Get Real DEA-C01 Exam Dumps [Nov-2024] Practice Tests
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Snowflake DEA-C01 Exam Syllabus Topics:
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NEW QUESTION # 10
A company is using an AWS Transfer Family server to migrate data from an on-premises environment to AWS. Company policy mandates the use of TLS 1.2 or above to encrypt the data in transit.
Which solution will meet these requirements?
- A. Update the security policy of the Transfer Family server to specify a minimum protocol version of TLS 1.2
- B. Generate new SSH keys for the Transfer Family server. Make the old keys and the new keys available for use.
- C. Update the security group rules for the on-premises network to allow only connections that use TLS 1.2 or above.
- D. Install an SSL certificate on the Transfer Family server to encrypt data transfers by using TLS 1.2.
Answer: A
Explanation:
https://docs.aws.amazon.com/transfer/latest/userguide/security-policies.html
NEW QUESTION # 11
Tasks may optionally use table streams to provide a convenient way to continuously process new or changed data. A task can transform new or changed rows that a stream surfaces. Each time a task is scheduled to run, it can verify whether a stream contains change data for a table and either consume the change data or skip the current run if no change data exists. Which System Function can be used by Data engineer to verify whether a stream contains changed data for a table?
- A. SYSTEM$STREAM_DELTA_DATA
- B. SYSTEM$STREAM_CDC_DATA
- C. SYSTEM$STREAM_HAS_DATA
- D. SYSTEM$STREAM_HAS_CHANGE_DATA
Answer: C
Explanation:
Explanation
SYSTEM$STREAM_HAS_DATA
Indicates whether a specified stream contains change data capture (CDC) records.
NEW QUESTION # 12
A Data Engineer wants to create a new development database (DEV) as a clone of the permanent production database (PROD) There is a requirement to disable Fail-safe for all tables.
Which command will meet these requirements?
- A. CREATE TRANSIENT DATABASE DEV
CLONE RPOD - B. CREATE DATABASE DEV
CLOSE PROD
DATA_RETENTION_TIME_IN_DAYS =0L - C. CREATE DATABASE DEV
CLONE PROD; - D. CREATE DATABASE DEV
CLONE PROD
FAIL_SAFE=FALSE;
Answer: A
Explanation:
Explanation
This option will meet the requirements of creating a new development database (DEV) as a clone of the permanent production database (PROD) and disabling Fail-safe for all tables. By using the CREATE TRANSIENT DATABASE command, the Data Engineer can create a transient database that does not have Fail-safe enabled by default. Fail-safe is a feature in Snowflake that provides additional protection against data loss by retaining historical data for seven days beyond the time travel retention period. Transient databases do not have Fail-safe enabled, which means that they do not incur additional storage costs for historical data beyond their time travel retention period. By using the CLONE option, the Data Engineer can create an exact copy of the PROD database, including its schemas, tables, views, and other objects.
NEW QUESTION # 13
During a security review, a company identified a vulnerability in an AWS Glue job. The company discovered that credentials to access an Amazon Redshift cluster were hard coded in the job script.
A data engineer must remediate the security vulnerability in the AWS Glue job. The solution must securely store the credentials.
Which combination of steps should the data engineer take to meet these requirements? (Choose two.)
- A. Grant the AWS Glue job IAM role access to the stored credentials.
- B. Store the credentials in a configuration file that is in an Amazon S3 bucket.
- C. Store the credentials in AWS Secrets Manager.
- D. Store the credentials in the AWS Glue job parameters.
- E. Access the credentials from a configuration file that is in an Amazon S3 bucket by using the AWS Glue job.
Answer: A,C
NEW QUESTION # 14
A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records.
A data engineer needs to find a solution to process the streaming data. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Load the data into Amazon S3. Use the COPY command to load the data into Amazon Redshift.
- B. Load data into Amazon Kinesis Data Firehose. Load the data into Amazon Redshift.
