使用優秀的DEA-C01熱門考題確保您通過您的Snowflake DEA-C01考試

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Snowflake DEA-C01 考試大綱:
| 主題 | 簡介 |
|---|
| 主題 1 | - Performance Optimization: This topic assesses the ability to optimize and troubleshoot underperforming queries in Snowflake. Candidates must demonstrate knowledge in configuring optimal solutions, utilizing caching, and monitoring data pipelines. It focuses on ensuring engineers can enhance performance based on specific scenarios, crucial for Snowflake Data Engineers and Software Engineers.
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| 主題 2 | - Data Movement: Snowflake Data Engineers and Software Engineers are assessed on their proficiency to load, ingest, and troubleshoot data in Snowflake. It evaluates skills in building continuous data pipelines, configuring connectors, and designing data sharing solutions.
|
| 主題 3 | - Storage and Data Protection: The topic tests the implementation of data recovery features and the understanding of Snowflake's Time Travel and micro-partitions. Engineers are evaluated on their ability to create new environments through cloning and ensure data protection, highlighting essential skills for maintaining Snowflake data integrity and accessibility.
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| 主題 4 | - Security: The Security topic of the DEA-C01 test covers the principles of Snowflake security, including the management of system roles and data governance. It measures the ability to secure data and ensure compliance with policies, crucial for maintaining secure data environments for Snowflake Data Engineers and Software Engineers.
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| 主題 5 | - Data Transformation: The SnowPro Advanced: Data Engineer exam evaluates skills in using User-Defined Functions (UDFs), external functions, and stored procedures. It assesses the ability to handle semi-structured data and utilize Snowpark for transformations. This section ensures Snowflake engineers can effectively transform data within Snowflake environments, critical for data manipulation tasks.
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>> DEA-C01熱門考題 <<
最真實的DEA-C01認證考試的真實考古題匯總
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最新的 SnowPro Advanced DEA-C01 免費考試真題 (Q191-Q196):
問題 #191
If the data retention period for a table is less than 90 days, and a stream has not been consumed, Snowflake temporarily extends this period to prevent it from going stale?
答案:A
解題說明:
Explanation
If the data retention period for a table is less than 14 days, and a stream has not been consumed, Snowflake temporarily extends this period to prevent it from going stale. The period is extended to the stream's offset, up to a maximum of 14 days by default, regardless of the Snowflake edition for your account. The maximum number of days for which Snowflake can extend the data retention period is determined by the MAX_DATA_EXTENSION_TIME_IN_DAYS parameter value. When the stream is consumed, the extended data retention period is reduced to the default period for the table.
問題 #192
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 AWS Lambda to prepare the data. Use Amazon Kinesis Data Firehose to load the data into Amazon Redshift. Use Amazon Athena to run queries.
- B. Use Amazon EMR to prepare the data. Use AWS Step Functions to load the data into Amazon Redshift. Use Amazon QuickSight 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 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.
答案:C
問題 #193
A data engineer is launching an Amazon EMR cluster. The data that the data engineer needs to load into the new cluster is currently in an Amazon S3 bucket. The data engineer needs to ensure that data is encrypted both at rest and in transit. The data that is in the S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The data engineer has an Amazon S3 path that has a Privacy Enhanced Mail (PEM) file.
Which solution will meet these requirements?
- A. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption.
Use the security configuration during EMR cluster creation. - B. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Create a second security configuration. Specify the Amazon S3 path of the PEM file for in-transit encryption. Create the EMR cluster, and attach both security configurations to the cluster.
- C. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption.
Create the EMR cluster, and attach the security configuration to the cluster. - D. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for local disk encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Use the security configuration during EMR cluster creation.
答案:A
解題說明:
Amazon EMR security configurations allow you to define encryption settings for both at-rest and in-transit data. In this case, the data in the S3 bucket is already encrypted with an AWS KMS key, so you need to ensure the EMR cluster uses the same key for at-rest encryption when accessing the S3 bucket.
For in-transit encryption, you need to provide a PEM file that contains the SSL/TLS certificate, which ensures that data transferred to and from the cluster is encrypted.
By specifying both the KMS key for at-rest encryption and the PEM file for in-transit encryption in a single security configuration, you ensure that the data is encrypted during storage and transmission.
問題 #194
What is the purpose of the BUILD_FILE_URL function in Snowflake?
- A. It generates a temporary URL for accessing a file in a stage.
- B. It generates a permanent URL for accessing files in a stage.
- C. It generates an encrypted URL foe accessing a file in a stage.
- D. It generates a staged URL for accessing a file in a stage.
答案:D
解題說明:
Explanation
The BUILD_FILE_URL function in Snowflake generates a temporary URL for accessing a file in a stage. The function takes two arguments: the stage name and the file path. The generated URL is valid for 24 hours and can be used to download or view the file contents. The other options are incorrect because they do not describe the purpose of the BUILD_FILE_URL function.
問題 #195
Elon, a Data Engineer, needs to Split Semi-structured Elements from the Source files and load them as an array into Separate Columns.
Source File:
1.+----------------------------------------------------------------------+
2.| $1 |
3.|----------------------------------------------------------------------|
4.| {"mac_address": {"host1": "197.128.1.1","host2": "197.168.0.1"}}, |
5.| {"mac_address": {"host1": "197.168.2.1","host2": "197.168.3.1"}} |
6.+----------------------------------------------------------------------+ Output: Splitting the Machine Address as below.
1.COL1 | COL2 |
2.|----------+----------|
3.| [ | [ |
4.| "197", | "197", |
5.| "128", | "168", |
6.| "1", | "0", |
7.| "1" | "1" |
8.| ] | ] |
9.| [ | [ |
10.| "197", | "197", |
11.| "168", | "168", |
12.| "2", | "3", |
13.| "1" | "1" |
14.| ] | ]
Which SnowFlake Function can Elon use to transform this semi structured data in the output for-mat?
- A. NEST
- B. SPLIT
- C. GROUP_BY_CONNECT
- D. CONVERT_TO_ARRAY
答案:B
問題 #196
......
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