2024 Updated Verified Pass DEA-C01 Exam - Real Questions and Answers
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NEW QUESTION # 27
As Data Engineer, you have requirement to Load set of New Product Files containing Product rele-vant information into the Snowflake internal tables, Later you analyzed that some of the Source files are already loaded in one of the historical batch & for that you have prechecked Metadata col-umn LAST_MODIFIED date for a staged data file & found out that LAST_MODIFIED date is older than 64 days for few files and the initial set of data was loaded into the table more than 64 days earlier, Which one is the best approach to Load Source data files with expired load metadata along with set of files whose metadata might be available to avoid data duplication?
- A. Set the FORCE option to load all files, ignoring load metadata if it exists.
- B. To load files whose metadata has expired, set the LOAD_UNCERTAIN_FILES copy option to true.
- C. The COPY command cannot definitively determine whether a file has been loaded al-ready if the LAST_MODIFIED date is older than 64 days and the initial set of data was loaded into the table more than 64 days earlier (and if the file was loaded into the table, that also occurred more than 64 days earlier). In this case, to prevent accidental reload, the command skips the product files by default.
- D. Since the initial set of data for the table (i.e. the first batch after the table was created) was loaded, we can simply use the COPY INTO command to load all the product files with the known load status irrespective of their column LAST_MODIFIED date values.
Answer: B
Explanation:
Explanation
To load files whose metadata has expired, set the LOAD_UNCERTAIN_FILES copy option to true. The copy option references load metadata, if available, to avoid data duplication, but also at-tempts to load files with expired load metadata.
Alternatively, set the FORCE option to load all files, ignoring load metadata if it exists. Note that this option reloads files, potentially duplicating data in a table.
Please refer the Example as mentioned in the link below:
https://docs.snowflake.com/en/user-guide/data-load-considerations-load.html#loading-older-files
NEW QUESTION # 28
A lab uses IoT sensors to monitor humidity, temperature, and pressure for a project. The sensors send 100 KB of data every 10 seconds. A downstream process will read the data from an Amazon S3 bucket every 30 seconds.
Which solution will deliver the data to the S3 bucket with the LEAST latency?
- A. Use Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose to deliver the data to the S3 bucket. Use the default buffer interval for Kinesis Data Firehose.
- B. Use Amazon Kinesis Data Streams to deliver the data to the S3 bucket. Configure the stream to use 5 provisioned shards.
- C. Use Amazon Kinesis Data Streams and call the Kinesis Client Library to deliver the data to the S3 bucket. Use a 5 second buffer interval from an application.
- D. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) and Amazon Kinesis Data Firehose to deliver the data to the S3 bucket. Use a 5 second buffer interval for Kinesis Data Firehose.
Answer: D
NEW QUESTION # 29
A company is building a dashboard for thousands of Analysts. The dashboard presents the results of a few summary queries on tables that are regularly updated. The query conditions vary by tope according to what data each Analyst needs Responsiveness of the dashboard queries is a top priority, and the data cache should be preserved.
How should the Data Engineer configure the compute resources to support this dashboard?
- A. Assign all queries to a multi-cluster virtual warehouse set to maximized mode Monitor to determine the smallest suitable number of clusters.
- B. Create a virtual warehouse for every 250 Analysts Monitor to determine how many of these virtual warehouses are being utilized at capacity.
- C. Create a size XL virtual warehouse to support all the dashboard queries Monitor query runtimes to determine whether the virtual warehouse should be resized.
- D. Assign queries to a multi-cluster virtual warehouse with economy auto-scaling Allow the system to automatically start and stop clusters according to demand.
Answer: A
Explanation:
Explanation
This option is the best way to configure the compute resources to support this dashboard. By assigning all queries to a multi-cluster virtual warehouse set to maximized mode, the Data Engineer can ensure that there is enough compute capacity to handle thousands of concurrent queries from different analysts. A multi-cluster virtual warehouse can scale up or down by adding or removing clusters based on the load. A maximized scaling policy ensures that there is always at least one cluster running and that new clusters are added as soon as possible whenneeded. By monitoring the utilization and performance of the virtual warehouse, the Data Engineer can determine the smallest suitable number of clusters that can meet the responsiveness requirement and minimize costs.
