Databricks-GenAI-Assoc Data Preparation — All Questions With Answers

Question 1mediummulti select

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A team is preparing data for a machine learning model and needs to handle missing values in a feature vector. Which TWO techniques are standard practices in Databricks for handling nulls in large-scale feature engineering pipelines?

Question 2hardmultiple choice

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A Data Engineer needs to handle schema evolution in a Delta Lake pipeline where source systems frequently add new columns. Which configuration ensures that the write operation automatically updates the target table schema without manual intervention?

Question 3mediummultiple choice

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When preparing data for a high-frequency streaming dashboard, a Data Engineer finds that the downstream Delta table is being flooded with small files, causing slow reads. Which tool should be used to rectify this without stopping the streaming job?

Question 4hardmulti select

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You are migrating a legacy ETL pipeline to Databricks. Which THREE of the following steps are considered best practices for optimizing data preparation in a Delta Lake environment?

Question 5hardmultiple choice

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Refer to the exhibit. You are implementing a data quality framework using Delta Live Tables. What happens when a record arrives with a null value in the 'id' column?

Exhibit

{
  "rule": "enforce_schema",
  "action": "drop_nulls",
  "target_col": "id",
  "fail_on_error": true
}

Question 6mediummultiple choice

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During data preparation, you observe severe data skew in a join operation. Which strategy is best for mitigating this skew?

Question 7hardmultiple choice

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Refer to the exhibit. The join fails due to ambiguous column names. How can you resolve this while keeping the 'id' column in the final result?

Exhibit

Error: AnalysisException: 'Multiple sources found for column id.'

val df1 = spark.table("table_a")
val df2 = spark.table("table_b")
val joined = df1.join(df2, "id")

Question 8mediummultiple choice

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A Data Engineer is using Databricks to prepare data for a machine learning model. The source data has significant imbalance. Which technique is most effective for addressing this imbalance during the preparation stage?

Question 9mediummultiple choice

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You are preparing a dataset by calculating a rolling average over a window of 30 days. Which Window function specification is required to ensure the average correctly considers only the preceding 30 days?

Question 10mediummultiple choice

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A data engineer is preparing a dataset for a LLM fine-tuning task. The raw data consists of millions of JSON files stored in Unity Catalog. Which approach provides the highest performance for loading this data into a PyTorch-based training pipeline?

Question 11hardmulti select

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Which TWO of the following steps are essential when cleaning unstructured text data for RAG applications within Databricks?

Question 12mediummultiple choice

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Refer to the exhibit. A data engineer is using a JSON-based configuration to prepare log data. Given the policy is 'strict_schema', what happens if an incoming log entry contains a field not defined in the Delta table schema?

Exhibit

{
  "policy": "strict_schema",
  "data_source": "s3://raw-logs/",
  "transformation": "regex_filter",
  "sink": "delta_table"
}

Question 13mediummulti select

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Which THREE techniques are commonly used to optimize data preparation for embedding generation in Databricks?

Question 14hardmultiple choice

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An engineer notices that a feature vector table in Delta is becoming excessively fragmented after frequent 'upserts'. Which action should be taken to optimize the table for future reads during inference?

Question 15hardmultiple choice

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Refer to the exhibit. You are appending new, streaming JSON logs to an existing Delta table. What is the most appropriate way to handle this schema evolution?

Exhibit

Error: PySparkException: [CANNOT_MERGE_INCOMPATIBLE_DATATYPES] Cannot merge incompatible data types 'String' and 'Integer'.

Question 16mediummultiple choice

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An enterprise engineering team is preparing unstructured PDF invoices stored in a Unity Catalog volume for a Retrieval-Augmented Generation pipeline. Which ingestion approach best aligns with Databricks-recommended data preparation practices for generative AI?

Question 17mediummultiple choice

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A data engineer is preparing a large set of policy PDFs stored in a Unity Catalog volume for a RAG application. Before generating embeddings, they must remove page headers, footers, and page numbers that repeat across nearly every page. Which approach is most appropriate for cleaning this text at scale on Databricks?

Question 18mediummultiple choice

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A GenAI team is preparing a fine-tuning dataset stored as a Delta table in Unity Catalog. They need to ensure that each training example is a valid JSON string conforming to a specific schema before feeding it to the model. They want to validate and optionally quarantine malformed records in a single pass. Which Databricks SQL function is most appropriate for this task?

Question 19hardmulti select

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A data engineer is preparing a large corpus of text for a RAG application. The text is stored in a Delta table with columns doc_id and content. The engineer needs to split the content into chunks of approximately 512 tokens with a 50-token overlap, and then compute embeddings for each chunk. They want to use Databricks-native capabilities to minimize data movement and cost. Which two approaches are valid and efficient for this task? (Choose two.)

