DATABRICKS-GAIE Practice Questions: Data Preparation Domain

Published: September 29, 2025 | 20 min read

Test your DATABRICKS-GAIE knowledge with 10 practice questions from the Data Preparation domain. Includes detailed explanations and answers.

DATABRICKS-GAIE Practice Questions

Master the Data Preparation Domain

Test your knowledge in the Data Preparation domain with these 10 practice questions. Each question is designed to help you prepare for the DATABRICKS-GAIE certification exam with detailed explanations to reinforce your learning.

Question 1

While designing an ETL pipeline in Databricks, you need to ensure data quality by removing duplicate records based on the 'user_id' column. Which of the following PySpark operations should you use to achieve this?

A) df.dropDuplicates(['user_id'])

B) df.distinct('user_id')

C) df.filter(df['user_id'].isNotNull())

D) df.dropna(subset=['user_id'])

Show Answer & Explanation

Correct Answer: A

Explanation: Option A uses the 'dropDuplicates()' method, which is the correct approach to remove duplicate records based on specific columns, in this case, 'user_id'. Option B is incorrect because 'distinct()' is used to remove duplicate rows entirely, not based on a specific column. Option C filters out null values, which is not the same as removing duplicates. Option D removes rows with null values in 'user_id', not duplicates.

Question 2

You need to prepare a dataset for machine learning that includes handling missing values and scaling numerical features. Which Databricks feature can be used to efficiently handle these tasks as part of a data preparation pipeline?

A) Databricks SQL

B) MLflow

C) Databricks Feature Store

D) Delta Live Tables

Show Answer & Explanation

Correct Answer: C

Explanation: Databricks Feature Store is designed to manage and serve machine learning features, including handling missing values and scaling numerical features as part of data preparation pipelines. Databricks SQL (A) is for querying data, MLflow (B) is for managing the ML lifecycle, and Delta Live Tables (D) is for building ETL pipelines but not specifically for feature engineering tasks.

Question 3

You are working with a large dataset in Databricks and need to optimize the data for fast read access and efficient storage. Which Delta Lake feature should you use to achieve this?

A) Delta Lake's Time Travel

B) Optimize command with Z-Order clustering

C) Schema Evolution

D) Delta Lake's ACID Transactions

Show Answer & Explanation

Correct Answer: B

Explanation: The Optimize command with Z-Order clustering is specifically designed to improve query performance by co-locating related data. This is particularly useful for range queries and improves read efficiency. Time Travel (A) allows access to previous versions of data, Schema Evolution (C) handles changes in schema, and ACID Transactions (D) ensure data reliability but do not directly optimize read performance.

Question 4

While developing a feature engineering pipeline in Databricks, you need to ensure that the features derived from raw data are consistent and reliable. Which approach should you take to validate the feature engineering process?

A) Run the feature engineering pipeline on a subset of the data and manually inspect the results.

B) Use automated tests to validate feature transformations against expected outputs.

C) Rely on Databricks' built-in visualization tools to verify feature correctness.

D) Ensure that all feature engineering steps are documented in markdown cells.

Show Answer & Explanation

Correct Answer: B

Explanation: Using automated tests to validate feature transformations against expected outputs ensures consistency and reliability in the feature engineering process. Option A is not scalable or reliable. Option C provides visual verification but not automated validation. Option D is good practice for documentation but does not validate the process.

Question 5

You are tasked with designing an ETL pipeline in Databricks to process large volumes of streaming data. Which of the following features of Delta Lake would be most beneficial for ensuring data reliability and consistency?

A) Schema Evolution

B) Time Travel

C) ACID Transactions

D) Z-Ordering

Show Answer & Explanation

Correct Answer: C

Explanation: ACID Transactions in Delta Lake ensure that all operations on data are atomic, consistent, isolated, and durable. This is crucial for maintaining data reliability and consistency, especially in streaming data pipelines where data is continuously ingested and processed. Schema Evolution allows for changes to the schema, Time Travel enables querying previous versions of data, and Z-Ordering improves query performance, but they do not directly ensure data reliability and consistency like ACID Transactions do.