NEW QUESTION # 30
Data Engineer try to load data from external stage using Snowpipe & later find out that some Set of Files Not Loaded. To debug the issue, she used COPY_HISTORY function & cross verified that its output indicates a subset of files was not loaded. What is possible reason of arising this situation in both REST API call and Auto-Ingest methods? [Select 2]
- A. A backlog of data files already existed in the external stage do not have any impact on Load failure, as this is well managed by serverless SnowPipe
- B. Files modified and staged again after 14 days and Snowpipe ignores modified files that are staged again.
- C. External event-driven functionality is used to call the REST APIs, and a backlog of da-ta files already existed in the external stage before the events were configured.
- D. An event notification failure prevented a set of files from getting queued.
Answer: C,D
Explanation:
Explanation
COPY_HISTORY Record Indicates Unloaded Subset of Files:
If the COPY_HISTORY function output indicates a subset of files was not loaded, you may try to "refresh" the pipe.
This situation can arise in any of the following situations:
The external stage was previously used to bulk load data using the COPY INTO table command.
REST API:
o External event-driven functionality is used to call the REST APIs, and a backlog of data files al-ready existed in the external stage before the events were configured.
Auto-ingest:
o A backlog of data files already existed in the external stage before event notifications were con-figured.
o An event notification failure prevented a set of files from getting queued.
To load the data files in your external stage using the configured pipe, execute an ALTER PIPE ... REFRESH statement.
NEW QUESTION # 31
A media company uses software as a service (SaaS) applications to gather data by using third- party tools. The company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform analytics based on the data.
Which AWS service or feature will meet these requirements with the LEAST operational overhead?
- A. AWS Glue Data Catalog
- B. Amazon AppFlow
- C. Amazon Kinesis
- D. Amazon Managed Streaming for Apache Kafka (Amazon MSK)
Answer: B
Explanation:
https://d1.awsstatic.com/solutions/guidance/architecture-diagrams/integrating-third-party-saas- data-using-amazon-appflow.pdf Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between Software as a Service (SaaS) applications like Salesforce, Marketo, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift, in just a few clicks. It can store the raw data pulled from SaaS applications in Amazon S3, and integrates with AWS Glue Data Catalog to catalog and store metadata.
NEW QUESTION # 32
An insurance company stores transaction data that the company compressed with gzip.
The company needs to query the transaction data for occasional audits.
Which solution will meet this requirement in the MOST cost-effective way?
- A. Store the data in Amazon Glacier Instant Retrieval. Use Amazon Athena to query the data.
- B. Store the data in Amazon S3. Use Amazon Athena to query the data.
- C. Store the data in Amazon S3. Use Amazon S3 Select to query the data.
- D. Store the data in Amazon Glacier Flexible Retrieval. Use Amazon S3 Glacier Select to query the data.
Answer: D
NEW QUESTION # 33
In Which Data Modelling Technique, Data Engineer generally refer the terms Hubs & Satellites?
- A. Star Schema
- B. Data Hub
- C. Data Vault
- D. Snowflake Schema
Answer: C
Explanation:
Explanation
In Data Vault modelling, Hubs are entities of interest to the business.
They contain just a distinct list of business keys and metadata about when each key was first loaded and from where.
In Data Vault modelling, Satellites connect to Hubs or Links. They are Point in Time: so we can ask and answer the question, "what did we know when?" Satellites contain data about their parent Hub or Link and metadata about when the data was load-ed, from where, and a business effectivity date.
NEW QUESTION # 34
A data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file.
Which Step Functions state should the data engineer use to meet these requirements?
- A. Parallel state
- B. Choice state
- C. Wait state
- D. Map state
Answer: D
Explanation:
Map state is designed precisely for the requirement described. It allows you to iterate over a collection of items, processing each item individually. The Map state can automatically manage the iteration and execute the specified transformation on each item in parallel, making it the perfect choice for parallel processing of a large collection of data files.
NEW QUESTION # 35
Data Engineer is looking out to delete staged files automatically/periodically when the data is suc-cessfully loaded into tables by the Snowpipe. For achieving the same, which options/command is best suited: [Select 2]
- A. REMOVE_STAGE_FILES option can be set as True in the COPY INTO Command embedded in PIPE objects definition.
