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DEA-C01 Dumps Questions Study Exam Guide
NEW QUESTION # 149
Data Engineer is using existing pipe that automates data loads using event notifications, later he figured out the needs to modify pipe properties. For the same, He decided to recreate the pipe as best practice. He followed the below steps for the same.
1. Query the SYSTEM$PIPE_STATUS function and verify that the pipe execution state is RUN-NING.
2. Recreate the pipe (using CREATE OR REPLACE PIPE).
3. Query the SYSTEM$PIPE_STATUS function and verify that the pipe execution state is RUN-NING.
Which are the Missing recommended steps while Recreating Pipes for Automated Data Loads?
- A. Pause the pipe (using ALTER PIPE ... SET PIPE_EXECUTION_PAUSED = true) Pre & Post recreation & Resume after recreation (using ALTER PIPE ... SET PIPE_EXECUTION_PAUSED = false).
- B. Terminate the existing pipe (using ALTER PIPE ... SET PIPE_EXECUTION_TERMINATE = true) before recreation.
- C. CREATE OR REPLACE PIPE command will recreate the PIPE successfully.
- D. Force the pipe to resume (using SYSTEM$PIPE_FORCE_RESUME).
Answer: A
Explanation:
Explanation
Recreating a pipe (using a CREATE OR REPLACE PIPE statement) is necessary to modify most pipe properties.
Recreating Pipes for Automated Data Loads
When recreating a pipe that automates data loads using event notifications, it's recommended that Data Engineer complete the following steps:
1. Pause the pipe (using ALTER PIPE ... SET PIPE_EXECUTION_PAUSED = true).
2. Query the SYSTEM$PIPE_STATUS function and verify that the pipe execution state is PAUSED.
3. Recreate the pipe (using CREATE OR REPLACE PIPE).
4. Pause the pipe again.
5. Review the configuration steps for your cloud messaging service to ensure the settings are still accurate.
6. Query the SYSTEM$PIPE_STATUS function again and verify that the pipe execution state is RUNNING.
NEW QUESTION # 150
A company wants to ingest streaming data into an Amazon Redshift data warehouse from an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster. A data engineer needs to develop a solution that provides low data access time and that optimizes storage costs.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Create an external schema that maps to the MSK cluster. Create a materialized view that references the external schema to consume the streaming data from the MSK topic.
- B. Develop an AWS Glue streaming extract, transform, and load (ETL) job to process the incoming data from Amazon MSK. Load the data into Amazon S3. Use Amazon Redshift Spectrum to read the data from Amazon S3.
- C. Create an Amazon S3 bucket. Ingest the data from Amazon MSK. Create an event-driven AWS Lambda function to load the data from the S3 bucket to a new Amazon Redshift table.
- D. Create an external schema that maps to the streaming data source. Create a new Amazon Redshift table that references the external schema.
Answer: B
Explanation:
By using a serverless Glue streaming job to continuously pull your MSK records, transform them as needed, and land them in Parquet (or another columnar) files in S3, you:
1. Optimize storage costs ?your data sits in S3, where you pay pennies per GB-month and can tier it further with lifecycle rules.
2. Get low-latency access ?Redshift Spectrum lets you query S3-backed tables with millisecond planning time, so freshly landed data becomes queryable almost immediately.
3. Minimize ops overhead ?you don't have to stand up or manage any EC2-based brokers, Lambda polling loops, or custom connector infrastructure.
Glue's managed streaming runtime handles checkpointing, autoscaling, and fault tolerance for you.
Once the data lands in S3, you simply define an external schema in Redshift that points at the Glue Data Catalog database where your streaming job writes tables. Analysts can then query the
"live" dataset via Spectrum as if it were inside Redshift, meeting both your performance and cost goals with minimal operational effort.
NEW QUESTION # 151
When created, a stream logically takes an initial snapshot of every row in the source object and the contents of a stream change as DML statements execute on the source table.
A Data Engineer, Sophie Created a view that queries the table and returns the CURRENT_USER and CURRENT_TIMESTAMP values for the query transaction. A Stream has been created on views to capture CDC.
Tony, another user inserted the data e.g.
insert into <table> values (1),(2),(3);
Emily, another user also inserted the data e.g.
insert into <table> values (4),(5),(6);
What will happened when Different user queries the same stream after 1 hour?
- A. All the 6 records would be shown with METADATA$ACTION as 'INSERT' out of which 3 records would be displayed with username 'Tony' & rest 3 records would be displayed with username 'Emily'.
- B. All the Six records would be displayed with User 'Sohpie' Who is the owner of the View.
- C. All the Six Records would be displayed with CURRENT_USER & CUR-RENT_TIMESTAMP while querying Streams.
