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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Store Management | 26% | - Select appropriate data storage solutions - Optimize storage performance and cost |
| Topic 2: Data Ingestion and Transformation | 34% | - Build and manage data pipelines - Ingest and transform data using AWS services |
| Topic 3: Data Security and Governance | 18% | - Apply governance and compliance best practices - Implement data security controls |
| Topic 4: Data Operations and Support | 22% | - Monitor and maintain data pipelines - Troubleshoot data workflow issues |
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NEW QUESTION # 177
A company is planning to upgrade its Amazon Elastic Block Store (Amazon EBS) General Purpose SSD storage from gp2 to gp3. The company wants to prevent any interruptions in its Amazon EC2 instances that will cause data loss during the migration to the upgraded storage.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
Changing the volume type of the existing gp2 volumes to gp3 is the easiest and fastest way to migrate to the new storage type without any downtime or data loss. You can use the AWS Management Console, the AWS CLI, or the Amazon EC2 API to modify the volume type, size, IOPS, and throughput of your gp2 volumes.
The modification takes effect immediately, and you can monitor the progress of the modification using CloudWatch. The other options are either more complex or require additional steps, such as creating snapshots, transferring data, or attaching new volumes, which can increase the operational overhead and the risk of errors. References:
Migrating Amazon EBS volumes from gp2 to gp3 and save up to 20% on costs (Section: How to migrate from gp2 to gp3) Switching from gp2 Volumes to gp3 Volumes to Lower AWS EBS Costs (Section: How to Switch from GP2 Volumes to GP3 Volumes) Modifying the volume type, IOPS, or size of an EBS volume - Amazon Elastic Compute Cloud (Section:
Modifying the volume type)
NEW QUESTION # 178
A data engineer is building a data pipeline on AWS by using AWS Glue extract, transform, and load (ETL) jobs. The data engineer needs to process data from Amazon RDS and MongoDB, perform transformations, and load the transformed data into Amazon Redshift for analytics. The data updates must occur every hour.
Which combination of tasks will meet these requirements with the LEAST operational overhead? (Choose two.)
Answer: A,C
Explanation:
The correct answer is to configure AWS Glue triggers to run the ETL jobs every hour and use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift. AWS Glue triggers are a way to schedule and orchestrate ETL jobs with the least operational overhead. AWS Glue connections are a way to securely connect to data sources and targets using JDBC or MongoDB drivers. AWS Glue DataBrew is a visual data preparation tool that does not support MongoDB as a data source. AWS Lambda functions are a serverless option to schedule and run ETL jobs, but they have a limit of 15 minutes for execution time, which may not be enough for complex transformations. The Redshift Data API is a way to run SQL commands on Amazon Redshift clusters without needing a persistent connection, but it does not support loading data from AWS Glue ETL jobs. References:
AWS Glue triggers
AWS Glue connections
AWS Glue DataBrew
[AWS Lambda functions]
[Redshift Data API]
NEW QUESTION # 179
A company uses Amazon S3 as a data lake. The company sets up a data warehouse by using a multi-node Amazon Redshift cluster. The company organizes the data files in the data lake based on the data source of each data file.
The company loads all the data files into one table in the Redshift cluster by using a separate COPY command for each data file location. This approach takes a long time to load all the data files into the table. The company must increase the speed of the data ingestion. The company does not want to increase the cost of the process.
Which solution will meet these requirements?
Answer: C
Explanation:
The company is facing performance issues loading data into Amazon Redshift because it is issuing separate COPY commands for each data file location. The most efficient way to increase the speed of data ingestion into Redshift without increasing the cost is to use a manifest file.
Option D: Create a manifest file that contains the data file locations. Use a COPY command to load the data into Amazon Redshift.
A manifest file provides a list of all the data files, allowing the COPY command to load all files in parallel from different locations in Amazon S3. This significantly improves the loading speed without adding costs, as it optimizes the data loading process in a single COPY operation.
Other options (A, B, C) involve additional steps that would either increase the cost (provisioning clusters, using Glue, etc.) or do not address the core issue of needing a unified and efficient COPY process.