- B. To remove staged files that no longer needed, periodically REMOVE command can be executed to delete the files.
- C. To remove staged files that no longer needed, periodically DELETE command can be executed to delete the files.
- D. PURGE option can be set as True in the COPY INTO Command embedded in PIPE objects definition.
Answer: B,D
Explanation:
Explanation
Deleting Staged Files After Snowpipe Loads the Data
Pipe objects do not support the PURGE copy option. Snowpipe cannot delete staged files automat-ically when the data is successfully loaded into tables.
To remove staged files that you no longer need, It is recommended to periodically executing the REMOVE command to delete the files.
Alternatively, configure any lifecycle management features provided by cloud storage service pro-vider.
NEW QUESTION # 36
A company uses a data lake that is based on an Amazon S3 bucket. To comply with regulations, the company must apply two layers of server-side encryption to files that are uploaded to the S3 bucket. The company wants to use an AWS Lambda function to apply the necessary encryption.
Which solution will meet these requirements?
- A. Use both server-side encryption with AWS KMS keys (SSE-KMS) and the Amazon S3 Encryption Client.
- B. Use dual-layer server-side encryption with AWS KMS keys (DSSE-KMS).
- C. Use server-side encryption with customer-provided keys (SSE-C) before files are uploaded.
- D. Use server-side encryption with AWS KMS keys (SSE-KMS).
Answer: B
Explanation:
https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingDSSEncryption.html
NEW QUESTION # 37
Data Engineer identified use case where he decided to use materialized view for query perfor-mance. Which one is not the limitation he must be aware of before using MVs in their use case?
- A. Truncating a materialized view is not supported.
- B. A materialized views cannot be created on Shared Data.
- C. A materialized views does not support clustering.
- D. A materialized view cannot include HAVING clauses OR ORDER BY clause.
- E. You cannot directly clone a materialized view by using the CREATE MATERIAL-IZED VIEW ...
CLONE... command. - F. A materialized view can query only a single table & Joins, including self-joins, are not supported.
- G. Time Travel is not currently supported on materialized views.
- H. Context Functions like CURRENT_TIME or CURRENT_TIMESTAMP is not per-mitted.
Answer: C,D
Explanation:
Explanation
Defining a clustering key on a materialized view is supported and can increase performance in many situations. However, it also adds costs.
If you cluster both the materialized view(s) and the base table on which the materialized view(s) are defined, you can cluster the materialized view(s) on different columns from the columns used to cluster the base table.
You can create a materialized view on shared data.
Also You can use Snowflake's data sharing feature to share a materialized view.
Rest all are correct.
NEW QUESTION # 38
Bob, a Lead Data Engineer is looking out to get the function definition & queried below statement to check if this function is secure enough to use in his script or not.
select is_secure from information_schema.functions where function_name = 'JOHNFUNCTION'; From the query output he is sure that, Function is secure UDF, what are the way provided by snow-flake to get the function definition of secure UDF?
- A. UDF definition or text, is visible to users via Query Profile (in the web interface).
- B. SHOW FUNCTIONS Commands
- C. He can get the secure UDF definition using GET_DDL utility function.
- D. Declaring a UDF as "secure" hide the definition from Bob & all the required Definition commands will throw error.
Answer: D
NEW QUESTION # 39
A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs.
The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account.
Which solution will meet these requirements?
- A. Create a destination data stream in the production AWS account. In the security AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the production AWS account.
- B. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the security AWS account.
- C. Create a destination data stream in the production AWS account. In the production AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the security AWS account.
- D. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the production AWS account.
Answer: D
NEW QUESTION # 40
David, a Lead Data engineer with XYZ company looking out to improve query performance & oth-er benefits while working with Tables, Regular Views, MVs and Cached Results.
Which one of the following does not shows key similarities and differences between tables, regular views, cached query results, and materialized views while choosing any of them by David?
- A. As with non-materialized views, a materialized view automatically inherits the privileges of its base table.
