasks in Chain of Tasks rather than resuming each task individually (using ALTER TASK ... RESUME)?
- A. SYSTEM$TASK_DEPENDENTS_ENABLE
- B. SYSTEM$TASK_DEPENDENTS_RESUME
- C. SYSTEM$TASK_DEPENDENTS
- D. SYSTEM$TASK_RECURSIVE_ENABLE
Answer: A
Explanation:
Explanation
To recursively resume all tasks in a DAG(A Directed Acyclic Graph (DAG) is a series of tasks com-posed of a single root task and additional tasks, organized by their dependencies.), query the SYS-TEM$TASK_DEPENDENTS_ENABLE function rather than resuming each task individually (us-ing ALTER TASK ... RESUME).
NEW QUESTION # 55
Steven created the task, what additional privileges required by Steven on the task so that he can suspend or resume the tasks?
- A. Steven is already owner of the task; he can execute the task & suspend/resume the task without any additional privileges.
- B. Steven must have SUSPEND privilege on the task so that he can suspend or resume the task.
- C. In addition to the task owner, a Steven Role must have OPERATE privilege on the task so that he can suspend or resume the task.
- D. Steven needs to have Global Managed RESUME privilege by TASK administrator.
Answer: C
Explanation:
Explanation
In addition to the task ownership privilege, a role that has the OPERATE privilege on the task can suspend or resume the task.
NEW QUESTION # 56
Data Engineer, ran the below clustering depth analysis function:
select system$clustering_depth('TPCH_CUSTOMERS', '(C1, C6)', 'C9 = 30'); on TPCH_CUSTOMERS table, will return which of the following?
- A. An error: this function does not accept predicates ('C9 = 30') as parameter.
- B. Calculate the clustering depth for a table using the clustering key defined for the table.
- C. Calculate the clustering depth for a table using mentioned columns in the table.
- D. An error: this function does not accept lists of columns as a third parameter.
Answer: C
NEW QUESTION # 57
A company uses Amazon Redshift for its data warehouse. The company must automate refresh schedules for Amazon Redshift materialized views.
Which solution will meet this requirement with the LEAST effort?
- A. Use an AWS Glue workflow to refresh the materialized views.
- B. Use the query editor v2 in Amazon Redshift to refresh the materialized views.
- C. Use Apache Airflow to refresh the materialized views.
- D. Use an AWS Lambda user-defined function (UDF) within Amazon Redshift to refresh the materialized views.
Answer: D
Explanation:
AWS Lambda allows running code in response to triggers without needing to provision or manage servers. However, creating a UDF within Amazon Redshift to call a Lambda function for this purpose involves writing custom code and managing permissions between Lambda and Redshift.
NEW QUESTION # 58
Which privilege are required on an object (i.e. user or role) with USERADMIN Role can modify the object properties?
- A. OWNERSHIP
- B. MODIFY
- C. OPEARTE
- D. MANAGE GRANTS
Answer: A
NEW QUESTION # 59
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