- C. Use the Amazon Aurora zero-ETL integration with Amazon Redshift.
- D. Use the streaming ingestion feature of Amazon Redshift.
Answer: D
Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-streaming-ingestion.html Use the Streaming Ingestion Feature of Amazon Redshift: Amazon Redshift recently introduced streaming data ingestion, allowing Redshift to consume data directly from Kinesis Data Streams in near real-time. This feature simplifies the architecture by eliminating the need for intermediate steps or services, and it is specifically designed to support near real-time analytics. The operational overhead is minimal since the feature is integrated within Redshift.
NEW QUESTION # 15
Melissa, Senior Data Engineer, looking out to optimize query performance for one of the Critical Control Dashboard, she found that most of the searches by the users on the control dashboards are based on Equality search on all the underlying columns mostly. Which Best techniques she should consider here?
- A. The search optimization service would best fit here as it can be applied to all underlying columns & speeds up equality searches.
(Correct) - B. Melissa can create Indexes & Hints on the searchable columns to speed up Equality search.
- C. She can go for clustering on underlying tables which can speedup Equality searches.
- D. A materialized view speeds both equality searches and range searches.
Answer: A
Explanation:
Explanation
Clustering a table can speed any of the following, as long as they are on the clustering key:
Range searches.
Equality searches.
However, a table can be clustered on only a single key (which can contain one or more columns or expressions).
The search optimization service speeds equality searches. However, this applies to all the columns of supported types in a table that has search optimization enabled. This is what required here& best fit for purpose.
A materialized view speeds both equality searches and range searches, as well as some sort opera-tions, but only for the subset of rows and columns included in the materialized view.
NEW QUESTION # 16
Stuart, a Lead Data Engineer in MACRO Data Company created streams on set of External tables. He has been asked to extend the data retention period of the stream for 90 days, which parameter he can utilize to enable this extension?
- A. DATA_RETENTION_TIME_IN_DAYS
- B. MAX_DATA_EXTENSION_TIME_IN_DAYS
- C. None of the above
- D. DATA_EXTENSION_TIME_IN_DAYS
Answer: C
Explanation:
Explanation
External tables do not have data retention period applicable.
Good to Understand other Options available.
DATA_RETENTION_TIME_IN_DAYS
Type: Object (for databases, schemas, and tables) - Can be set for Account - Database - Schema - Table Description: Number of days for which Snowflake retains historical data for performing Time Trav-el actions (SELECT, CLONE, UNDROP) on the object. A value of 0 effectively disables Time Travel for the specified database, schema, or table.
Values:
0 or 1 (for Standard Edition)
0 to 90 (for Enterprise Edition or higher)
Default:
1
MAX_DATA_EXTENSION_TIME_IN_DAYS
Type: Object (for databases, schemas, and tables) - Can be set for Account - Database - Schema - Table Description: Maximum number of days for which Snowflake can extend the data retention period for tables to prevent streams on the tables from becoming stale. By default, if the DA-TA_RETENTION_TIME_IN_DAYS setting for a source table is less than 14 days, and a stream has not been consumed, Snowflake temporarily extends this period to the stream's offset, up to a maximum of 14 days, regardless of the Snowflake Edition for your account. The MAX_DATA_EXTENSION_TIME_IN_DAYS parameter enables you to limit this automatic ex-tension period to control storage costs for data retention or for compliance reasons.
This parameter can be set at the account, database, schema, and table levels. Note that setting the parameter at the account or schema level only affects tables for which the parameter has not already been explicitly set at a lower level (e.g. at the table level by the table owner). A value of 0 effective-ly disables the automatic extension for the specified database, schema, or table.
Values:
0 to 90 (i.e. 90 days) - a value of 0 disables the automatic extension of the data retention period. To increase the maximum value for tables in your account, Client needs to contact Snowflake Sup-port.