Question 20easymultiple choice

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A data engineer is preparing a dataset for training a generative AI model. The dataset contains a column 'text' with raw user reviews. Before tokenization, the engineer needs to remove HTML tags and special characters, convert text to lowercase, and expand common contractions. Which Databricks feature or approach is best suited for this text normalization step?

Question 21mediummultiple choice

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A data engineer is preparing a dataset for a RAG application. The source data is a Delta table with a 'content' column containing long documents. To improve retrieval, they need to split each document into overlapping chunks of approximately 500 tokens. They want to use a built-in Databricks function that leverages a tokenizer to count tokens accurately. Which approach should they take?

Question 22hardmultiple choice

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A team prepares customer support transcripts for fine-tuning. Each transcript is a JSON record with a nested messages array, and approximately 8 percent of records contain a null role field. The team wants records with malformed messages to be quarantined rather than silently dropped. Which Delta Live Tables configuration accomplishes this?

Question 23mediummultiple choice

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A data engineer is building a RAG pipeline and needs to store document embeddings in Databricks Vector Search. The embeddings are computed from text chunks and stored in a Delta table with columns chunk_id, embedding (array of floats), and metadata. The engineer wants to enable fast similarity search with metadata filtering. Which step is required to make this Delta table searchable in Databricks Vector Search?

Question 24mediummultiple choice

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A generative AI team stores raw support tickets in a Unity Catalog volume as newline-delimited JSON. Before fine-tuning, they must convert the nested arrays into a flat table and enforce the ticket schema on every read so malformed records fail fast instead of silently producing nulls. Which approach best satisfies both requirements?

Question 25mediummultiple choice

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A GenAI engineer has a Delta table of 50 million customer support transcripts in Unity Catalog, each containing a raw_text column. Before generating embeddings with a foundation model endpoint, they must split each transcript into ~500-token chunks and preserve the source document_id and chunk index. Which Databricks-native approach should they use to produce a new Delta table of chunked records at scale?

Question 26easymultiple choice

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An engineer is building a training set from a Delta table in Unity Catalog. The table contains a free-text column with customer emails, and the fine-tuning data must not expose personal data. Which approach satisfies the requirement while keeping the data usable for model training?

Question 27easymultiple choice

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A data engineer needs to convert a Delta table of raw HTML product descriptions into clean plain text for embedding generation. The cleaning must remove HTML tags, collapse whitespace, and normalize Unicode. Which Databricks capability should they use to apply this transformation across millions of rows?

Question 28mediummultiple choice

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A Data Engineer needs to ensure that PII data in a Delta table is masked before serving it to non-privileged users. Which Databricks feature provides the most efficient, centralized control for this requirement?

Question 29mediummultiple choice

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You need to ingest data from an external JSON source into a Delta table. The source schema is inconsistent. Which strategy is most effective for preparing this data?

Question 30mediummulti select

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Which TWO of the following are benefits of using Delta Lake for data preparation over standard Parquet files on cloud storage?

Question 31easymultiple choice

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Which Databricks feature is best suited for maintaining the lineage of data used during the preparation of training sets for Generative AI?

Question 32mediummultiple choice

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When preparing a dataset for fine-tuning an LLM, you need to ensure the data is representative of the target domain. What is the most effective approach to detect and mitigate sampling bias in your training set using Databricks?

Question 33mediummultiple choice

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Which of the following describes the purpose of using a 'Feature Store' when preparing data for Generative AI applications?

Question 34mediummultiple choice

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You are preparing a large dataset for fine-tuning a model using Databricks Delta Live Tables (DLT). Which configuration is best for ensuring data quality and lineage in this pipeline?

Question 35hardmulti select

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Which TWO factors are most important when selecting a chunking strategy for text data prior to vectorization?

Question 36mediummultiple choice

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A team is preparing data for a RAG system and needs to remove duplicates from a large collection of PDF text extracts. What is the most efficient way to perform de-duplication in Databricks?

Question 37easymultiple choice

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Which of the following describes the 'Gold' layer in a Medallion architecture, and why is it important for GenAI data preparation?

Question 38hardmultiple choice

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You need to store embeddings generated by an LLM in a Delta table. Which data type is most efficient for storing these high-dimensional vector arrays in Databricks?

Question 39mediummultiple choice

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When preparing data for a fine-tuning task, you realize the dataset is severely imbalanced. Which Databricks technique should you use to create a more balanced dataset?