- B. Cached Query Results: Used only if data has not changed and if query only uses de-terministic functions (e.g. not CURRENT_DATE).
- C. Both materialized views and regular views enhance data security by allowing data to be exposed or hidden at the row level or column level.
- D. 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.
- E. Regular views do not cache data, and therefore cannot improve performance by cach-ing.
Answer: A
Explanation:
Explanation
Materialized Views, like other database objects (tables, views, UDFs, etc.), are owned by a role and have privileges that can be granted to other roles.
You can grant the following privileges on a materialized view:
SELECT
As with non-materialized views, a materialized view does not automatically inherit the privileges of its base table. You should explicitly grant privileges on the materialized view to the roles that should use that view.
As with non-materialized views, a user who wishes to access a materialized view needs privileges only on the view, not on the underlying object(s) that the view references.
Rest is correct.
NEW QUESTION # 41
The following chart represents the performance of a virtual warehouse over time:
A DataEngineer notices that the warehouse is queueing queries The warehouse is size X-Smallthe minimum and maximum cluster counts are set to 1 the scaling policy is set to i and auto-suspend is set to 10 minutes.
How canthe performance be improved?
- A. Change the cluster settings
- B. Increase the size of the warehouse
- C. Change the scaling policy to economy
- D. Change auto-suspend to a longer time frame
Answer: B
Explanation:
Explanation
The performance can be improved by increasing the size of the warehouse. The chart shows that the warehouse is queueing queries, which means that there are more queries than the warehouse can handle at its current size. Increasing the size of the warehouse will increase its processing power and concurrency limit, which could reduce the queueing time and improve the performance. The other options are not likely to improve the performance significantly. Option A, changing the cluster settings, will not help unless the minimum and maximum cluster countsare increased to allow for multi-cluster scaling. Option C, changing the scaling policy to economy, will not help because it will reduce the responsiveness of the warehouse to scale up or down based on demand. Option D, changing auto-suspend to a longer time frame, will not help because it will only affect how long the warehouse stays idle before suspending itself.
NEW QUESTION # 42
A company is building an analytics solution. The solution uses Amazon S3 for data lake storage and Amazon Redshift for a data warehouse. The company wants to use Amazon Redshift Spectrum to query the data that is in Amazon S3.
Which actions will provide the FASTEST queries? (Choose two.)
- A. Partition the data based on the most common query predicates.
- B. Use gzip compression to compress individual files to sizes that are between 1 GB and 5 GB.
- C. Use a columnar storage file format.
- D. Use file formats that are not splittable.
- E. Split the data into files that are less than 10 KB.
Answer: A,C
NEW QUESTION # 43
A company plans to use Amazon Kinesis Data Firehose to store data in Amazon S3. The source data consists of 2 MB .csv files. The company must convert the .csv files to JSON format. The company must store the files in Apache Parquet format.
Which solution will meet these requirements with the LEAST development effort?
- A. Use Kinesis Data Firehose to convert the .csv files to JSON. Use an AWS Lambda function to store the files in Parquet format.
- B. Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON. Use Kinesis Data Firehose to store the files in Parquet format.
- C. Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON and stores the files in Parquet format.
- D. Use Kinesis Data Firehose to convert the .csv files to JSON and to store the files in Parquet format.
Answer: D
Explanation:
By using the built-in transformation and format conversion features of Kinesis Data Firehose, you achieve the desired result with minimal custom development, thereby meeting the requirements efficiently and cost-effectively.
NEW QUESTION # 44
A data engineer must ingest a source of structured data that is in .csv format into an Amazon S3 data lake. The .csv files contain 15 columns. Data analysts need to run Amazon Athena queries on one or two columns of the dataset. The data analysts rarely query the entire file.
Which solution will meet these requirements MOST cost-effectively?
- A. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to ingest the data into the data lake in JSON format.
- B. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to write the data into the data lake in Apache Parquet format.
- C. Use an AWS Glue PySpark job to ingest the source data into the data lake in .csv format.
- D. Use an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format.
