[May-2025] Exam DEA-C01: New Brain Dump Professional - TroytecDumps
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Snowflake DEA-C01 Exam Syllabus Topics:
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NEW QUESTION # 33
A company uses Amazon RDS for MySQL as the database for a critical application. The database workload is mostly writes, with a small number of reads.
A data engineer notices that the CPU utilization of the DB instance is very high. The high CPU utilization is slowing down the application. The data engineer must reduce the CPU utilization of the DB Instance.
Which actions should the data engineer take to meet this requirement? (Choose two.)
- A. Implement caching to reduce the database query load.
- B. Modify the database schema to include additional tables and indexes.
- C. Use the Performance Insights feature of Amazon RDS to identify queries that have high CPU utilization. Optimize the problematic queries.
- D. Reboot the RDS DB instance once each week.
- E. Upgrade to a larger instance size.
Answer: C,E
NEW QUESTION # 34
A company receives call logs as Amazon S3 objects that contain sensitive customer information.
The company must protect the S3 objects by using encryption. The company must also use encryption keys that only specific employees can access.
Which solution will meet these requirements with the LEAST effort?
- A. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the KMS keys that encrypt the objects.
- B. Use server-side encryption with customer-provided keys (SSE-C) to encrypt the objects that contain customer information. Restrict access to the keys that encrypt the objects.
- C. Use an AWS CloudHSM cluster to store the encryption keys. Configure the process that writes to Amazon S3 to make calls to CloudHSM to encrypt and decrypt the objects. Deploy an IAM policy that restricts access to the CloudHSM cluster.
- D. Use server-side encryption with Amazon S3 managed keys (SSE-S3) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the Amazon S3 managed keys that encrypt the objects.
Answer: A
NEW QUESTION # 35
SYSTEM$CLUSTERING_INFORMATION functions returns clustering information, including average clustering depth, for a table based on one or more columns in the table. The function returns a JSON object containing average_overlaps name/value pairs. Does High average_overlaps indicates well organized Clustering?
- A. NO
- B. YES
Answer: A
Explanation:
Explanation
Higher the avg_overlap indicates poorly organized clustering.
NEW QUESTION # 36
If external software i.e. TIBCO, exports Data fields enclosed in quotes but inserts a leading space before the opening quotation character for each field, How Snowflake handle it? [Select 2]
- A. field_optionally_enclosed_by option along with TRIM_IF function in COPY INTO statement can be used to handle this scenario successfully.
- B. COPY command trims the leading space and removes the quotation marks enclosing each field
1.copy into SFtable
2.from @%SFtable
3.file_format = (type = csv trim_space=true field_optionally_enclosed_by = '0x22'); - C. Snowflake automatically handles leading spaces by trimming implicitly & removes the quotation marks enclosing each field.
- D. Snowflake reads the leading space rather than the opening quotation character as the beginning of the field and the quotation characters are interpreted as string data.
(Correct)
Answer: B
Explanation:
Explanation
If your external software exports fields enclosed in quotes but inserts a leading space before the opening quotation character for each field, Snowflake reads the leading space rather than the open-ing quotation character as the beginning of the field. The quotation characters are interpreted as string data.
Use the TRIM_SPACE file format option to remove undesirable spaces during the data load.
NEW QUESTION # 37
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 --> insertReport
- C. REST endpoints --> ingestfiles
- D. REST endpoints--> insertfiles
Answer: D
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 # 38
A CSV file around 1 TB in size is generated daily on an on-premise server A corresponding table. Internal stage, and file format have already been created in Snowflake to facilitate the data loading process How can the process of bringing the CSV file into Snowflake be automated using the LEAST amount of operational overhead?
- A. On the on-premise server schedule a SQL file to run using SnowSQL that executes a PUT to push a specific file to the internal stage. Create a pipe that runs a copy into statement that references the internal stage Snowpipe auto-ingest will automatically load the file from the internal stage when the new file lands in the internal stage.
