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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q200-Q205):

NEW QUESTION # 200
A company stores daily records of the financial performance of investment portfolios in .csv format in an Amazon S3 bucket. A data engineer uses AWS Glue crawlers to crawl the S3 data.
The data engineer must make the S3 data accessible daily in the AWS Glue Data Catalog.
Which solution will meet these requirements?

Answer: D

Explanation:
To make the S3 data accessible daily in the AWS Glue Data Catalog, the data engineer needs to create a crawler that can crawl the S3 data and write the metadata to the Data Catalog. The crawler also needs to run on a daily schedule to keep the Data Catalog updated with the latest data. Therefore, the solution must include the following steps:
* Create an IAM role that has the necessary permissions to access the S3 data and the Data Catalog. The AWSGlueServiceRole policy is a managed policy that grants these permissions1.
* Associate the role with the crawler.
* Specify the S3 bucket path of the source data as the crawler's data store. The crawler will scan the data and infer the schema and format2.
* Create a daily schedule to run the crawler. The crawler will run at the specified time every day and update the Data Catalog with any changes in the data3.
* Specify a database name for the output. The crawler will create or update a table in the Data Catalog under the specified database. The table will contain the metadata about the data in the S3 bucket, such as the location, schema, and classification.
Option B is the only solution that includes all these steps. Therefore, option B is the correct answer.
Option A is incorrect because it configures the output destination to a new path in the existing S3 bucket. This is unnecessary and may cause confusion, as the crawler does not write any data to the S3 bucket, only metadata to the Data Catalog.
Option C is incorrect because it allocates data processing units (DPUs) to run the crawler every day. This is also unnecessary, as DPUs are only used for AWS Glue ETL jobs, not crawlers.
Option D is incorrect because it combines the errors of option A and C. It configures the output destination to a new path in the existing S3 bucket and allocates DPUs to run the crawler every day, both of which are irrelevant for the crawler.
References:
* 1: AWS managed (predefined) policies for AWS Glue - AWS Glue
* 2: Data Catalog and crawlers in AWS Glue - AWS Glue
* 3: Scheduling an AWS Glue crawler - AWS Glue
* [4]: Parameters set on Data Catalog tables by crawler - AWS Glue
* [5]: AWS Glue pricing - Amazon Web Services (AWS)


NEW QUESTION # 201
A company is building data processing pipelines by using AWS Glue. The pipelines access data stored in Amazon S3. The company has organized the data into folders with prefixes that represent different classification levels. The company needs to restrict AWS Glue jobs to access only specific prefixes based on the data classification. The company must also restrict access to business hours (9 AM to 5 PM).
Which elements must the company include in a custom IAM policy to meet these requirements?

Answer: C

Explanation:
Option B is correct because access to specific S3 folders for AWS Glue jobs should be restricted by scoping permissions to the correct S3 object ARNs in the IAM policy Resource element, such as bucket paths with wildcard patterns for the allowed prefixes. Time-based access is then enforced by adding a Condition that uses the global IAM condition key aws:CurrentTime with date operators such as DateGreaterThan and DateLessThan. AWS IAM documentation explicitly states that date condition operators are used with aws:
CurrentTime or aws:EpochTime in the Condition element.
Option C is not the best answer because s3:prefix is generally used with bucket listing operations rather than as the main mechanism to control object-level access for Glue reads and writes. For object access, the cleanest least-privilege design is to scope the allowed object ARNs directly. Option A is incorrect because $util.time is not an IAM policy condition key. Option D uses the wrong S3 condition key for the stated requirement. The uploaded study guide also emphasizes granting AWS Glue access through an IAM service role with the right scoped permissions, which fits this pattern.


NEW QUESTION # 202
A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs.
The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account.
Which solution will meet these requirements?