Question 40mediummultiple choice

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A GenAI engineer is preparing a large text corpus stored in a Unity Catalog volume for fine-tuning a chat model. The raw files are JSON Lines, each containing a 'conversation' array with alternating 'user' and 'assistant' turns, but many records have malformed turns or missing roles. The engineer needs to convert this into a Delta table with a schema of (conversation_id STRING, messages ARRAY<STRUCT<role:STRING, content:STRING>>) while filtering out records where any turn has a null role or empty content. Which approach uses the appropriate Databricks-native capability for this transformation?

Question 41mediummultiple choice

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You are preparing a large corpus of customer support emails stored as Parquet files in a Unity Catalog volume. The emails must be cleaned by removing boilerplate signatures and disclaimers before tokenization for a fine-tuning dataset. Your team wants to enforce this transformation as a declarative, testable pipeline stage that fails fast if any cleaned record still contains a known boilerplate marker. Which Databricks capability should you use to implement this cleaning step with built-in data quality expectations?

Question 42easymultiple choice

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A data engineer is preparing a dataset of product descriptions for embedding generation. The source table in Unity Catalog contains a column 'description' with mixed languages, and the team wants to filter to English-only text before vectorization. They need a scalable, built-in Databricks function that can detect the language of each description without external API calls. Which function should they use?

Question 43mediummultiple choice

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A GenAI engineer is preparing a corpus of HTML product pages stored in a Unity Catalog volume for a RAG application. The pages contain navigation bars, script tags, and boilerplate footers that add noise to embeddings. Which Databricks-native approach best removes this noise while preserving the main article text before chunking?

Question 44hardmulti select

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You are preparing a large text corpus for fine-tuning a generative AI model. The corpus is stored in a Delta table with columns: doc_id, raw_text, and metadata. You need to create a cleaned dataset that removes personally identifiable information (PII) and normalizes whitespace, while preserving document boundaries for training. Which two actions should you perform to achieve this in a scalable and maintainable way? (Choose two.)

Question 45mediummultiple choice

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A GenAI engineer must build a fine-tuning dataset from 40 TB of raw JSONL conversation logs stored in cloud object storage. The logs are immutable and only ever read once, in full, during preprocessing. Which storage and access configuration should be used to minimize cost while keeping the data readable by Spark on Databricks?

Question 46mediummultiple choice

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A Generative AI engineer is building a Delta Live Tables pipeline that ingests raw JSON event logs into a bronze table, then uses ai_query to classify each event's free-text field. The classification call is expensive, so the engineer wants to avoid re-running it on events that have already been processed in previous pipeline updates. The source table is append-only and new events arrive continuously. Which Delta Live Tables feature should the engineer configure on the bronze table to prevent reprocessing of previously ingested rows?

Question 47hardmultiple choice

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A team is preparing a pretraining corpus and must filter out near-duplicate documents before tokenization. The corpus contains 500 million short text records in a Delta table. They want a scalable, deterministic deduplication signal that can be computed per record and compared across the dataset. Which approach best fits this requirement?

Question 48hardmultiple choice

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A data engineer is preparing a large corpus of customer support transcripts stored as Parquet in a Unity Catalog volume. Before generating embeddings, each transcript must be tokenized and truncated to a maximum token length. The engineer wants to use a Databricks-native approach that runs distributed across the cluster and avoids pulling the full corpus into a single node. Which approach best satisfies these requirements?

Question 49mediummulti select

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A Generative AI engineer is preparing a Delta table of product reviews for a retrieval-augmented generation application. The reviews contain HTML tags, inconsistent casing, and occasional very long paragraphs that exceed the embedding model's context window. The engineer wants to clean and normalize the text before chunking and embedding. Which two actions should the engineer take to directly address the stated quality issues? (Choose two.)

Question 50easymultiple choice

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A data engineer needs to persist a prepared instruction-tuning dataset so that downstream fine-tuning jobs can read it with ACID guarantees, time travel, and schema enforcement, and so that Unity Catalog can track column-level lineage. Which storage format and registration should be used?

Question 51easymultiple choice

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A team stores raw documents in a Unity Catalog volume and needs to build a training set for fine-tuning a large language model. The raw files are in mixed formats including PDF, DOCX, and plain text. The team wants a single Delta table where every row is one document with its extracted text and source path, and wants the extraction to run in parallel across the cluster. Which approach should the team use?

Question 52hardmultiple choice

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You are building a data preparation pipeline for a generative AI application. The raw data is stored in a Unity Catalog volume as JSON files with nested fields. You need to flatten the nested structure and extract specific fields into a Delta table for downstream embedding. The JSON schema may evolve, with new fields added occasionally. Which approach provides the most robust and maintainable solution?

