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| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Data Engineer Certification Exam |
| Exam Number: | DEA-C01 |
| Related Certifications: | SnowPro Core Certification |
| Exam Format: | Multiple choice, Multiple select, Scenario-based questions |
| Exam Price: | $375 USD |
| Exam Duration: | 115 minutes |
| Available Languages: | English |
| Real Exam Qty: | Approximately 65 questions |
| Passing Score: | Not publicly disclosed (Snowflake uses scaled scoring) |
| Certificate Validity Period: | 2 years |
| Recommended Training: | Snowflake University Training SnowPro Advanced Data Engineer Exam Guide |
| Exam Registration: | Snowflake Certification Portal |
| Sample Questions: | Snowflake DEA-C01 Sample Questions |
| Exam Way: | Online proctored or authorized testing center |
| Pre Condition: | Recommended: SnowPro Core Certification or equivalent Snowflake experience |
| Official Syllabus URL: | https://www.snowflake.com/certifications/snowpro-advanced-data-engineer/ |
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NEW QUESTION # 152
An online retail company has an application that runs on Amazon EC2 instances that are in a VPC. The company wants to collect flow logs for the VPC and analyze network traffic.
Which solution will meet these requirements MOST cost-effectively?
Answer: B
Explanation:
Publishing flow logs to Amazon S3 in Apache Parquet format and using Amazon Athena for analytics (D) is the most cost-effective solution. This approach minimizes storage costs due to the efficient compression of Parquet, and optimizes query performance and cost in Athena due to the reduced data size and optimized columnar storage.
NEW QUESTION # 153
A gaming company uses Amazon Kinesis Data Streams to collect clickstream data. The company uses Amazon Data Firehose delivery streams to store the data in JSON format in Amazon S3.
Data scientists at the company use Amazon Athena to query the most recent data to obtain business insights.
The company wants to reduce Athena costs but does not want to recreate the data pipeline.
Which solution will meet these requirements with the LEAST management effort?
Answer: C
Explanation:
Changing the Firehose output format to Apache Parquet reduces Amazon Athena query costs because Parquet is a columnar storage format, which is much more efficient for queries compared to row-based formats like JSON. Parquet helps reduce the amount of data scanned by Athena, thus lowering costs.
Configuring Firehose to use a large buffer size ensures fewer, larger files, which also improves query performance in Athena.
For the existing JSON data, using an AWS Glue ETL job to convert the files into Parquet and apply the necessary partitioning (e.g., YYYYMMDD) will help optimize future queries without needing to recreate the data pipeline. Using the ALTER TABLE ADD PARTITION statement in Athena will allow the existing table to reflect the new partitions.
This solution minimizes management effort by reconfiguring Amazon Kinesis Data Firehose and using AWS Glue for handling existing data, without requiring significant changes to the existing pipeline.
Running an Apache Spark job on Amazon EMR requires more operational management, as you would need to launch and manage ephemeral EMR clusters daily, which increases complexity.
Amazon Managed Service for Apache Flink introduces additional components (Kinesis Data Streams and Flink), which adds complexity and management overhead compared to using the existing Firehose and simply changing its output format.
Using Lambda for real-time transformation and combining that with a Glue ETL job introduces unnecessary complexity. Firehose already supports direct output to Parquet format, making the Lambda function redundant.
NEW QUESTION # 154
Which connector creates the RECORD_CONTENT and RECORD_METADATA columns in the existing Snowflake table while connecting to Snowflake?
Answer: B
Explanation:
Explanation
Apache Kafka software uses a publish and subscribe model to write and read streams of records, similar to a message queue or enterprise messaging system. Kafka allows processes to read and write messages asynchronously. A subscriber does not need to be connected directly to a publisher; a pub-lisher can queue a message in Kafka for the subscriber to receive later.
An application publishes messages to a topic, and an application subscribes to a topic to receive those messages. Kafka can process, as well as transmit, messages; however, that is outside the scope of this document. Topics can be divided into partitions to increase scalability.
Kafka Connect is a framework for connecting Kafka with external systems, including databases. A Kafka Connect cluster is a separate cluster from the Kafka cluster. The Kafka Connect cluster sup-ports running and scaling out connectors (components that support reading and/or writing between external systems).
The Kafka connector is designed to run in a Kafka Connect cluster to read data from Kafka topics and write the data into Snowflake tables.
Every Snowflake table loaded by the Kafka connector has a schema consisting of two VARIANT columns:
RECORD_CONTENT. This contains the Kafka message.
RECORD_METADATA. This contains metadata about the message, for example, the topic from which the message was read.
NEW QUESTION # 155
A company runs an Apache Spark application every night in an Amazon EMR cluster. The company uses Amazon EC2 instances to supply compute capacity for the EMR cluster. The company deployed the Spark application in cluster mode.
An error occurs in the Spark application. A log for the error is stored in the application's Spark driver standard error logs. A data engineer needs to investigate the error.
Where can the data engineer find this error log?
Answer: B
Explanation:
In EMR cluster mode, the Spark driver runs inside the cluster and its standard error logs are captured as step logs. These logs are automatically archived to Amazon S3 and accessible from the Amazon EMR console under step logs. This is the correct location for investigating Spark driver errors.
NEW QUESTION # 156
A media company wants to use Amazon OpenSearch Service to analyze rea-time data about popular musical artists and songs. The company expects to ingest millions of new data events every day. The new data events will arrive through an Amazon Kinesis data stream. The company must transform the data and then ingest the data into the OpenSearch Service domain.
Which method should the company use to ingest the data with the LEAST operational overhead?
Answer: C
Explanation:
Amazon Kinesis Data Firehose is a fully managed service that reliably loads streaming data into data lakes, data stores and analytics services like OpenSearch Service. It can automatically scale to match the throughput of your data and requires no ongoing administration.
NEW QUESTION # 157
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