Answer: C

Explanation:
Amazon Kinesis Data Streams is a service that enables you to collect, process, and analyze real-time streaming data. You can use Kinesis Data Streams to ingest data from various sources, such as Amazon CloudWatch Logs, and deliver it to different destinations, such as Amazon S3 or Amazon Redshift. To use Kinesis Data Streams to deliver the security logs from the production AWS account to the security AWS account, you need to create a destination data stream in the security AWS account. This data stream will receive the log data from the CloudWatch Logs service in the production AWS account. To enable this cross-account data delivery, you need to create an IAM role and a trust policy in the security AWS account. The IAM role defines the permissions that the CloudWatch Logs service needs to put data into the destination data stream. The trust policy allows the production AWS account to assume the IAM role. Finally, you need to create a subscription filter in the production AWS account. A subscription filter defines the pattern to match log events and the destination to send the matching events. In this case, the destination is the destination data stream in the security AWS account. This solution meets the requirements of using Kinesis Data Streams to deliver the security logs to the security AWS account. The other options are either not possible or not optimal. You cannot create a destination data stream in the production AWS account, as this would not deliver the data to the security AWS account. You cannot create a subscription filter in the security AWS account, as this would not capture the log events from the production AWS account. Reference:
Using Amazon Kinesis Data Streams with Amazon CloudWatch Logs
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.3: Amazon Kinesis Data Streams


NEW QUESTION # 203
A gaming company uses AWS Glue to perform read and write operations on Apache Iceberg tables for real- time streaming data. The data in the Iceberg tables is stored in Apache Parquet format. The company is experiencing slow query performance.
Which solutions will improve query performance? (Select TWO)

Answer: A,D

Explanation:
Apache Iceberg query performance depends heavily on metadata optimization and file layout efficiency.
Generating column-level statistics allows query engines such as AWS Glue, Amazon Athena, and Amazon Redshift to perform predicate pushdown and data skipping. These statistics help avoid scanning unnecessary Parquet files and significantly reduce query execution time.
Real-time streaming workloads often generate many small files, which degrades query performance. Table compaction consolidates small Parquet files into fewer, larger files, improving scan efficiency and reducing metadata overhead. Iceberg supports compaction as a core optimization technique, and Glue can schedule these operations automatically.
Iceberg does not use traditional indexes, so index optimization is not applicable. Copy-on-write affects write semantics and consistency, not query speed. Views do not improve physical query performance.
Therefore, generating statistics and compacting files are the two most effective optimizations.


NEW QUESTION # 204
A company needs to build a data lake in AWS. The company must provide row-level data access and column- level data access to specific teams. The teams will access the data by using Amazon Athena, Amazon Redshift Spectrum, and Apache Hive from Amazon EMR.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: C

Explanation:
Option D is the best solution to meet the requirements with the least operational overhead because AWS Lake Formation is a fully managed service that simplifies the process of building, securing, and managing data lakes. AWS Lake Formation allows you to define granular data access policies at the row and column level for different users and groups. AWS Lake Formation also integrates with Amazon Athena, Amazon RedshiftSpectrum, and Apache Hive on Amazon EMR, enabling these services to access the data in the data lake through AWS Lake Formation.
Option A is not a good solution because S3 access policies cannot restrict data access by rows and columns.
S3 access policies are based on the identity and permissions of the requester, the bucket and object ownership, and the object prefix and tags. S3 access policies cannot enforce fine-grained data access control at the row and column level.
Option B is not a good solution because it involves using Apache Ranger and Apache Pig, which are not fully managed services and require additional configuration and maintenance. Apache Ranger is a framework that provides centralized security administration for data stored in Hadoop clusters, such as Amazon EMR.
Apache Ranger can enforce row-level and column-level access policies for Apache Hive tables. However, Apache Ranger is not a native AWS service and requires manual installation and configuration on Amazon EMR clusters. Apache Pig is a platform that allows you to analyze large data sets using a high-level scripting language called Pig Latin. Apache Pig can access data stored in Amazon S3 and process it using Apache Hive. However, Apache Pig is not a native AWS service and requires manual installation and configuration on Amazon EMR clusters.
Option C is not a good solution because Amazon Redshift is not a suitable service for data lake storage.
Amazon Redshift is a fully managed data warehouse service that allows you to run complex analytical queries using standard SQL. Amazon Redshift can enforce row-level and column-level access policies for different users and groups. However, Amazon Redshift is not designed to store and process large volumes of unstructured or semi-structured data, which are typical characteristics of data lakes. Amazon Redshift is also more expensive and less scalable than Amazon S3 for data lake storage.
:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
What Is AWS Lake Formation? - AWS Lake Formation
Using AWS Lake Formation with Amazon Athena - AWS Lake Formation
Using AWS Lake Formation with Amazon Redshift Spectrum - AWS Lake Formation Using AWS Lake Formation with Apache Hive on Amazon EMR - AWS Lake Formation Using Bucket Policies and User Policies - Amazon Simple Storage Service Apache Ranger Apache Pig What Is Amazon Redshift? - Amazon Redshift


NEW QUESTION # 205
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