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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
| Topic 2: Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
| Topic 3: Data Security and Governance | 18% | - Implement data security controls - Apply governance and compliance best practices |
| Topic 4: Data Store Management | 26% | - Select appropriate data storage solutions - Optimize storage performance and cost |
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NEW QUESTION # 75
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.
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 # 76
A data engineer is configuring an AWS Glue Apache Spark extract, transform, and load (ETL) job. The job contains a sort-merge join of two large and equally sized DataFrames.
The job is failing with the following error: No space left on device.
Which solution will resolve the error?
Answer: B
Explanation:
A sort-merge join generates large shuffle files, leading to "No space left on device" errors when both datasets are large. Using a broadcast join sends a smaller dataset to all executors, avoiding shuffle and disk I/O overhead.
"Broadcast joins reduce shuffle I/O by distributing the smaller dataset to all worker nodes, mitigating disk space and shuffle errors."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This is the most cost-effective and direct fix for large shuffle-stage failures.
NEW QUESTION # 77
A company stores historical customer data in an Amazon Redshift table. A column named Email contains null entries and values that are not email addresses. The quality of the Email column is critical for multiple downstream processes. A data engineer must create an AWS Glue Data Quality rule that fails when the percentage of valid email addresses in the Email column is less than 90%.
Which component of an AWS Glue Data Quality rule will meet these requirements?
Answer: B
Explanation:
Option C is correct because the requirement is to verify that at least 90% of values in the Email column are valid email addresses. In AWS Glue Data Quality, rules are written in DQDL, and ColumnValues is the rule component used to validate whether column values satisfy a condition or pattern. AWS Glue Data Quality documentation explains that DQDL is the language used to define rules, and it specifically notes that ColumnValues rules can evaluate column content and that NULL values do not pass during comparisons.
That is important here because the problem explicitly says the column contains null entries and invalid email values. A threshold of > 0.9 means the rule passes only when more than 90% of rows satisfy the email- validation condition.
Option A is incorrect because Uniqueness checks the percentage of values that are unique, not whether values are valid email addresses. AWS documents Uniqueness as measuring how many values occur exactly once, which is unrelated to email-format validity. Option D has the same issue because UniqueValueRatio addresses uniqueness characteristics, not format validation. Option B uses the right rule family but the wrong threshold because > 0.1 would require only 10% valid values. Therefore, ColumnValues with a threshold greater than 0.9 is the correct choice.
NEW QUESTION # 78
A company has a data warehouse that contains a table that is named Sales. The company stores the table in Amazon Redshift The table includes a column that is named city_name. The company wants to query the table to find all rows that have a city_name that starts with "San" or "El." Which SQL query will meet this requirement?
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