Reference:
Amazon Redshift COPY Command
Redshift Manifest File Documentation
NEW QUESTION # 180
A data engineer wants to optimize the runtime performance of an AWS Glue extract, transform, and load (ETL) job. The job processes large JSON files from Amazon S3. The job currently reads all fields from the source files but transforms only a subset of the fields.
Which solution will meet these requirements?
Answer: C
Explanation:
The correct answer is B because the performance issue is caused by reading unnecessary data. The best optimization is to reduce the amount of data and schema fields that the AWS Glue job reads before transformation. Pushdown-style filtering and selecting only required fields reduce scan and serialization overhead, especially when the job transforms only a subset of the JSON structure. Job bookmarks help track previously processed data, but they do not reduce the amount of data read within each file. Splitting the job into several smaller jobs increases orchestration and does not solve unnecessary field reads. An AWS Glue crawler catalogs metadata; it does not convert JSON files into Parquet. Therefore, restricting the source schema and pushing filtering closer to the source is the best available answer.
NEW QUESTION # 181
A data engineer runs Amazon Athena queries on data that is in an Amazon S3 bucket. The Athena queries use AWS Glue Data Catalog as a metadata table.
The data engineer notices that the Athena query plans are experiencing a performance bottleneck. The data engineer determines that the cause of the performance bottleneck is the large number of partitions that are in the S3 bucket. The data engineer must resolve the performance bottleneck and reduce Athena query planning time.
Which solutions will meet these requirements? (Choose two.)
Answer: A,D
Explanation:
The best solutions to resolve the performance bottleneck and reduce Athena query planning time are to create an AWS Glue partition index and enable partition filtering, and to use Athena partition projection based on the S3 bucket prefix.
AWS Glue partition indexes are a feature that allows you to speed up query processing of highly partitioned tables cataloged in AWS Glue Data Catalog. Partition indexes are available for queries in Amazon EMR, Amazon Redshift Spectrum, and AWS Glue ETL jobs. Partition indexes are sublists of partition keys defined in the table. When you create a partition index, you specify a list of partition keys that already exist on a given table. AWS Glue then creates an index for the specified keys and stores it in the Data Catalog. When you run a query that filters on the partition keys, AWS Glue uses the partition index to quickly identify the relevant partitions without scanning the entire table metadata. This reduces the query planning time and improves the query performance1.
Athena partition projection is a feature that allows you to speed up query processing of highly partitioned tables and automate partition management. In partition projection, Athena calculates partition values and locations using the table properties that you configure directly on your table in AWS Glue. The table properties allow Athena to 'project', or determine, the necessary partition information instead of having to do a more time-consuming metadata lookup in the AWS Glue Data Catalog. Because in-memory operations are often faster than remote operations, partition projection can reduce the runtime of queries against highly partitioned tables. Partition projection also automates partition management because it removes the need to manually create partitions in Athena, AWS Glue, or your external Hive metastore2.
Option B is not the best solution, as bucketing the data based on a column that the data have in common in a WHERE clause of the user query would not reduce the query planning time. Bucketing is a technique that divides data into buckets based on a hash function applied to a column. Bucketing can improve the performance of join queries by reducing the amount of data that needs to be shuffled between nodes. However, bucketing does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario3.
Option D is not the best solution, as transforming the data that is in the S3 bucket to Apache Parquet format would not reduce the query planning time. Apache Parquet is a columnar storage format that can improve the performance of analytical queries by reducing the amount of data that needs to be scanned and providing efficient compression and encoding schemes. However, Parquet does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario4.
Option E is not the best solution, as using the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects would not reduce the query planning time. S3DistCP is a tool that can copy large amounts of data between Amazon S3 buckets or from HDFS to Amazon S3. S3DistCP can also aggregate smaller files into larger files to improve the performance of sequential access. However, S3DistCP does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario5. Reference:
Improve query performance using AWS Glue partition indexes
Partition projection with Amazon Athena
Bucketing vs Partitioning
Columnar Storage Formats
S3DistCp
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
NEW QUESTION # 182
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