- D. User would be displayed with the one who queried during the session, but Recorded timestamp would be of past 1 hour i.e. actual records insertion time.
Answer: C
Explanation:
Explanation
When User queries the stream, the stream returns the username for the user. The stream also returns the current timestamp for the query transaction in each row, NOT the timestamp when each row was inserted.
NEW QUESTION # 152
A data engineer is processing and analyzing multiple terabytes of raw data that is in Amazon S3.
The data engineer needs to clean and prepare the data. Then the data engineer needs to load the data into Amazon Redshift for analytics.
The data engineer needs a solution that will give data analysts the ability to perform complex queries. The solution must eliminate the need to perform complex extract, transform, and load (ETL) processes or to manage infrastructure.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use Amazon EMR to prepare the data. Use AWS Step Functions to load the data into Amazon Redshift. Use Amazon QuickSight to run queries.
- B. Use AWS Glue to prepare the data. Use AWS Database Migration Service (AVVS DMS) to load the data into Amazon Redshift. Use Amazon Redshift Spectrum to run queries.
- C. Use AWS Glue DataBrew to prepare the data. Use AWS Glue to load the data into Amazon Redshift. Use Amazon Redshift to run queries.
- D. Use AWS Lambda to prepare the data. Use Amazon Kinesis Data Firehose to load the data into Amazon Redshift. Use Amazon Athena to run queries.
Answer: C
NEW QUESTION # 153
A company is building a new application that ingests CSV files into Amazon Redshift. The company has developed the frontend for the application.
The files are stored in an Amazon S3 bucket. Files are no larger than 5 MB.
A data engineer is developing the extract, transform, and load (ETL) pipeline for the CSV files.
The data engineer configured a Redshift cluster and an AWS Lambda function that copies the data out of the files into the Redshift cluster.
Which additional steps should the data engineer perform to meet these requirements?
- A. Configure the $3 bucket to send S3 event notifications to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the Lambda function to process the queue.
- B. Configure the bucket to send S3 event notifications to Amazon EventBridge. Configure an EventBridge rule that matches S3 new object created events. Set the Lambda function as the target.
- C. Configure AWS Database Migration Service (AWS DMS) to stream new S3 objects to a data stream in Amazon Kinesis Data Streams. Set the Lambda function as the target of the data stream.
- D. Configure an Amazon EventBridge rule that matches S3 new object created events. Set an Amazon Simple Queue Service (Amazon SQS) queue as the target of the rule. Configure the Lambda function to process the queue.
Answer: B
Explanation:
By sending S3 "Object Created" events to EventBridge and matching those events with a rule that invokes your Lambda function, you trigger your ETL whenever a new CSV lands in S3, without extra polling or queue management. This direct, event-driven pattern keeps operational overhead to a minimum.
NEW QUESTION # 154
A company has several new datasets in CSV and JSON formats. A data engineer needs to make the data available to a team of data analysts who will analyze the data by using SQL queries.
Which solution will meet these requirements in the MOST cost-effective way?
- A. Store the data in an Amazon S3 bucket. Use an AWS Glue crawler to catalog the S3 bucket as tables. Create an Amazon Athena workgroup that has a data usage threshold. Grant the data analysts access to the Athena workgroup.
- B. Create an AWS Glue DataBrew project that contains the new data. Make the DataBrew project available to the data analysts.
- C. Load the data into Super-fast, Parallel, In-memory Calculation Engine (SPICE) in Amazon QuickSight. Allow the data analysts to create analyses and dashboards in QuickSight.
- D. Create an Amazon RDS MySQL cluster. Use AWS Glue to transform and load the CSV and JSON files into database tables. Provide the data analysts access to the MySQL cluster.
Answer: A
Explanation:
By storing the CSV and JSON files in Amazon S3 and running an AWS Glue crawler, you automatically catalog them as tables without upfront ETL.
Amazon Athena then lets analysts run SQL queries directly against those tables on a pay-per- query basis. Using an Athena workgroup with a usage threshold keeps costs under control, making this the most cost-effective, low-operational-overhead solution.
NEW QUESTION # 155
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. SHOW FUNCTIONS Commands
- B. He can get the secure UDF definition using GET_DDL utility function.
- C. UDF definition or text, is visible to users via Query Profile (in the web interface).
- D. Declaring a UDF as "secure" hide the definition from Bob & all the required Definition commands will throw error.
Answer: D
NEW QUESTION # 156
Which are the two ways to access elements in a JSON object?