Question 53mediummultiple choice

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An engineer is cleaning a Delta table of customer support transcripts stored in Unity Catalog. The table has a nested array column named messages, where each element has role and content fields. They need to keep only rows where at least one message has role equal to 'user' and content longer than 20 characters. Which transformation correctly expresses this filter?

Question 54hardmulti select

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A Databricks team is building a retrieval corpus from mixed-format documents stored in a Unity Catalog volume. They need a preparation pipeline that preserves document structure for later chunking and that records which source file each chunk came from so retrieval results can cite evidence. Which two design choices best meet these requirements? (Choose two.)

Question 55hardmultiple choice

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A Generative AI engineer is preparing a Delta table of support tickets for embedding generation. The pipeline computes embeddings with ai_query and stores them in a separate embeddings table keyed by ticket_id. Tickets are frequently updated by agents, and the engineer wants the embeddings table to reflect the latest ticket text without recomputing embeddings for tickets whose text has not changed. Which design best achieves this?

Question 56hardmultiple choice

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A team is building a RAG application using Databricks Vector Search with a Delta table as the source. They need the index to automatically reflect new and updated chunks as the source table changes, without rebuilding the entire index each time. Which configuration should they use?

Question 57hardmulti select

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A team is preparing a large text corpus for embedding generation with a foundation model endpoint on Databricks. They must reduce token cost and improve retrieval quality before vectorization. Which two preprocessing steps should be applied to the raw text? (Choose two.)

Question 58easymultiple choice

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A data engineer needs to prepare a Delta table of customer reviews for embedding generation. The reviews contain HTML tags, inconsistent whitespace, and mixed casing that hurt embedding quality. Which preparation step should be applied before generating embeddings?

Question 59mediummulti select

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A data engineer is preparing a large corpus of support tickets stored in a Unity Catalog volume for fine-tuning a Llama model on Databricks. They must remove personally identifiable information (PII) before the data reaches the training cluster. Which TWO approaches are appropriate for detecting and redacting PII at scale in this pipeline? (Choose two.)

Question 60hardmulti select

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A GenAI engineer is preparing a large text corpus for fine-tuning an LLM. The corpus contains many near-duplicate documents and documents in multiple languages. They need to reduce redundancy and ensure language consistency. Which two steps should be performed during data preparation? (Choose two.)

Question 61hardmultiple choice

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A team is preparing a Delta table of product descriptions for a RAG application. The table receives continuous upserts from a streaming pipeline, and the embedding job reads the table every hour. Engineers notice the embedding job reprocesses every row on each run even though only a few rows change. Which change should be made to the source table to let the embedding job process only new or updated rows?

Question 62mediummulti select

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A data engineer is preparing a large Delta table of conversation logs for embedding generation. The table has frequent small appends, and the engineer needs to reduce file fragmentation and improve read throughput before the embedding job runs. Which two actions should the engineer take? (Choose two.)

Question 63mediummultiple choice

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A data engineer is assembling a fine-tuning dataset from a Delta table of conversation transcripts. Each transcript contains a list of message objects with a role and a content field, and the training job requires one row per conversation with the messages serialized into the expected format. Which transformation should the engineer apply?

Question 64mediummultiple choice

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A data engineer is preparing a dataset for fine-tuning a chat model. The dataset contains conversations with alternating user and assistant messages. They need to format each conversation into a single string with special tokens indicating roles. Which approach is most appropriate in Databricks?

Question 65hardmultiple choice

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A GenAI engineer prepares a Delta table of product descriptions that will feed a chunking and embedding pipeline. The descriptions are written in mixed languages, and the embedding model supports only English. The engineer needs to ensure that non-English rows are detected and routed for translation before embedding. Which approach is most appropriate?

Question 66mediummultiple choice

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A team is preparing a fine-tuning dataset from customer reviews stored in a Delta table. They need to filter out reviews shorter than 20 tokens and reviews flagged as spam by a classifier, then write the result to a Unity Catalog table for training. Which approach best fits Databricks best practices?

Question 67mediummultiple choice

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A GenAI engineer is preparing a fine-tuning dataset from a Delta table in Unity Catalog that contains raw user feedback. The feedback text includes irregular capitalization, HTML tags, and excessive punctuation. The engineer needs to normalize the text using Spark NLP within a Databricks notebook, ensuring the pipeline is reproducible and scalable. Which approach should the engineer use to apply this transformation?

Question 1mediummulti select

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A team is preparing data for a machine learning model and needs to handle missing values in a feature vector. Which TWO techniques are standard practices in Databricks for handling nulls in large-scale feature engineering pipelines?