- B. Create a task in Snowflake that executes once a day and runs a copy into statement that references the internal stage The internal stage will read the files directly from the on-premise server and copy the newest file into the table from the on-premise server to the Snowflake table
- C. On the on-premise server schedule a SQL file to run using SnowSQL that executes a PUT to push a specific file to the internal stage Create a task that executes once a day m Snowflake and runs a OOPY WTO statement that references the internal stage Schedule the task to start after the file lands in the internal stage
- D. On the on premise server schedule a Python file that uses the Snowpark Python library. The Python script will read the CSV data into a DataFrame and generate an insert into statement that will directly load into the table The script will bypass the need to move a file into an internal stage
Answer: A
Explanation:
Explanation
This option is the best way to automate the process of bringing the CSV file into Snowflake with the least amount of operational overhead. SnowSQL is a command-line tool that can be used to execute SQL statements and scripts on Snowflake. By scheduling a SQL file that executes a PUT command, the CSV file can be pushed from the on-premise server to the internal stage in Snowflake. Then, by creating a pipe that runs a COPY INTO statement that references the internal stage, Snowpipe can automatically load the file from the internal stage into the table when it detects a new file in the stage. This way, there is no need to manually start or monitor a virtual warehouse or task.
NEW QUESTION # 39
A data engineer needs to schedule a workflow that runs a set of AWS Glue jobs every day. The data engineer does not require the Glue jobs to run or finish at a specific time.
Which solution will run the Glue jobs in the MOST cost-effective way?
- A. Choose the FLEX execution class in the Glue job properties.
- B. Choose the latest version in the GlueVersion field in the Glue job properties.
- C. Choose the STANDARD execution class in the Glue job properties.
- D. Use the Spot Instance type in Glue job properties.
Answer: A
Explanation:
Flex allows you to optimize your costs on your non-urgent or non-time sensitive data integration workloads such as testing, and one-time data loads. With Flex, AWS Glue jobs run on spare compute capacity instead of dedicated hardware. The start and runtimes of jobs using Flex can vary because spare compute resources aren't readily available and can be reclaimed during the run of a job.
https://aws.amazon.com/blogs/big-data/introducing-aws-glue-flex-jobs-cost-savings-on-etl- workloads/
NEW QUESTION # 40
A telecommunications company collects network usage data throughout each day at a rate of several thousand data points each second. The company runs an application to process the usage data in real time. The company aggregates and stores the data in an Amazon Aurora DB instance.
Sudden drops in network usage usually indicate a network outage. The company must be able to identify sudden drops in network usage so the company can take immediate remedial actions.
Which solution will meet this requirement with the LEAST latency?
- A. Create an AWS Lambda function to query Aurora for drops in network usage. Use Amazon EventBridge to automatically invoke the Lambda function every minute.
- B. Replace the Aurora database with an Amazon DynamoDB table. Create an AWS Lambda function to query the DynamoDB table for drops in network usage every minute. Use DynamoDB Accelerator (DAX) between the processing application and DynamoDB table.
- C. Create an AWS Lambda function within the Database Activity Streams feature of Aurora to detect drops in network usage.
- D. Modify the processing application to publish the data to an Amazon Kinesis data stream. Create an Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) application to detect drops in network usage.
Answer: D
NEW QUESTION # 41
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. AZURE
- B. GCP
- C. None of the Above currently provide cross cloud support for Snowpipe.
- D. AWS
Answer: D
Explanation:
Explanation
Cross-cloud support only available to accounts hosted on Amazon Web Services currently.
NEW QUESTION # 42
What kind of Snowflake integration is required when defining an external function in Snowflake?
- A. HTTP integration
- B. Security integration
- C. Notification integration
- D. API integration
Answer: D
Explanation:
Explanation
An API integration is required when defining an external function in Snowflake. An API integration is a Snowflake object that defines how Snowflake communicates with an externalservice via HTTPS requests and responses. An API integration specifies parameters such as URL, authentication method, encryption settings, request headers, and timeout values. An API integration is used to create an external function object that invokes the external service from within SQL queries.
NEW QUESTION # 43
Which are supported Programming Languages for Creating UDTFs?
- A. Node.javascript
- B. Java
- C. Javascript
- D. Perl
- E. Python
Answer: B,C,E
NEW QUESTION # 44
Ron, Snowflake Developer needs to capture change data (insert only) on the source views, for that he follows the below steps:
Enable change tracking on the source views & its underlying tables.