- A. Use SemiColon notation to traverse a path in a JSON object:
<col-umn>:<level1_element>;<level2_element>;<level3_element>. - B. Use dot notation to traverse a path in a JSON object:
<col-umn>:<level1_element>.<level2_element>.<level3_element>. - C. use Curly bracket notation to traverse the path in an object:
<col-umn>{'<level1_element>'}{'<level2_element>'}. - D. use bracket notation to traverse the path in an object:
<col-umn>['<level1_element>']['<level2_element>'].
Answer: B,D
NEW QUESTION # 157
What is a characteristic of the use of external tokenization?
- A. External tokenization cannot be used with database replication
- B. Secure data sharing can be used with external tokenization
- C. External tokenization allows (he preservation of analytical values after de-identification
- D. Pre-loading of unmasked data is supported with external tokenization
Answer: C
Explanation:
Explanation
External tokenization is a feature in Snowflake that allows users to replace sensitive data values with tokens that are generated and managed by an external service. External tokenization allows the preservation of analytical values after de-identification, such as preserving the format, length, or range of the original values.
This way, users can perform analytics on the tokenized data without compromising the security or privacy of the sensitive data.
NEW QUESTION # 158
A company is building an inventory management system and an inventory reordering system to automatically reorder products. Both systems use Amazon Kinesis Data Streams. The inventory management system uses the Amazon Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the Amazon Kinesis Client Library (KCL) to consume data from the stream. The company configures the stream to scale up and down as needed.
Before the company deploys the systems to production, the company discovers that the inventory reordering system received duplicated data.
Which factors could have caused the reordering system to receive duplicated data? (Choose two.)
- A. The producer experienced network-related timeouts.
- B. The max_records configuration property was set to a number that was too high.
- C. The AggregationEnabled configuration property was set to true.
- D. The stream's value for the IteratorAgeMilliseconds metric was too high.
- E. There was a change in the number of shards, record processors, or both.
Answer: A,E
NEW QUESTION # 159
A Data Engineer executes a complex query and wants to make use of Snowflake s query results caching capabilities to reuse the results.
Which conditions must be met? (Select THREE).
- A. The results must be reused within 72 hours.
- B. The new query must have the same syntax as the previously executed query.
- C. The query must be executed using the same virtual warehouse.
- D. The USED_CACHED_RESULT parameter must be included in the query.
- E. The table structure contributing to the query result cannot have changed
- F. The micro-partitions cannot have changed due to changes to other data in the table
Answer: A,B,E
Explanation:
Explanation
Snowflake's query results caching capabilities allow users to reuse the results of previously executed queries without re-executing them. For this to happen, the following conditions must be met:
The results must be reused within 24 hours (not 72 hours), which is the default time-to-live (TTL) for cached results.
The query must be executed using any virtual warehouse (not necessarily the same one), as long as it is in the same region and account as the original query.
The USED_CACHED_RESULT parameter does not need to be included in the query, as it is enabled by default at the account level. However, it can be disabled or overridden at the session or statement level.
The table structure contributing to the query result cannot have changed, such as adding or dropping columns, changing data types, or altering constraints.
The new query must have the same syntax as the previously executed query, including whitespace and case sensitivity.
The micro-partitions cannot have changed due to changes to other data in the table, such as inserting, updating, deleting, or merging rows.
NEW QUESTION # 160
Within a Snowflake account permissions have been defined with custom roles and role hierarchies.
To set up column-level masking using a role in the hierarchy of the current user, what command would be used?
- A. IS_RCLE_IN_SESSION
- B. IKVOKER_ROLE
- C. CORRECT_ROLE
- D. IS_GRANTED_TO_INVOKER_ROLE
Answer: A
Explanation:
Explanation
The IS_ROLE_IN_SESSION function is used to set up column-level masking using a role in the hierarchy of the current user. Column-level masking is a feature in Snowflake that allows users to apply dynamic data masking policies to specific columns based on the roles of the users who access them. The IS_ROLE_IN_SESSION function takes a role name as an argument and returns true if the role is in the current user's session, or false otherwise. The function can be used in a masking policy expression to determine whether to mask or unmask a column value based on the role of the user. For example:
CREATE OR REPLACE MASKING POLICY email_mask AS (val string) RETURNS string -> CASE WHEN IS_ROLE_IN_SESSION('HR') THEN val ELSE REGEXP_REPLACE(val, '(.).(.@.)', '\1****\2') END; In this example, the IS_ROLE_IN_SESSION function is used to create a masking policy for an email column.
The masking policy returns the original email value if the user has the HR role in their session, or returns a masked email value with asterisks if not.
NEW QUESTION # 161
In data modeling, an entity-relationship diagram (ERD) is primarily used to:
- A. Cleanse dirty data.
- B. Transform raw data into structured data.
- C. Store data in a physical location.
- D. Illustrate relationships between entities.