Question 2hardmultiple choice

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A Data Engineer needs to handle schema evolution in a Delta Lake pipeline where source systems frequently add new columns. Which configuration ensures that the write operation automatically updates the target table schema without manual intervention?

Question 3mediummultiple choice

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When preparing data for a high-frequency streaming dashboard, a Data Engineer finds that the downstream Delta table is being flooded with small files, causing slow reads. Which tool should be used to rectify this without stopping the streaming job?

Question 4hardmulti select

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You are migrating a legacy ETL pipeline to Databricks. Which THREE of the following steps are considered best practices for optimizing data preparation in a Delta Lake environment?

Question 5hardmultiple choice

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Refer to the exhibit. You are implementing a data quality framework using Delta Live Tables. What happens when a record arrives with a null value in the 'id' column?

Exhibit

{
  "rule": "enforce_schema",
  "action": "drop_nulls",
  "target_col": "id",
  "fail_on_error": true
}

Question 6mediummultiple choice

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During data preparation, you observe severe data skew in a join operation. Which strategy is best for mitigating this skew?

Question 7hardmultiple choice

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Refer to the exhibit. The join fails due to ambiguous column names. How can you resolve this while keeping the 'id' column in the final result?

Exhibit

Error: AnalysisException: 'Multiple sources found for column id.'

val df1 = spark.table("table_a")
val df2 = spark.table("table_b")
val joined = df1.join(df2, "id")

Question 8mediummultiple choice

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A Data Engineer is using Databricks to prepare data for a machine learning model. The source data has significant imbalance. Which technique is most effective for addressing this imbalance during the preparation stage?

Question 9mediummultiple choice

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You are preparing a dataset by calculating a rolling average over a window of 30 days. Which Window function specification is required to ensure the average correctly considers only the preceding 30 days?

Question 10mediummultiple choice

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A data engineer is preparing a dataset for a LLM fine-tuning task. The raw data consists of millions of JSON files stored in Unity Catalog. Which approach provides the highest performance for loading this data into a PyTorch-based training pipeline?

Question 11hardmulti select

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Which TWO of the following steps are essential when cleaning unstructured text data for RAG applications within Databricks?

Question 12mediummultiple choice

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Refer to the exhibit. A data engineer is using a JSON-based configuration to prepare log data. Given the policy is 'strict_schema', what happens if an incoming log entry contains a field not defined in the Delta table schema?

Exhibit

{
  "policy": "strict_schema",
  "data_source": "s3://raw-logs/",
  "transformation": "regex_filter",
  "sink": "delta_table"
}

Question 13mediummulti select

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Which THREE techniques are commonly used to optimize data preparation for embedding generation in Databricks?

Question 14hardmultiple choice

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An engineer notices that a feature vector table in Delta is becoming excessively fragmented after frequent 'upserts'. Which action should be taken to optimize the table for future reads during inference?

Question 15hardmultiple choice

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Refer to the exhibit. You are appending new, streaming JSON logs to an existing Delta table. What is the most appropriate way to handle this schema evolution?

Exhibit

Error: PySparkException: [CANNOT_MERGE_INCOMPATIBLE_DATATYPES] Cannot merge incompatible data types 'String' and 'Integer'.

Question 16mediummultiple choice

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An enterprise engineering team is preparing unstructured PDF invoices stored in a Unity Catalog volume for a Retrieval-Augmented Generation pipeline. Which ingestion approach best aligns with Databricks-recommended data preparation practices for generative AI?

Question 17mediummultiple choice

Read the full Data Preparation explanation →

A data engineer is preparing a large set of policy PDFs stored in a Unity Catalog volume for a RAG application. Before generating embeddings, they must remove page headers, footers, and page numbers that repeat across nearly every page. Which approach is most appropriate for cleaning this text at scale on Databricks?

Question 18mediummultiple choice

Read the full Data Preparation explanation →

A GenAI team is preparing a fine-tuning dataset stored as a Delta table in Unity Catalog. They need to ensure that each training example is a valid JSON string conforming to a specific schema before feeding it to the model. They want to validate and optionally quarantine malformed records in a single pass. Which Databricks SQL function is most appropriate for this task?

Question 19hardmulti select

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A data engineer is preparing a large corpus of text for a RAG application. The text is stored in a Delta table with columns doc_id and content. The engineer needs to split the content into chunks of approximately 512 tokens with a 50-token overlap, and then compute embeddings for each chunk. They want to use Databricks-native capabilities to minimize data movement and cost. Which two approaches are valid and efficient for this task? (Choose two.)