Inserted the data via Scripts scheduled with the help of Tasks.
then simply run the below Select statements.
1.select *
2.from test_table
3.changes(information => append_only)
4.at(timestamp => (select current_timestamp()));
Select the Correct Query Execution Output option below:
- A. Select query will fail with error: 'SQL compilation error-Incorrect Keyword "Chang-es()" found'
- B. Select statement complied but gives erroneous results.
- C. Developer missed to create stream on the source table which can further query to cap-ture DML records.
- D. No Error reported, select command gives Changed records with Metadata columns as change tracking enabled on the Source views & its underlying tables.
Answer: D
Explanation:
Explanation
As an alternative to streams, Snowflake supports querying change tracking metadata for tables or views using the CHANGES clause for SELECT statements. The CHANGES clause enables query-ing change tracking metadata between two points in time without having to create a stream with an explicit transactional offset.
To Know more about Snowflake CHANGES clause, please refer the mentioned link:
https://docs.snowflake.com/en/sql-reference/constructs/changes
NEW QUESTION # 45
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. Use an S3 bucket that is in the same account that uses Athena to query the data.
- C. Add a randomized string to the beginning of the keys in Amazon S3 to get more throughput across partitions.
- D. Preprocess the .csv data to Apache Parquet format by fetching only the data blocks that are needed for predicates.
- E. Use an S3 bucket that is in the same AWS Region where the company runs Athena queries.
Answer: D,E
Explanation:
https://docs.aws.amazon.com/athena/latest/ug/performance-tuning.html
NEW QUESTION # 46
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 workload management (WLM) for Redshift Serverless workgroups.
- B. Turn on concurrency scaling in the settings during the creation of any new Redshift cluster.
- C. Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.
- D. Turn on concurrency scaling for the daily usage quota for the Redshift cluster.
Answer: C
Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/concurrency-scaling-queues.html
NEW QUESTION # 47
A company has a business intelligence platform on AWS. The company uses an AWS Storage Gateway Amazon S3 File Gateway to transfer files from the company's on-premises environment to an Amazon S3 bucket.
A data engineer needs to setup a process that will automatically launch an AWS Glue workflow to run a series of AWS Glue jobs when each file transfer finishes successfully.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Determine when the file transfers usually finish based on previous successful file transfers. Set up an Amazon EventBridge scheduled event to initiate the AWS Glue jobs at that time of day.
- B. Set up an AWS Lambda function that will invoke the AWS Glue Workflow. Set up an event for the creation of an S3 object as a trigger for the Lambda function.
- C. Set up an Amazon EventBridge event that initiates the AWS Glue workflow after every successful S3 File Gateway file transfer event.
- D. Set up an on-demand AWS Glue workflow so that the data engineer can start the AWS Glue workflow when each file transfer is complete.
Answer: C
Explanation:
Using EventBridge directly to trigger the AWS Glue workflow upon S3 events is straightforward and leverages AWS's event-driven architecture, requiring minimal maintenance.
NEW QUESTION # 48
To view/monitor the clustering metadata for a table, Snowflake provides which of the following system functions?
- A. SYSTEM$CLUSTERING_INFORMATION (including clustering depth)
- B. SYSTEM$CLUSTERING_KEY_INFORMATION (including clustering depth)
- C. SYSTEM$CLUSTERING_DEPTH_KEY
- D. SYSTEM$CLUSTERING_DEPTH
Answer: A,D
Explanation:
Explanation
SYSTEM$CLUSTERING_DEPTH:
Computes the average depth of the table according to the specified columns (or the clustering key defined for the table). The average depth of a populated table (i.e. a table containing data) is always 1 or more. The smaller the average depth, the better clustered the table is with regards to the speci-fied columns.
Calculate the clustering depth for a table using two columns in the table:
SELECT SYSTEM$CLUSTERING_DEPTH('TPCH_PRODUCT', '(C2, C9)');
SYSTEM$CLUSTERING_INFORMATION:
Returns clustering information, including average clustering depth, for a table based on one or more columns in the table.
SELECT SYSTEM$CLUSTERING_INFORMATION('SAMPLE_TABLE', '(col1, col3)');
NEW QUESTION # 49
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