Answer: B
NEW QUESTION # 45
A company stores datasets in JSON format and .csv format in an Amazon S3 bucket. The company has Amazon RDS for Microsoft SQL Server databases, Amazon DynamoDB tables that are in provisioned capacity mode, and an Amazon Redshift cluster. A data engineering team must develop a solution that will give data scientists the ability to query all data sources by using syntax similar to SQL.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use AWS Lake Formation to create a data lake. Use Lake Formation jobs to transform the data from all data sources to Apache Parquet format. Store the transformed data in an S3 bucket. Use Amazon Athena or Redshift Spectrum to query the data.
- B. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Redshift Spectrum to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.
- C. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format. Store the transformed data in an S3 bucket. Use Amazon Athena to query the original and transformed data from the S3 bucket.
- D. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Amazon Athena to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.
Answer: D
NEW QUESTION # 46
A data engineer uses Amazon Redshift to run resource-intensive analytics processes once every month. Every month, the data engineer creates a new Redshift provisioned cluster. The data engineer deletes the Redshift provisioned cluster after the analytics processes are complete every month. Before the data engineer deletes the cluster each month, the data engineer unloads backup data from the cluster to an Amazon S3 bucket.
The data engineer needs a solution to run the monthly analytics processes that does not require the data engineer to manage the infrastructure manually.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use Amazon Step Functions to pause the Redshift cluster when the analytics processes are complete and to resume the cluster to run new processes every month.
- B. Use Amazon Redshift Serverless to automatically process the analytics workload.
- C. Use the AWS CLI to automatically process the analytics workload.
- D. Use AWS CloudFormation templates to automatically process the analytics workload.
Answer: B
Explanation:
Use Amazon Redshift Serverless. This option allows the data engineer to focus on the analytics processes themselves without worrying about cluster provisioning, scaling, or management. It provides an on-demand, serverless solution that can handle variable workloads and is cost- effective for intermittent and irregular processing needs like those described.
NEW QUESTION # 47
A company currently stores all of its data in Amazon S3 by using the S3 Standard storage class.
A data engineer examined data access patterns to identify trends. During the first 6 months, most data files are accessed several times each day. Between 6 months and 2 years, most data files are accessed once or twice each month. After 2 years, data files are accessed only once or twice each year.
The data engineer needs to use an S3 Lifecycle policy to develop new data storage rules. The new storage solution must continue to provide high availability.
Which solution will meet these requirements in the MOST cost-effective way?
- A. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
- B. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.
- C. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.
- D. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
Answer: A
NEW QUESTION # 48
A manufacturing company has many IoT devices in facilities around the world. The company uses Amazon Kinesis Data Streams to collect data from the devices. The data includes device ID, capture date, measurement type, measurement value, and facility ID. The company uses facility ID as the partition key.
The company's operations team recently observed many WriteThroughputExceeded exceptions.
The operations team found that some shards were heavily used but other shards were generally idle.
How should the company resolve the issues that the operations team observed?
- A. Change the partition key from facility ID to a randomly generated key.
- B. Change the partition key from facility ID to capture date.
- C. Archive the data on the producer's side.
- D. Increase the number of shards.
Answer: A
Explanation:
The best solution to resolve the issue of uneven shard usage and WriteThroughputExceeded exceptions is to balance the load more evenly across the shards. This can be effectively achieved by changing the partition key to something that ensures a more uniform distribution of data across the shards.
NEW QUESTION # 49
A company uses an on-premises Microsoft SQL Server database to store financial transaction data. The company migrates the transaction data from the on-premises database to AWS at the end of each month. The company has noticed that the cost to migrate data from the on-premises database to an Amazon RDS for SQL Server database has increased recently.
The company requires a cost-effective solution to migrate the data to AWS. The solution must cause minimal downtown for the applications that access the database.
Which AWS service should the company use to meet these requirements?
- A. AWS Direct Connect
- B. AWS Database Migration Service (AWS DMS)
- C. AWS Lambda
- D. AWS DataSync
Answer: B
Explanation:
AWS Database Migration Service (DMS) is specifically designed for migrating data from various sources, including on-premises databases, to AWS with minimal downtime and disruption to applications. It supports homogeneous migrations (e.g., SQL Server to SQL Server) as well as heterogeneous migrations (e.g., SQL Server to Amazon RDS for SQL Server).
NEW QUESTION # 50
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