Question 6

You are tasked with ensuring data quality in your Databricks ETL pipeline. Which of the following practices would best help you maintain high data quality?

A) Use Delta Lake's schema enforcement and constraints

B) Rely solely on source data quality

C) Perform data validation only after loading into the target system

D) Ignore data quality checks to improve performance

Show Answer & Explanation

Correct Answer: A

Explanation: Using Delta Lake's schema enforcement and constraints is a best practice for maintaining high data quality, as it ensures data adheres to a defined schema and constraints. Relying solely on source data or ignoring quality checks can lead to poor data quality. Performing validation only after loading may be too late to catch issues.

Question 7

You are preparing a feature engineering pipeline in Databricks for a machine learning model. The dataset contains a 'timestamp' column, and you need to extract the 'hour' and 'day of the week' as separate features. Which of the following PySpark operations would you use?

A) df.withColumn('hour', hour('timestamp')).withColumn('day_of_week', dayofweek('timestamp'))

B) df.select(hour('timestamp').alias('hour'), dayofweek('timestamp').alias('day_of_week'))

C) df.withColumn('hour', date_format('timestamp', 'HH')).withColumn('day_of_week', date_format('timestamp', 'E'))

D) df.withColumn('hour', extract('hour', 'timestamp')).withColumn('day_of_week', extract('dow', 'timestamp'))

Show Answer & Explanation

Correct Answer: A

Explanation: Option A correctly uses PySpark functions 'hour()' and 'dayofweek()' to extract the hour and day of the week from a timestamp column. Option B would only select these columns without adding them to the existing DataFrame. Option C uses 'date_format()', which is not a direct PySpark function for extracting hour and day of the week. Option D uses incorrect function names.

Question 8

In your Databricks pipeline, you need to handle slowly changing dimensions (SCD) effectively. Which Delta Lake feature would be most suitable for implementing SCD Type 2?

A) Time Travel

B) MERGE INTO

C) VACUUM

D) Z-Ordering

Show Answer & Explanation

Correct Answer: B

Explanation: MERGE INTO is suitable for implementing SCD Type 2 in Delta Lake, as it allows you to insert new records and update existing ones with historical tracking. Time Travel allows querying historical data but doesn't implement SCD. VACUUM cleans up old files, and Z-Ordering optimizes query performance.

Question 9

During the data preparation phase in Databricks, you encounter missing values in a key dataset that will be used for training a Generative AI model. Which technique would be most appropriate to handle these missing values to ensure model accuracy?

A) Drop all rows with missing values to maintain data integrity.

B) Replace missing values with zeros to simplify the dataset.

C) Use data imputation techniques to fill in missing values based on other available data.

D) Ignore the missing values as they are not significant for model training.

Show Answer & Explanation

Correct Answer: C

Explanation: Using data imputation techniques to fill in missing values based on other available data helps maintain dataset integrity and can improve model accuracy by providing more complete information. Option A could result in significant data loss. Option B may introduce bias or inaccuracies. Option D ignores potential data quality issues.

Question 10

You are tasked with transforming raw data into a structured format suitable for machine learning model training on Databricks. The data is stored in a Delta Lake table. Which of the following steps is crucial to ensure data consistency and quality before feature engineering?

A) Directly start feature engineering without any checks.

B) Perform a full refresh of the Delta Lake table to ensure data is up-to-date.

C) Use Delta Lake's ACID transactions to perform data validation and clean up duplicates.

D) Export the data to a CSV file for manual inspection.

Show Answer & Explanation

Correct Answer: C

Explanation: Using Delta Lake's ACID transactions to perform data validation and clean up duplicates ensures data consistency and quality. Option A is incorrect because skipping validation could lead to poor quality data. Option B is inefficient without validation. Option D is impractical for large datasets.

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About DATABRICKS-GAIE Certification

The DATABRICKS-GAIE certification validates your expertise in data preparation and other critical domains. Our comprehensive practice questions are carefully crafted to mirror the actual exam experience and help you identify knowledge gaps before test day.