Default:14
NEW QUESTION # 17
A company is using Snowpipe to bring in millions of rows every day of Change Data Capture (CDC) into a Snowflake staging table on a real-time basis The CDC needs to get processedand combined with other data in Snowflake and land in a final table as part of the full data pipeline.
How can a Data engineer MOST efficiently process the incoming CDC on an ongoing basis?
- A. Create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data.
- B. Schedule a task that dynamically retrieves the last time the task was run from information_schema-rask_hiSwOry and use that timestamp to process the delta of the new rows since the last time the task was run.
- C. Transform the data during the data load with Snowpipe by modifying the related copy into statement to include transformation steps such as case statements andJOIN'S.
- D. Use a create ok replace table as statement that references the staging table and includes all the transformation SQL. Use a task to run the full create or replace table as statement on a scheduled basis
Answer: A
Explanation:
Explanation
The most efficient way to process the incoming CDC on an ongoing basis is to create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data. A stream is a Snowflake object that records changes made to a table, such as inserts, updates, or deletes. A stream can be queried like a table and can provide information about what rows have changed since the last time the stream was consumed. A task is a Snowflake object that can execute SQL statements on a schedule without requiring a warehouse. A task can be configured to run only when certain conditions are met, such as when a stream has data or when another task has completed successfully. By creating a stream on the staging table and scheduling a task that transforms data from the stream, the Data Engineer can ensure that only new or modified rows are processed and that no unnecessary computations are performed.
NEW QUESTION # 18
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$PIPE_STATUS function and verify that the pipe execution state is RUN-NING.
Which are the Missing recommended steps while Recreating Pipes for Automated Data Loads?
- A. Force the pipe to resume (using SYSTEM$PIPE_FORCE_RESUME).
- B. 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).
- C. CREATE OR REPLACE PIPE command will recreate the PIPE successfully.
- D. Terminate the existing pipe (using ALTER PIPE ... SET PIPE_EXECUTION_TERMINATE = true) before recreation.
Answer: B
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 # 19
Which query will show a list of the 20 most recent executions of a specified task kttask, that have been scheduled within the last hour that have ended or are stillrunning's.
- A.
- B.
- C.
- D.
Answer: C
NEW QUESTION # 20
Streams record the differences between two offsets. If a row is added and then updated in the cur-rent offset, what will be the value of METADATA$ISUPDATE Columns in this scenario?
- A. FALSE
- B. INSERT
- C. UPDATE
- D. TRUE
Answer: A
Explanation:
Explanation
Stream Columns
A stream stores an offset for the source object and not any actual table columns or data. When que-ried, a stream accesses and returns the historic data in the same shape as the source object (i.e. the same column names and ordering) with the following additional columns:
METADATA$ACTION
Indicates the DML operation (INSERT, DELETE) recorded.
METADATA$ISUPDATE
Indicates whether the operation was part of an UPDATE statement. Updates to rows in the source object are represented as a pair of DELETE and INSERT records in the stream with a metadata column METADATA$ISUPDATE values set to TRUE.
METADATA$ROW_ID
Specifies the unique and immutable ID for the row, which can be used to track changes to specific rows over time.
Note that streams record the differences between two offsets. If a row is added and then updated in the current offset, the delta change is a new row. The METADATA$ISUPDATE row records a FALSE value.
NEW QUESTION # 21
A data engineer creates an AWS Lambda function that an Amazon EventBridge event will invoke.
When the data engineer tries to invoke the Lambda function by using an EventBridge event, an AccessDeniedException message appears.
How should the data engineer resolve the exception?
- A. Ensure that both the IAM role that EventBridge uses and the Lambda function's resource-based policy have the necessary permissions.
- B. Ensure that EventBridge schemas are valid and that the event mapping configuration is correct.
- C. Ensure that the trust policy of the Lambda function execution role allows EventBridge to assume the execution role.
- D. Ensure that the subnet where the Lambda function is deployed is configured to be a private subnet.
Answer: A
Explanation:
The lambda resource based policy must allow the events principle to invoke the lambda function.
