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

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

• Question 171:

  A data engineer needs to access the view created by the sales team, using a shared cluster.

  The data engineer has been provided usage permissions on the catalog and schema. In order to access the view created by sales team.

  What are the minimum permissions the data engineer would require in addition?

  A. Needs SELECT permission on the VIEW and the underlying TABLE.
  B. Needs SELECT permission only on the VIEW
  C. Needs ALL PRIVILEGES on the VIEW
  D. Needs ALL PRIVILEGES at the SCHEMA level
  A. Needs SELECT permission on the VIEW and the underlying TABLE.

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  Explanation

• Question 172:

  A data engineer needs to ingest from both streaming and batch sources for a firm that relies on highly accurate data. Occasionally, some of the data picked up by the sensors that provide a streaming input are outside the expected parameters. If this occurs, the data must be dropped, but the stream should not fail.

  Which feature of Delta Live Tables meets this requirement?

  A. Change Data Capture
  B. Error Handling
  C. Monitoring
  D. Expectations
  D. Expectations

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  Explanation

  Delta Live Tables Expectations allow defining data quality rules. Records that fail the expectations (such as sensor readings outside valid ranges) can be dropped without stopping the streaming pipeline, ensuring continuous and accurate processing.

• Question 173:

  Which of the following approaches should be used to send the Databricks Job owner an email in the case that the Job fails?

  A. Manually programming in an alert system in each cell of the Notebook
  B. Setting up an Alert in the Job page
  C. Setting up an Alert in the Notebook
  D. There is no way to notify the Job owner in the case of Job failure
  E. MLflow Model Registry Webhooks
  B. Setting up an Alert in the Job page

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  Explanation

  To send the Databricks Job owner an email in the case that the Job fails, the best approach is to set up an Alert in the Job page. This way, the Job owner can configure the email address and the notification type for the Job failure event. The other options are either not feasible, not reliable, or not relevant for this task. Manually programming an alert system in each cell of the Notebook is tedious and error-prone. Setting up an Alert in the Notebook is not possible, as Alerts are only available for Jobs and Clusters. There is a way to notify the Job owner in the case of Job failure, so option D is incorrect. MLflow Model Registry Webhooks are used for model lifecycle events, not Job events, so option E is not applicable.

  References: Add email and system notifications for job events Alerts MLflow Model Registry Webhooks

• Question 174:

  Which type of workloads are compatible with Auto Loader?

  A. Streaming workloads
  B. Machine learning workloads
  C. Serverless workloads
  D. Batch workloads
  A. Streaming workloads

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  Explanation

• Question 175:

  Which of the following describes the storage organization of a Delta table?

  A. Delta tables are stored in a single file that contains data, history, metadata, and other attributes.
  B. Delta tables store their data in a single file and all metadata in a collection of files in a separate location.
  C. Delta tables are stored in a collection of files that contain data, history, metadata, and other attributes.
  D. Delta tables are stored in a collection of files that contain only the data stored within the table.
  E. Delta tables are stored in a single file that contains only the data stored within the table.
  C. Delta tables are stored in a collection of files that contain data, history, metadata, and other attributes.

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  Explanation

  Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse. Delta Lake is open source software that extends Parquet data files with a file-based transaction log for ACID transactions and scalable metadata handling1. Delta Lake stores its data and metadata in a collection of files in a directory on a cloud storage system, such as AWS S3 or Azure Data Lake Storage2. Each Delta table has a transaction log that records the history of operations performed on the table, such as insert, update, delete, merge, etc. The transaction log also stores the schema and partitioning information of the table2. The transaction log enables Delta Lake to provide ACID guarantees, time travel, schema enforcement, and other features.

  References: What is Delta Lake? | Databricks on AWS Quickstart -- Delta Lake Documentation

• Question 176:

  Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?

  A. The ability to manipulate the same data using a variety of languages
  B. The ability to collaborate in real time on a single notebook
  C. The ability to set up alerts for query failures
  D. The ability to support batch and streaming workloads
  E. The ability to distribute complex data operations
  D. The ability to support batch and streaming workloads

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  Explanation

  Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse. Delta Lake is fully compatible with Apache Spark APIs, and was developed for tight integration with Structured

  Streaming, allowing you to easily use a single copy of data for both batch and streaming operations and providing incremental processing at scale1. Delta Lake supports upserts using the merge operation, which enables you to efficiently

  update existing data or insert new data into your Delta tables2. Delta Lake also provides time travel capabilities, which allow you to query previous versions of your data or roll back to a specific point in time3.

  References:

  1: What is Delta Lake? | Databricks on AWS

  2: Upsert into a table using merge | Databricks on AWS

  3: [Query an older snapshot of a table (time travel) | Databricks on AWS] Learn more 1blob:https://www.bing.com/a746b4b4-48d0-4f44-9736-44d1ce0c4228 learn.microsoft.com2blob: https://www.bing.com/525fbb0f-e02f-4a70-8085-22c065fe0ca0 medium.com3blob: https://www.bing.com/5cb5bd07-1008-4cf7-9fa3-42a5a689c7d5 slideshare.net4blob: https://www.bing.com/9a7e8352-30c1-4356-a73f-a7253b607ef7 docs.databricks.com5blob: https://www.bing.com/3f65cc27-d573-4810-b272-01238a431c03 github.com6blob: https://www.bing.com/334f6880-dfeb-4e61-bd9a-76efae0a2d01 key2consulting.com

• Question 177:

  Which file format is primarily used to store Delta tables in Databricks?

  A. CSV
  B. JSON
  C. Avro
  D. Parquet
  E. Delta
  D. Parquet

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  Explanation

  Delta Lake uses Parquet as the base format with an additional transaction log.

• Question 178:

  Which two items are characteristics of the Gold Layer? (Choose two.)

  A. Historical lineage
  B. Raw Data
  C. Normalised
  D. De-normalised
  E. Read-optimized
  D. De-normalised
  E. Read-optimized

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  Explanation

  The Gold layer contains de-normalized, business-level tables that are read-optimized for analytics, dashboards, and reporting, providing fast and simplified data access.

• Question 179:

  A data engineer has realized that the data files associated with a Delta table are incredibly small. They want to compact the small files to form larger files to improve performance.

  Which keyword can be used to compact the small files?

  A. OPTIMIZE
  B. VACUUM
  C. COMPACTION
  D. REPARTITION
  A. OPTIMIZE

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  Explanation

• Question 180:

  Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?

  A. Parquet files can be partitioned
  B. CREATE TABLE AS SELECT statements cannot be used on files
  C. Parquet files have a well-defined schema
  D. Parquet files have the ability to be optimized
  E. Parquet files will become Delta tables
  C. Parquet files have a well-defined schema

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  Explanation

  Option C is the correct answer because Parquet files have a well-defined schema that is embedded within the data itself. This means that the data types and column names of the Parquet files are automatically detected and preserved when creating an external table from them. This also enables the use of SQL and other structured query languages to access and analyze the data. CSV files, on the other hand, do not have a schema embedded in them, and require specifying the schema explicitly or inferring it from the data when creating an external table from them. This can lead to errors or inconsistencies in the data types and column names, and also increase the processing time and complexity.

  References: CREATE TABLE AS SELECT, Parquet Files, CSV Files, Parquet vs. CSV