Question 20easymultiple choice

Read the full Data Preparation explanation →

A data engineer is preparing a dataset for training a generative AI model. The dataset contains a column 'text' with raw user reviews. Before tokenization, the engineer needs to remove HTML tags and special characters, convert text to lowercase, and expand common contractions. Which Databricks feature or approach is best suited for this text normalization step?

Question 21mediummultiple choice

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A data engineer is preparing a dataset for a RAG application. The source data is a Delta table with a 'content' column containing long documents. To improve retrieval, they need to split each document into overlapping chunks of approximately 500 tokens. They want to use a built-in Databricks function that leverages a tokenizer to count tokens accurately. Which approach should they take?

Question 22hardmultiple choice

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A team prepares customer support transcripts for fine-tuning. Each transcript is a JSON record with a nested messages array, and approximately 8 percent of records contain a null role field. The team wants records with malformed messages to be quarantined rather than silently dropped. Which Delta Live Tables configuration accomplishes this?

Question 23mediummultiple choice

Read the full Data Preparation explanation →

A data engineer is building a RAG pipeline and needs to store document embeddings in Databricks Vector Search. The embeddings are computed from text chunks and stored in a Delta table with columns chunk_id, embedding (array of floats), and metadata. The engineer wants to enable fast similarity search with metadata filtering. Which step is required to make this Delta table searchable in Databricks Vector Search?

Question 24mediummultiple choice

Read the full Data Preparation explanation →

A generative AI team stores raw support tickets in a Unity Catalog volume as newline-delimited JSON. Before fine-tuning, they must convert the nested arrays into a flat table and enforce the ticket schema on every read so malformed records fail fast instead of silently producing nulls. Which approach best satisfies both requirements?

Question 25mediummultiple choice

Read the full Data Preparation explanation →

A GenAI engineer has a Delta table of 50 million customer support transcripts in Unity Catalog, each containing a raw_text column. Before generating embeddings with a foundation model endpoint, they must split each transcript into ~500-token chunks and preserve the source document_id and chunk index. Which Databricks-native approach should they use to produce a new Delta table of chunked records at scale?

Question 26easymultiple choice

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An engineer is building a training set from a Delta table in Unity Catalog. The table contains a free-text column with customer emails, and the fine-tuning data must not expose personal data. Which approach satisfies the requirement while keeping the data usable for model training?

Question 27easymultiple choice

Read the full Data Preparation explanation →

A data engineer needs to convert a Delta table of raw HTML product descriptions into clean plain text for embedding generation. The cleaning must remove HTML tags, collapse whitespace, and normalize Unicode. Which Databricks capability should they use to apply this transformation across millions of rows?

Question 28mediummultiple choice

Read the full Data Preparation explanation →

A Data Engineer needs to ensure that PII data in a Delta table is masked before serving it to non-privileged users. Which Databricks feature provides the most efficient, centralized control for this requirement?

Question 29mediummultiple choice

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You need to ingest data from an external JSON source into a Delta table. The source schema is inconsistent. Which strategy is most effective for preparing this data?

Question 30mediummulti select

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Which TWO of the following are benefits of using Delta Lake for data preparation over standard Parquet files on cloud storage?

Question 31easymultiple choice

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Which Databricks feature is best suited for maintaining the lineage of data used during the preparation of training sets for Generative AI?

Question 32mediummultiple choice

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When preparing a dataset for fine-tuning an LLM, you need to ensure the data is representative of the target domain. What is the most effective approach to detect and mitigate sampling bias in your training set using Databricks?

Question 33mediummultiple choice

Read the full Data Preparation explanation →

Which of the following describes the purpose of using a 'Feature Store' when preparing data for Generative AI applications?

Question 34mediummultiple choice

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You are preparing a large dataset for fine-tuning a model using Databricks Delta Live Tables (DLT). Which configuration is best for ensuring data quality and lineage in this pipeline?

Question 35hardmulti select

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Which TWO factors are most important when selecting a chunking strategy for text data prior to vectorization?

Question 36mediummultiple choice

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A team is preparing data for a RAG system and needs to remove duplicates from a large collection of PDF text extracts. What is the most efficient way to perform de-duplication in Databricks?

Question 37easymultiple choice

Read the full Data Preparation explanation →

Which of the following describes the 'Gold' layer in a Medallion architecture, and why is it important for GenAI data preparation?

Question 38hardmultiple choice

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You need to store embeddings generated by an LLM in a Delta table. Which data type is most efficient for storing these high-dimensional vector arrays in Databricks?

Question 39mediummultiple choice

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When preparing data for a fine-tuning task, you realize the dataset is severely imbalanced. Which Databricks technique should you use to create a more balanced dataset?