Amazon SQS, Amazon SNS, Lambda, CloudWatch Logs, and EventBridge bus targets do not use roles, and permissions to EventBridge must be granted via a resource policy.
https://docs.aws.amazon.com/eventbridge/latest/userguide/eb-run-lambda-schedule.html#eb- schedule-create-rule
https://docs.aws.amazon.com/eventbridge/latest/userguide/eb-run-lambda-schedule.html#eb- schedule-create-rule
NEW QUESTION # 22
A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data contained some duplicate information.
The data engineer must identify and remove duplicate information from the legacy application data.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Write an AWS Glue extract, transform, and load (ETL) job. Import the Python dedupe library. Use the dedupe library to perform data deduplication.
- B. Write an AWS Glue extract, transform, and load (ETL) job. Use the FindMatches machine learning (ML) transform to transform the data to perform data deduplication.
- C. Write a custom extract, transform, and load (ETL) job in Python. Import the Python dedupe library. Use the dedupe library to perform data deduplication.
- D. Write a custom extract, transform, and load (ETL) job in Python. Use the DataFrame.drop_duplicates() function by importing the Pandas library to perform data deduplication.
Answer: B
NEW QUESTION # 23
To support Time Travel, Which of the following SQL extensions/parameters/commands have been implemented?
- A. STATEMENT_ID (identifier for statement, e.g. query ID)
- B. STATEMENT (identifier for statement, e.g. query ID)
- C. ONSET (time difference in seconds from the present time)
- D. AT | BEFORE clause which can be specified in the CREATE ... CLONE commands.
- E. OFFSET (time difference in seconds from the present time)
- F. UNDROP command for tables, schemas, and databases.
Answer: B,D,E,F
NEW QUESTION # 24
A data engineer must build an extract, transform, and load (ETL) pipeline to process and load data from 10 source systems into 10 tables that are in an Amazon Redshift database. All the source systems generate .csv, JSON, or Apache Parquet files every 15 minutes. The source systems all deliver files into one Amazon S3 bucket. The file sizes range from 10 MB to 20 GB.
The ETL pipeline must function correctly despite changes to the data schema.
Which data pipeline solutions will meet these requirements? (Choose two.)
- A. Configure an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket. Configure an AWS Glue job to process and load the data into the Amazon Redshift tables. Create a second Lambda function to run the AWS Glue job. Create an Amazon EventBridge rule to invoke the second Lambda function when the AWS Glue crawler finishes running successfully.
- B. Use an Amazon EventBridge rule to invoke an AWS Glue workflow job every 15 minutes.
Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables. - C. Configure an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket. Configure the AWS Glue job to read the files from the S3 bucket into an Apache Spark DataFrame. Configure the AWS Glue job to also put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to load data into the Amazon Redshift tables.
- D. Configure an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
- E. Use an Amazon EventBridge rule to run an AWS Glue job every 15 minutes. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
Answer: B,D
NEW QUESTION # 25
An online retail company stores Application Load Balancer (ALB) access logs in an Amazon S3 bucket. The company wants to use Amazon Athena to query the logs to analyze traffic patterns.
A data engineer creates an unpartitioned table in Athena. As the amount of the data gradually increases, the response time for queries also increases. The data engineer wants to improve the query performance in Athena.
Which solution will meet these requirements with the LEAST operational effort?
- A. Use Apache Hive to create bucketed tables. Use an AWS Lambda function to transform all ALB access logs.
- B. Create an AWS Lambda function to transform all ALB access logs. Save the results to Amazon S3 in Apache Parquet format. Partition the metadata. Use Athena to query the transformed data.
- C. Create an AWS Glue crawler that includes a classifier that determines the schema of all ALB access logs and writes the partition metadata to AWS Glue Data Catalog.
- D. Create an AWS Glue job that determines the schema of all ALB access logs and writes the partition metadata to AWS Glue Data Catalog.
