A data engineering team is designing a pipeline to ingest data from a legacy mainframe that exports files nightly to an FTP server. The files are large and contain fixed-width records. The team needs to load the data into a cloud data warehouse. What should the pipeline do?

Parse the fixed-width files, validate record lengths, and load into the warehouse

Convert the files to JSON before parsing

Use a streaming platform to ingest the files

Load the files directly into the warehouse without parsing

Why is data versioning important in a machine learning project?

To ensure reproducibility of experiments

To automatically clean the data

To increase the speed of model training

A machine learning team uses a feature store to serve features for training and inference. They update a feature definition, which changes the feature values for new data. How should they handle the change to maintain reproducibility of models trained before the change?

Store the new feature values in a separate table without versioning.

Overwrite the existing feature definition so all models use the new values.

Create a new feature version and keep the old version available for models that were trained on it.

Delete the old feature and retrain all models immediately.

GIAC Machine Learning Engineer

Domain 1Objective 1

Data Acquisition GMLE Practice Questions (Page 2)

Part of the Foundational Data Science and Statistics domain, which makes up ~41% of our current practice bank. GIAC (SANS) does not publish an official question count, but from its 180-minute exam (~70–120 total, ~29–49 in this domain), expect 7–12 from this objective — we provide 39 practice questions to prepare you well beyond it. (estimate)

39questions here

8free pages

5concepts

Questions 6–10

6foundation · easy
A data engineer needs to extract all customer records from a relational database where the customer's country is 'Canada'. Which SQL clause is used to filter the rows?
Select an answer first
7application · medium
A data engineering team is designing a pipeline to ingest data from a legacy mainframe that exports files nightly to an FTP server. The files are large and contain fixed-width records. The team needs to load the data into a cloud data warehouse. What should the pipeline do?
Select an answer first
8foundation · easy
Why is data versioning important in a machine learning project?
Select an answer first
9foundation · easy
During data acquisition, a dataset has many missing values in a critical column. Which data quality dimension is most directly affected?
Select an answer first
10application · medium
A machine learning team uses a feature store to serve features for training and inference. They update a feature definition, which changes the feature values for new data. How should they handle the change to maintain reproducibility of models trained before the change?
Select an answer first

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Data Acquisition GMLE Practice Questions (Page 2)

Questions 6–10

A data engineer needs to extract all customer records from a relational database where the customer's country is 'Canada'. Which SQL clause is used to filter the rows?

A data engineering team is designing a pipeline to ingest data from a legacy mainframe that exports files nightly to an FTP server. The files are large and contain fixed-width records. The team needs to load the data into a cloud data warehouse. What should the pipeline do?

Why is data versioning important in a machine learning project?

During data acquisition, a dataset has many missing values in a critical column. Which data quality dimension is most directly affected?

A machine learning team uses a feature store to serve features for training and inference. They update a feature definition, which changes the feature values for new data. How should they handle the change to maintain reproducibility of models trained before the change?

Keep studying — Foundational Data Science and Statistics