Question 40mediummultiple choice

Read the full Data Preparation explanation →

A GenAI engineer is preparing a large text corpus stored in a Unity Catalog volume for fine-tuning a chat model. The raw files are JSON Lines, each containing a 'conversation' array with alternating 'user' and 'assistant' turns, but many records have malformed turns or missing roles. The engineer needs to convert this into a Delta table with a schema of (conversation_id STRING, messages ARRAY<STRUCT<role:STRING, content:STRING>>) while filtering out records where any turn has a null role or empty content. Which approach uses the appropriate Databricks-native capability for this transformation?

Question 41mediummultiple choice

Read the full Data Preparation explanation →

You are preparing a large corpus of customer support emails stored as Parquet files in a Unity Catalog volume. The emails must be cleaned by removing boilerplate signatures and disclaimers before tokenization for a fine-tuning dataset. Your team wants to enforce this transformation as a declarative, testable pipeline stage that fails fast if any cleaned record still contains a known boilerplate marker. Which Databricks capability should you use to implement this cleaning step with built-in data quality expectations?

Question 42easymultiple choice

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A data engineer is preparing a dataset of product descriptions for embedding generation. The source table in Unity Catalog contains a column 'description' with mixed languages, and the team wants to filter to English-only text before vectorization. They need a scalable, built-in Databricks function that can detect the language of each description without external API calls. Which function should they use?

Question 43mediummultiple choice

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A GenAI engineer is preparing a corpus of HTML product pages stored in a Unity Catalog volume for a RAG application. The pages contain navigation bars, script tags, and boilerplate footers that add noise to embeddings. Which Databricks-native approach best removes this noise while preserving the main article text before chunking?

Question 44hardmulti select

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You are preparing a large text corpus for fine-tuning a generative AI model. The corpus is stored in a Delta table with columns: doc_id, raw_text, and metadata. You need to create a cleaned dataset that removes personally identifiable information (PII) and normalizes whitespace, while preserving document boundaries for training. Which two actions should you perform to achieve this in a scalable and maintainable way? (Choose two.)

Question 45mediummultiple choice

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A GenAI engineer must build a fine-tuning dataset from 40 TB of raw JSONL conversation logs stored in cloud object storage. The logs are immutable and only ever read once, in full, during preprocessing. Which storage and access configuration should be used to minimize cost while keeping the data readable by Spark on Databricks?

Question 46mediummultiple choice

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A Generative AI engineer is building a Delta Live Tables pipeline that ingests raw JSON event logs into a bronze table, then uses ai_query to classify each event's free-text field. The classification call is expensive, so the engineer wants to avoid re-running it on events that have already been processed in previous pipeline updates. The source table is append-only and new events arrive continuously. Which Delta Live Tables feature should the engineer configure on the bronze table to prevent reprocessing of previously ingested rows?

Question 47hardmultiple choice

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A team is preparing a pretraining corpus and must filter out near-duplicate documents before tokenization. The corpus contains 500 million short text records in a Delta table. They want a scalable, deterministic deduplication signal that can be computed per record and compared across the dataset. Which approach best fits this requirement?

Question 48hardmultiple choice

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A data engineer is preparing a large corpus of customer support transcripts stored as Parquet in a Unity Catalog volume. Before generating embeddings, each transcript must be tokenized and truncated to a maximum token length. The engineer wants to use a Databricks-native approach that runs distributed across the cluster and avoids pulling the full corpus into a single node. Which approach best satisfies these requirements?

Question 49mediummulti select

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A Generative AI engineer is preparing a Delta table of product reviews for a retrieval-augmented generation application. The reviews contain HTML tags, inconsistent casing, and occasional very long paragraphs that exceed the embedding model's context window. The engineer wants to clean and normalize the text before chunking and embedding. Which two actions should the engineer take to directly address the stated quality issues? (Choose two.)

Question 50easymultiple choice

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A data engineer needs to persist a prepared instruction-tuning dataset so that downstream fine-tuning jobs can read it with ACID guarantees, time travel, and schema enforcement, and so that Unity Catalog can track column-level lineage. Which storage format and registration should be used?

Question 51easymultiple choice

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A team stores raw documents in a Unity Catalog volume and needs to build a training set for fine-tuning a large language model. The raw files are in mixed formats including PDF, DOCX, and plain text. The team wants a single Delta table where every row is one document with its extracted text and source path, and wants the extraction to run in parallel across the cluster. Which approach should the team use?