Answer: D
NEW QUESTION # 162
A data engineer wants to orchestrate a set of extract, transform, and load (ETL) jobs that run on AWS. The ETL jobs contain tasks that must run Apache Spark jobs on Amazon EMR, make API calls to Salesforce, and load data into Amazon Redshift.
The ETL jobs need to handle failures and retries automatically. The data engineer needs to use Python to orchestrate the jobs.
Which service will meet these requirements?
- A. Amazon EventBridge
- B. AWS Glue
- C. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)
- D. AWS Step Functions
Answer: C
Explanation:
Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is a fully managed service that makes it easy to run Apache Airflow at scale. Airflow is a popular tool for orchestrating complex workflows, including ETL jobs, and can handle various task types such as running Apache Spark jobs on Amazon EMR, making API calls to external systems like Salesforce, and loading data into Amazon Redshift. Airflow allows you to define workflows using Python and offers built-in mechanisms for handling failures, retries, and retries automatically, making it a great fit for this use case.
While AWS Step Functions can orchestrate workflows and handle retries and errors, it is less suited for Python-based orchestration compared to Airflow. Also, Step Functions is more suited for AWS services and does not offer built-in support for external systems like Salesforce, which would require custom integrations.
AWS Glue is a serverless ETL service, but it primarily focuses on data transformation tasks. Glue does not handle orchestration of complex workflows involving non-Glue jobs, such as calling Salesforce APIs or running Spark jobs on EMR, as effectively as Airflow.
EventBridge is an event-driven service, not an orchestration service. It is designed for event- based workflows but does not offer the workflow orchestration capabilities needed for handling tasks like retries, error handling, and running complex workflows across different systems.
NEW QUESTION # 163
Select the incorrect statements regarding Clustering depth?
- A. The clustering depth for a populated table measures the average depth (1 or greater) of the overlapping micro-partitions for specified columns in a table. The smaller the aver-age depth, the better clustered the table is with regards to the specified columns.
- B. It helps Monitoring the clustering "health" of a large table, particularly over time as DML is performed on the table.
- C. A table with no micro-partitions (i.e. an unpopulated/empty table) has a clustering depth of 1.
(Correct) - D. Clustering depth can be used for determining whether a large table would benefit from explicitly defining a clustering key.
Answer: C
Explanation:
Explanation
A table with no micro-partitions (i.e. an unpopulated/empty table) has a clustering depth of 0.
NEW QUESTION # 164
Ryan, a Data Engineer, wants to improve the performance of large, complex queries against large data sets. He decided to Scale up underlying warehouse/cluster. What is correct Snowflake consid-eration while scaling up so that he can achieve better performance results? [Select all that apply]
- A. Resizing a running warehouse does not impact queries that are already being processed by the warehouse; the additional compute resources, once fully provisioned, are only used for queued and new queries.
- B. Scaling up is not intended for handling concurrency issues; instead, use additional warehouses to handle the workload or use a multi-cluster warehouse (if this feature is available for your account).
- C. Resizing between a 5XL or 6XL warehouse to a 4XL or smaller warehouse results in a brief period during which the customer is charged for both the new warehouse and the old warehouse while the old warehouse is quiesced.
- D. Snowflake supports resizing a warehouse at any time, even while running.
- E. Resizing can help reduce the queuing that occurs if a warehouse does not have enough compute resources to process all the queries that are submitted concurrently.
Answer: A,B,C,D,E
Explanation:
Explanation
Resizing a warehouse generally improves query performance, particularly for larger, more complex queries. It can also help reduce the queuing that occurs if a warehouse does not have enough com-pute resources to process all the queries that are submitted concurrently. Note that warehouse resiz-ing is not intended for handling concurrency issues; instead, use additional warehouses to handle the workload or use a multi-cluster warehouse (if this feature is available for your account).
Snowflake supports resizing a warehouse at any time, even while running. If a query is running slowly and you have additional queries of similar size and complexity that you want to run on the same warehouse, you might choose to resize the warehouse while it is running; however, note the following:
Larger warehouse size is not necessarily faster; for smaller, basic queries that are already executing quickly, you may not see any significant improvement after resizing.
Resizing a running warehouse does not impact queries that are already being processed by the warehouse; the additional compute resources, once fully provisioned, are only used for queued and new queries.
Resizing between a 5XL or 6XL warehouse to a 4XL or smaller warehouse results in a brief period during which the customer is charged for both the new warehouse and the old warehouse while the old warehouse is quiesced.
NEW QUESTION # 165
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. 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.
- 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. 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.
- D. Create a PySpark program in AWS Lambda to extract, transform, and load the data into the S3 bucket.
Answer: B
NEW QUESTION # 166
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