Quick Answer
The answer is AWS Resource Access Manager (RAM). This is the correct choice because RAM allows you to share Glue Data Catalog databases and tables across multiple AWS accounts without duplicating metadata, enabling a centralized, single source of truth for your data lake while keeping permissions granular and auditable. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of cross-account data governance for ML pipelines—a common scenario where training data or feature stores reside in a central catalog but need to be consumed by separate accounts for ETL or querying. The typical trap is confusing this with AWS Lake Formation, which manages fine-grained access but relies on RAM for the initial cross-account sharing; remember that RAM is the underlying sharing mechanism, while Lake Formation adds column-level security on top. Memory tip: RAM shares the catalog, Lake Formation locks the columns.
This MLS-C01 practice question tests your understanding of data engineering. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company is using AWS Glue Data Catalog as the metadata store for their data lake. They have multiple AWS accounts and want to share the catalog across accounts. Which feature should they use?
Amazon Athena Federated Query
Why wrong: Federated query accesses external sources, not catalog sharing.
AWS Lake Formation
Why wrong: Lake Formation helps with permissions but not cross-account catalog sharing directly.
AWS Resource Access Manager (RAM)
RAM allows sharing Glue Data Catalog across accounts.
Amazon S3 Cross-Region Replication
Why wrong: CRR is for data replication, not metadata catalog.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
AWS Resource Access Manager (RAM)
AWS Resource Access Manager (RAM) enables you to share AWS Glue Data Catalog databases and tables across multiple AWS accounts without needing to copy metadata. This allows a centralized catalog to be consumed by different accounts for querying and ETL operations, maintaining a single source of truth for the data lake.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Amazon Athena Federated Query
Why it's wrong here
Federated query accesses external sources, not catalog sharing.
AWS Lake Formation
Why it's wrong here
Lake Formation helps with permissions but not cross-account catalog sharing directly.
AWS Resource Access Manager (RAM)
Why this is correct
RAM allows sharing Glue Data Catalog across accounts.
Related concept
Read the scenario before looking for a memorised answer.
Amazon S3 Cross-Region Replication
Why it's wrong here
CRR is for data replication, not metadata catalog.
Common exam traps
The trap here is that candidates often confuse AWS Lake Formation's cross-account access capabilities with the actual sharing mechanism, but Lake Formation relies on AWS RAM to enable the sharing of Data Catalog resources.
Detailed technical explanation
AWS RAM uses a resource-based sharing model where you create a resource share that includes the Glue Data Catalog databases and tables, and then specify principal AWS accounts or organizational units. The shared catalog appears as a read-only resource in the consumer account, and changes to the catalog (e.g., adding partitions) must be made in the owner account. This is critical for multi-account data lake architectures where teams need consistent metadata without duplicating schemas.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
The correct answer is: AWS Resource Access Manager (RAM) — AWS Resource Access Manager (RAM) enables you to share AWS Glue Data Catalog databases and tables across multiple AWS accounts without needing to copy metadata. This allows a centralized catalog to be consumed by different accounts for querying and ETL operations, maintaining a single source of truth for the data lake.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
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