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Amazon-DEA-C01 AWS Certified Data Engineer - Associate Exam Practice Test
Question 1
A data engineer needs to use Amazon Neptune to develop graph applications.
Which programming languages should the engineer use to develop the graph applications? (Select TWO.)
Answer : A, D
Amazon Neptune supports graph applications using Gremlin and SPARQL as query languages. Neptune is a fully managed graph database service that supports both property graph and RDF graph models.
Option A: Gremlin Gremlin is a query language for property graph databases, which is supported by Amazon Neptune. It allows the traversal and manipulation of graph data in the property graph model.
Option D: SPARQL SPARQL is a query language for querying RDF graph data in Neptune. It is used to query, manipulate, and retrieve information stored in RDF format.
Other options:
SQL (Option B) and ANSI SQL (Option C) are traditional relational database query languages and are not used for graph databases.
Spark SQL (Option E) is related to Apache Spark for big data processing, not for querying graph databases.
Question 2
An ecommerce company wants to use AWS to migrate data pipelines from an on-premises environment into the AWS Cloud. The company currently uses a third-party too in the on-premises environment to orchestrate data ingestion processes.
The company wants a migration solution that does not require the company to manage servers. The solution must be able to orchestrate Python and Bash scripts. The solution must not require the company to refactor any code.
Which solution will meet these requirements with the LEAST operational overhead?
Answer : B
The ecommerce company wants to migrate its data pipelines into the AWS Cloud without managing servers, and the solution must orchestrate Python and Bash scripts without refactoring code. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is the most suitable solution for this scenario.
Option B: Amazon Managed Workflows for Apache Airflow (Amazon MWAA) MWAA is a managed orchestration service that supports Python and Bash scripts via Directed Acyclic Graphs (DAGs) for workflows. It is a serverless, managed version of Apache Airflow, which is commonly used for orchestrating complex data workflows, making it an ideal choice for migrating existing pipelines without refactoring. It supports Python, Bash, and other scripting languages, and the company would not need to manage the underlying infrastructure.
Other options:
AWS Lambda (Option A) is more suited for event-driven workflows but would require breaking down the pipeline into individual Lambda functions, which may require refactoring.
AWS Step Functions (Option C) is good for orchestration but lacks native support for Python and Bash without using Lambda functions, and it may require code changes.
AWS Glue (Option D) is an ETL service primarily for data transformation and not suitable for orchestrating general scripts without modification.
Amazon Managed Workflows for Apache Airflow (MWAA) Documentation
Question 3
A data engineer uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to run data pipelines in an AWS account. A workflow recently failed to run. The data engineer needs to use Apache Airflow logs to diagnose the failure of the workflow. Which log type should the data engineer use to diagnose the cause of the failure?
Answer : D
In Amazon Managed Workflows for Apache Airflow (MWAA), the type of log that is most useful for diagnosing workflow (DAG) failures is the Task logs. These logs provide detailed information on the execution of each task within the DAG, including error messages, exceptions, and other critical details necessary for diagnosing failures.
Option D: YourEnvironmentName-Task Task logs capture the output from the execution of each task within a workflow (DAG), which is crucial for understanding what went wrong when a DAG fails. These logs contain detailed execution information, including errors and stack traces, making them the best source for debugging.
Other options (WebServer, Scheduler, and DAGProcessing logs) provide general environment-level logs or logs related to scheduling and DAG parsing, but they do not provide the granular task-level execution details needed for diagnosing workflow failures.
Question 4
A company wants to migrate data from an Amazon RDS for PostgreSQL DB instance in the eu-east-1 Region of an AWS account named Account_
Answer : A, A
To migrate data from an Amazon RDS for PostgreSQL DB instance in the eu-east-1 Region (Account_A) to an Amazon Redshift cluster in the eu-west-1 Region (Account_B), AWS DMS needs a replication instance located in the target region (in this case, eu-west-1) to facilitate the data transfer between regions.
Option A: Set up an AWS DMS replication instance in Account_B in eu-west-1. Placing the DMS replication instance in the target account and region (Account_B in eu-west-1) is the most efficient solution. The replication instance can connect to the source RDS PostgreSQL in eu-east-1 and migrate the data to the Redshift cluster in eu-west-1. This setup ensures data is replicated across AWS accounts and regions.
