Databricks DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE Online Practice Questions and Exam Preparation

Databricks DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE Online Questions & Answers

• Question 101:

  Which of the following statements regarding the relationship between Silver tables and Bronze tables is always true?

  A. Silver tables contain a less refined, less clean view of data than Bronze data.
  B. Silver tables contain aggregates while Bronze data is unaggregated.
  C. Silver tables contain more data than Bronze tables.
  D. Silver tables contain a more refined and cleaner view of data than Bronze tables.
  E. Silver tables contain less data than Bronze tables.
  D. Silver tables contain a more refined and cleaner view of data than Bronze tables.

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  Explanation

  In a medallion architecture, a common data design pattern for lakehouses, data flows from Bronze to Silver to Gold layer tables, with each layer progressively improving the structure and quality of data. Bronze tables store raw data ingested

  from various sources, while Silver tables apply minimal transformations and cleansing to create an enterprise view of the data. Silver tables can also join and enrich data from different Bronze tables to provide a more complete and consistent

  view of the data. Therefore, option D is the correct answer, as Silver tables contain a more refined and cleaner view of data than Bronze tables. Option A is incorrect, as it is the opposite of the correct answer. Option B is incorrect, as Silver

  tables do not necessarily contain aggregates, but can also store detailed records. Option C is incorrect, as Silver tables may contain less data than Bronze tables, depending on the transformations and cleansing applied. Option E is incorrect,

  as Silver tables may contain more data than Bronze tables, depending on the joins and enrichments applied.

  References: What is a Medallion Architecture?, Transforming Bronze Tables in Silver Tables, What is the medallion lakehouse architecture?

• Question 102:

  Which command removes unused data files in Delta tables?

  A. DELETE
  B. TRUNCATE
  C. OPTIMIZE
  D. VACUUM
  E. PURGE
  D. VACUUM

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  Explanation

  VACUUM deletes old, unreferenced files based on retention thresholds.

• Question 103:

  An organization is looking for an optimized storage layer that supports ACID transactions and schema enforcement. Which technology should the organization use?

  A. Delta Lake
  B. Unity Catalog
  C. Cloud File Storage
  D. Data lake
  A. Delta Lake

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  Explanation

  Delta Lake is the optimized storage layer that adds ACID transactions, schema enforcement, and reliability on top of cloud object storage, making it ideal for managing large-scale data workloads.

• Question 104:

  Which command appends a new record to an existing Delta table?

  A. MERGE INTO
  B. UPSERT
  C. INSERT INTO
  D. CREATE TABLE
  E. UPDATE
  C. INSERT INTO

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  Explanation

  INSERT INTO is used to add new rows to an existing table.

• Question 105:

  A data engineer has developed a data pipeline to ingest data from a JSON source using Auto Loader, but the engineer has not provided any type inference or schema hints in their pipeline. Upon reviewing the data, the data engineer has noticed that all of the columns in the target table are of the string type despite some of the fields only including float or boolean values.

  Which of the following describes why Auto Loader inferred all of the columns to be of the string type?

  A. There was a type mismatch between the specific schema and the inferred schema
  B. JSON data is a text-based format
  C. Auto Loader only works with string data
  D. All of the fields had at least one null value
  E. Auto Loader cannot infer the schema of ingested data
  B. JSON data is a text-based format

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  Explanation

  JSON data is a text-based format that represents data as a collection of name-value pairs. By default, when Auto Loader infers the schema of JSON data, it treats all columns as strings. This is because JSON data can have varying data types for the same column across different files or records, and Auto Loader does not attempt to reconcile these differences. For example, a column named "age" may have integer values in some files, but string values in others. To avoid data loss or errors, Auto Loader infers the column as a string type. However, Auto Loader also provides an option to infer more precise column types based on the sample data. This option is called cloudFiles.inferColumnTypes and it can be set to true or false. When set to true, Auto Loader tries to infer the exact data types of the columns, such as integers, floats, booleans, or nested structures. When set to false, Auto Loader infers all columns as strings. The default value of this option is false.

  References: Configure schema inference and evolution in Auto Loader, Schema inference with auto loader (non-DLT and DLT), Using and Abusing Auto Loader's Inferred Schema, Explicit path to data or a defined schema required for Auto loader.

• Question 106:

  Which method should a Data Engineer apply to ensure Workflows are being triggered on schedule?

  A. Scheduled Workflows require an always-running cluster, which is more expensive but reduces processing latency.
  B. Scheduled Workflows process data as it arrives at configured sources.
  C. Scheduled Workflows can reduce resource consumption and expense since the cluster runs only long enough to execute the pipeline.
  D. Scheduled Workflows run continuously until manually stopped.
  C. Scheduled Workflows can reduce resource consumption and expense since the cluster runs only long enough to execute the pipeline.

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  Explanation

• Question 107:

  A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.

  Which of the following tools can the data engineer use to solve this problem?

  A. Unity Catalog
  B. Delta Lake
  C. Databricks SQL
  D. Data Explorer
  E. Auto Loader
  E. Auto Loader

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  Explanation

  Auto Loader is a tool that can incrementally and efficiently process new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a Structured Streaming source called cloudFiles, which automatically detects and processes new files in a given input directory path on the cloud file storage. Auto Loader also tracks the ingestion progress and ensures exactly-once semantics when writing data into Delta Lake. Auto Loader can ingest various file formats, such as JSON, CSV, XML, PARQUET, AVRO, ORC, TEXT, and BINARYFILE. Auto Loader has support for both Python and SQL in Delta Live Tables, which are a declarative way to build production-quality data pipelines with Databricks.

  References: What is Auto Loader?, Get started with Databricks Auto Loader, Auto Loader in Delta Live Tables

• Question 108:

  How does Databricks support time travel for Delta tables?

  A. By copying tables hourly
  B. Through transaction logs
  C. Using snapshot backups
  D. With real-time syncs
  E. Using table clones
  B. Through transaction logs

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  Explanation

  Delta Lake uses transaction logs to support versioned queries and time travel.

• Question 109:

  What happens when a managed Delta table is dropped?

  A. Metadata is removed, data is kept
  B. Only permissions are removed
  C. Both metadata and data are deleted
  D. Only table history is deleted
  E. Cluster is restarted
  C. Both metadata and data are deleted

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  Explanation

  Dropping a managed table deletes both metadata and underlying data files.

• Question 110:

  A team's Databricks SQL endpoint runs queries slowly when many users run small queries at the same time. Which configuration change can improve performance?

  A. Enable Auto Stop
  B. Use Job Clusters
  C. Increase maximum bound of scaling range
  D. Use larger Photon-enabled nodes
  E. Set warehouse to manual scaling
  C. Increase maximum bound of scaling range

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  Explanation

  Increasing the upper scaling bound allows the SQL warehouse to spin up additional clusters to handle concurrency efficiently.