Answer: C
Explanation:
An AWS Glue crawler can automatically determine the schema of the logs, infer partitions, and update the Glue Data Catalog. Crawlers can be scheduled to run at intervals, minimizing manual intervention.
NEW QUESTION # 26
A company uses an Amazon QuickSight dashboard to monitor usage of one of the company's applications. The company uses AWS Glue jobs to process data for the dashboard. The company stores the data in a single Amazon S3 bucket. The company adds new data every day.
A data engineer discovers that dashboard queries are becoming slower over time. The data engineer determines that the root cause of the slowing queries is long-running AWS Glue jobs.
Which actions should the data engineer take to improve the performance of the AWS Glue jobs?
(Choose two.)
- A. Partition the data that is in the S3 bucket. Organize the data by year, month, and day.
- B. Modify the IAM role that grants access to AWS glue to grant access to all S3 features.
- C. Adjust AWS Glue job scheduling frequency so the jobs run half as many times each day.
- D. Convert the AWS Glue schema to the DynamicFrame schema class.
- E. Increase the AWS Glue instance size by scaling up the worker type.
Answer: A,E
NEW QUESTION # 27
When using the CURRENT_ROLE and CURRENT_USER functions with secure views that will be shared to other Snowflake accounts, Snowflake returns a NULL value for these functions?
- A. FALSE
- B. TRUE
Answer: B
Explanation:
Explanation
When using the CURRENT_ROLE and CURRENT_USER functions with secure views that will be shared to other Snowflake accounts, Snowflake returns a NULL value for these functions. The reason is that the owner of the data being shared does not typically control the users or roles in the account with which the view is being shared.
NEW QUESTION # 28
To view/monitor the clustering metadata for a table, Snowflake provides which of the following system functions?
- A. SYSTEM$CLUSTERING_DEPTH_KEY
- B. SYSTEM$CLUSTERING_KEY_INFORMATION (including clustering depth)
- C. SYSTEM$CLUSTERING_INFORMATION (including clustering depth)
- D. SYSTEM$CLUSTERING_DEPTH
Answer: C,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 # 29
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 # 30
Charles, A Lead Data engineer, with ACCOUNTADMIN role wants to configure the time travel for one of the Schema's object. He setup the MIN_DATA_RETENTION_TIME_IN_DAYS pa-rameter with Value 79 at account level but he figured out that DA-TA_RETENTION_TIME_IN_DAYS is already set with value 81 at account level. What would be the effective minimum data retention period for an object?
- A. 0
- B. There is no such MIN_DATA_RETENTION_TIME_IN_DAYS parameter
- C. 1
- D. 2
Answer: D
Explanation:
Explanation
A user with the ACCOUNTADMIN role can also set the MIN_DATA_RETENTION_TIME_IN_DAYS at the account level. This parameter setting enforc-es a minimum data retention period for databases, schemas, and tables. Setting MIN_DATA_RETENTION_TIME_IN_DAYS does not alter or replace the DA-TA_RETENTION_TIME_IN_DAYS parameter value. It may, however, change the effective data retention period for objects. When MIN_DATA_RETENTION_TIME_IN_DAYS is set at the ac-count level, the data retention period for an object is determined by MAX(DATA_RETENTION_TIME_IN_DAYS, MIN_DATA_RETENTION_TIME_IN_DAYS).
NEW QUESTION # 31
A company needs to set up a data catalog and metadata management for data sources that run in the AWS Cloud. The company will use the data catalog to maintain the metadata of all the objects that are in a set of data stores. The data stores include structured sources such as Amazon RDS and Amazon Redshift. The data stores also include semistructured sources such as JSON files and .xml files that are stored in Amazon S3.
The company needs a solution that will update the data catalog on a regular basis. The solution also must detect changes to the source metadata.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically.
- B. Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog.
- C. Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically.
- D. Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and to update the Data Catalog with metadata changes. Schedule the crawlers to run periodically to update the metadata catalog.
Answer: D
NEW QUESTION # 32
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