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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Operations and Support | 22% | - Monitor and troubleshoot data pipelines
- Backup, restore, and disaster recovery - Ensure reliability and scalability |
| Topic 2: Data Store Management | 26% | - Manage data lifecycle and storage tiers - Design and implement data storage solutions
|
| Topic 3: Data Security and Governance | 18% | - Encrypt data at rest and in transit - Protect sensitive data - Implement access control and authentication
|
| Topic 4: Data Ingestion and Transformation | 34% | - Implement data quality and validation - Transform and enrich data
|
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NEW QUESTION # 44
A data engineer manages a provisioned Amazon Redshift cluster that stores petabytes of data. The cluster receives continuous data ingestion from multiple sources. As data volume grows, the query performance has degraded. The current table design uses the AUTO distribution style and default sort keys. The data engineer needs to optimize the query performance for recent transactions.
Which solution will meet these requirements?
Answer: B
Explanation:
The correct answer is A because the workload has grown to petabyte scale, but the performance requirement is focused on recent transactions. The best architecture is to keep hot, frequently queried data inside Amazon Redshift and move historical/cold data to Amazon S3. Amazon Redshift Spectrum lets Redshift efficiently query structured and semi-structured files in S3 without loading that data into Redshift tables, and most of the data remains in S3 while Spectrum performs parallel processing. This reduces pressure on the provisioned cluster while preserving query access to historical data. Decreasing nodes would worsen performance. DISTSTYLE ALL for historical tables can increase storage and maintenance overhead. Redshift is already columnar, so switching to row-based storage is not valid.
NEW QUESTION # 45
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?
Answer: D
Explanation:
Option C is the most cost-effective because it keeps the datasets in Amazon S3 and uses Amazon Athena to query them with SQL only when needed, avoiding the cost of running always-on database infrastructure. The study material states that "Amazon Athena is a serverless service that allows you to query data stored in Amazon S3 using standard SQL" . This directly matches the requirement that analysts will "analyze the data by using SQL queries," while remaining cost-efficient due to serverless, on-demand querying.
To make CSV and JSON in S3 easily queryable, metadata must be discoverable and managed. The material also highlights that AWS Glue automates cataloging data in S3 through the AWS Glue Data Catalog, which
"helps discover and manage metadata for data stored in AWS," enabling query engines to treat file data as tables .
Other options are less aligned: RDS MySQL adds continuous capacity and administration costs; DataBrew is primarily for visual preparation (clean/normalize) rather than serving as the SQL query layer ; and QuickSight
/SPICE targets BI dashboards, not general-purpose ad hoc SQL over raw files.
NEW QUESTION # 46
A data engineer must manage the ingestion of real-time streaming data into AWS. The data engineer wants to perform real-time analytics on the incoming streaming data by using time-based aggregations over a window of up to 30 minutes. The data engineer needs a solution that is highly fault tolerant.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
This solution meets the requirements of managing the ingestion of real-time streaming data into AWS and performing real-time analytics on the incoming streaming data with the least operational overhead. Amazon Managed Service for Apache Flink is a fully managed service that allows you to run Apache Flink applications without having to manage any infrastructure or clusters. Apache Flink is a framework for stateful stream processing that supports various types of aggregations, such as tumbling, sliding, and session windows, over streaming data. By using Amazon Managed Service for Apache Flink, you can easily connect to Amazon Kinesis Data Streams as the source and sink of your streaming data, and perform time-based analytics over a window of up to 30 minutes. This solution is also highly fault tolerant, as Amazon Managed Service for Apache Flink automatically scales, monitors, and restarts your Flink applications in case of failures. Reference:
Amazon Managed Service for Apache Flink
Apache Flink
Window Aggregations in Flink
NEW QUESTION # 47
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.)
Answer: B,C
Explanation:
Partitioning the data in the S3 bucket can improve the performance of AWS Glue jobs by reducing the amount of data that needs to be scanned and processed. By organizing the data by year, month, and day, the AWS Glue job can use partition pruning to filter out irrelevant data and only read the data that matches the query criteria. This can speed up the data processing and reduce the cost of running the AWS Glue job. Increasing the AWS Glue instance size by scaling up the worker type can also improve the performance of AWS Glue jobs by providing more memory and CPU resources for the Spark execution engine. This can help the AWS Glue job handle larger data sets and complex transformations more efficiently. The other options are either incorrect or irrelevant, as they do not affect the performance of the AWS Glue jobs. Converting the AWS Glue schema to the DynamicFrame schema class does not improve the performance, but rather provides additional functionality and flexibility for data manipulation. Adjusting the AWS Glue job scheduling frequency does not improve the performance, but rather reduces the frequency of data updates. Modifying the IAM role that grants access to AWS Glue does not improve the performance, but rather affects the security and permissions of the AWS Glue service. Reference:
Optimising Glue Scripts for Efficient Data Processing: Part 1 (Section: Partitioning Data in S3) Best practices to optimize cost and performance for AWS Glue streaming ETL jobs (Section: Development tools) Monitoring with AWS Glue job run insights (Section: Requirements) AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 133)
NEW QUESTION # 48
A data engineer is using an AWS Glue ETL job to remove outdated customer records from a table that contains customer account information. The data engineer is using the following SQL command:
MERGE INTO accounts t USING monthly_accounts_update s
ON t.customer = s.customer
WHEN MATCHED THEN DELETE
What will happen when the data engineer runs the SQL command?
Answer: C
Explanation:
In AWS Glue's SQL implementation (Spark SQL-compatible), the MERGE INTO statement supports conditional actions.
The clause WHEN MATCHED THEN DELETE deletes matching records from the target table (accounts) where the join condition is true.
"A MERGE INTO statement can perform updates, inserts, or deletes based on the match condition between source and target tables."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
NEW QUESTION # 49
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