Question 52hardmultiple choice

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You are building a data preparation pipeline for a generative AI application. The raw data is stored in a Unity Catalog volume as JSON files with nested fields. You need to flatten the nested structure and extract specific fields into a Delta table for downstream embedding. The JSON schema may evolve, with new fields added occasionally. Which approach provides the most robust and maintainable solution?

Question 53mediummultiple choice

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An engineer is cleaning a Delta table of customer support transcripts stored in Unity Catalog. The table has a nested array column named messages, where each element has role and content fields. They need to keep only rows where at least one message has role equal to 'user' and content longer than 20 characters. Which transformation correctly expresses this filter?

Question 54hardmulti select

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A Databricks team is building a retrieval corpus from mixed-format documents stored in a Unity Catalog volume. They need a preparation pipeline that preserves document structure for later chunking and that records which source file each chunk came from so retrieval results can cite evidence. Which two design choices best meet these requirements? (Choose two.)

Question 55hardmultiple choice

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A Generative AI engineer is preparing a Delta table of support tickets for embedding generation. The pipeline computes embeddings with ai_query and stores them in a separate embeddings table keyed by ticket_id. Tickets are frequently updated by agents, and the engineer wants the embeddings table to reflect the latest ticket text without recomputing embeddings for tickets whose text has not changed. Which design best achieves this?

Question 56hardmultiple choice

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A team is building a RAG application using Databricks Vector Search with a Delta table as the source. They need the index to automatically reflect new and updated chunks as the source table changes, without rebuilding the entire index each time. Which configuration should they use?

Question 57hardmulti select

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A team is preparing a large text corpus for embedding generation with a foundation model endpoint on Databricks. They must reduce token cost and improve retrieval quality before vectorization. Which two preprocessing steps should be applied to the raw text? (Choose two.)

Question 58easymultiple choice

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A data engineer needs to prepare a Delta table of customer reviews for embedding generation. The reviews contain HTML tags, inconsistent whitespace, and mixed casing that hurt embedding quality. Which preparation step should be applied before generating embeddings?

Question 59mediummulti select

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A data engineer is preparing a large corpus of support tickets stored in a Unity Catalog volume for fine-tuning a Llama model on Databricks. They must remove personally identifiable information (PII) before the data reaches the training cluster. Which TWO approaches are appropriate for detecting and redacting PII at scale in this pipeline? (Choose two.)

Question 60hardmulti select

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A GenAI engineer is preparing a large text corpus for fine-tuning an LLM. The corpus contains many near-duplicate documents and documents in multiple languages. They need to reduce redundancy and ensure language consistency. Which two steps should be performed during data preparation? (Choose two.)

Question 61hardmultiple choice

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A team is preparing a Delta table of product descriptions for a RAG application. The table receives continuous upserts from a streaming pipeline, and the embedding job reads the table every hour. Engineers notice the embedding job reprocesses every row on each run even though only a few rows change. Which change should be made to the source table to let the embedding job process only new or updated rows?

Question 62mediummulti select

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A data engineer is preparing a large Delta table of conversation logs for embedding generation. The table has frequent small appends, and the engineer needs to reduce file fragmentation and improve read throughput before the embedding job runs. Which two actions should the engineer take? (Choose two.)

Question 63mediummultiple choice

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A data engineer is assembling a fine-tuning dataset from a Delta table of conversation transcripts. Each transcript contains a list of message objects with a role and a content field, and the training job requires one row per conversation with the messages serialized into the expected format. Which transformation should the engineer apply?

Question 64mediummultiple choice

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A data engineer is preparing a dataset for fine-tuning a chat model. The dataset contains conversations with alternating user and assistant messages. They need to format each conversation into a single string with special tokens indicating roles. Which approach is most appropriate in Databricks?

Question 65hardmultiple choice

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A GenAI engineer prepares a Delta table of product descriptions that will feed a chunking and embedding pipeline. The descriptions are written in mixed languages, and the embedding model supports only English. The engineer needs to ensure that non-English rows are detected and routed for translation before embedding. Which approach is most appropriate?

Question 66mediummultiple choice

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A team is preparing a fine-tuning dataset from customer reviews stored in a Delta table. They need to filter out reviews shorter than 20 tokens and reviews flagged as spam by a classifier, then write the result to a Unity Catalog table for training. Which approach best fits Databricks best practices?

Question 67mediummultiple choice

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A GenAI engineer is preparing a fine-tuning dataset from a Delta table in Unity Catalog that contains raw user feedback. The feedback text includes irregular capitalization, HTML tags, and excessive punctuation. The engineer needs to normalize the text using Spark NLP within a Databricks notebook, ensuring the pipeline is reproducible and scalable. Which approach should the engineer use to apply this transformation?