Options B, C, and D place the replication instance in either the wrong account or region, which increases complexity without adding any benefit.
Question 5
A data engineer maintains a materialized view that is based on an Amazon Redshift database. The view has a column named load_date that stores the date when each row was loaded.
The data engineer needs to reclaim database storage space by deleting all the rows from the materialized view.
Which command will reclaim the MOST database storage space?
Answer : A
To reclaim the most storage space from a materialized view in Amazon Redshift, you should use a DELETE operation that removes all rows from the view. The most efficient way to remove all rows is to use a condition that always evaluates to true, such as 1=1. This will delete all rows without needing to evaluate each row individually based on specific column values like load_date.
Option A: DELETE FROM materialized_view_name WHERE 1=1; This statement will delete all rows in the materialized view and free up the space. Since materialized views in Redshift store precomputed data, performing a DELETE operation will remove all stored rows.
Other options either involve inappropriate SQL statements (e.g., VACUUM in option C is used for reclaiming storage space in tables, not materialized views), or they don't remove data effectively in the context of a materialized view (e.g., TRUNCATE cannot be used directly on a materialized view).
Question 6
A company ingests data from multiple data sources and stores the data in an Amazon S3 bucket. An AWS Glue extract, transform, and load (ETL) job transforms the data and writes the transformed data to an Amazon S3 based data lake. The company uses Amazon Athena to query the data that is in the data lake.
The company needs to identify matching records even when the records do not have a common unique identifier.
Which solution will meet this requirement?
Answer : D, D
The problem described requires identifying matching records even when there is no unique identifier. AWS Lake Formation FindMatches is designed for this purpose. It uses machine learning (ML) to deduplicate and find matching records in datasets that do not share a common identifier.
Alternatives Considered:
A (Amazon Made pattern matching): Amazon Made is not a service in AWS, and pattern matching typically refers to regular expressions, which are not suitable for deduplication without a common identifier.
B (AWS Glue PySpark Filter class): PySpark's Filter class can help refine datasets, but it does not offer the ML-based matching capabilities required to find matches between records without unique identifiers.
C (Partition tables on a unique identifier): Partitioning requires a unique identifier, which the question states is unavailable.
Question 7
A retail company uses an Amazon Redshift data warehouse and an Amazon S3 bucket. The company ingests retail order data into the S3 bucket every day.
The company stores all order data at a single path within the S3 bucket. The data has more than 100 columns. The company ingests the order data from a third-party application that generates more than 30 files in CSV format every day. Each CSV file is between 50 and 70 MB in size.
The company uses Amazon Redshift Spectrum to run queries that select sets of columns. Users aggregate metrics based on daily orders. Recently, users have reported that the performance of the queries has degraded. A data engineer must resolve the performance issues for the queries.
Which combination of steps will meet this requirement with LEAST developmental effort? (Select TWO.)
Answer : A, C
The performance issue in Amazon Redshift Spectrum queries arises due to the nature of CSV files, which are row-based storage formats. Spectrum is more optimized for columnar formats, which significantly improve performance by reducing the amount of data scanned. Also, partitioning data based on relevant columns like order date can further reduce the amount of data scanned, as queries can focus only on the necessary partitions.
A . Configure the third-party application to create the files in a columnar format:
Columnar formats (like Parquet or ORC) store data in a way that is optimized for analytical queries because they allow queries to scan only the columns required, rather than scanning all columns in a row-based format like CSV.
Amazon Redshift Spectrum works much more efficiently with columnar formats, reducing the amount of data that needs to be scanned, which improves query performance.
C . Partition the order data in the S3 bucket based on order date:
Partitioning the data on columns like order date allows Redshift Spectrum to skip scanning unnecessary partitions, leading to improved query performance.
By organizing data into partitions, you minimize the number of files Spectrum has to read, further optimizing performance.
Alternatives Considered:
B (Develop an AWS Glue ETL job): While consolidating files can improve performance by reducing the number of small files (which can be inefficient to process), it adds additional ETL complexity. Switching to a columnar format (Option A) and partitioning (Option C) provides more significant performance improvements with less development effort.
D and E (JSON-related options): Using JSON format or the SUPER type in Redshift introduces complexity and isn't as efficient as the proposed solutions, especially since JSON is not a columnar format.
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