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Real Databricks Databricks-Certified-Data-Engineer-Associate Exam Questions [Updated 2024]
The GAQM Databricks-Certified-Data-Engineer-Associate exam is a rigorous and challenging certification that requires candidates to have a strong foundation in data engineering principles and practices. It is recommended that candidates have at least 2-3 years of experience working with big data technologies before attempting the exam. However, there are training courses and study materials available to help candidates prepare for the exam and gain the necessary skills and knowledge.
Databricks Certified Data Engineer Associate certification is essential for individuals who want to work with big data and build scalable data pipelines. Databricks Certified Data Engineer Associate Exam certification exam covers a range of topics, including data modeling, data ingestion, data transformation, data storage, and data processing. Candidates will also be tested on their knowledge of Apache Spark, a popular open-source data processing engine, and Databricks Unified Analytics Platform.
NEW QUESTION # 31
Which of the following describes a scenario in which a data team will want to utilize cluster pools?
- A. An automated report needs to be tested to identify errors.
- B. An automated report needs to be runnable by all stakeholders.
- C. An automated report needs to be version-controlled across multiple collaborators.
- D. An automated report needs to be made reproducible.
- E. An automated report needs to be refreshed as quickly as possible.
Answer: E
Explanation:
Explanation
Cluster pools are typically used in distributed computing environments, such as cloud-based data platforms like Databricks. They allow you to pre-allocate a set of compute resources (a cluster) for specific tasks or workloads. In this case, if an automated report needs to be refreshed as quickly as possible, you can allocate a cluster pool with sufficient resources to ensure fast data processing and report generation. This helps ensure that the report is generated with minimal latency and can be delivered to stakeholders in a timely manner.
Cluster pools allow you to optimize resource allocation for high-demand, time-sensitive tasks like real-time report generation.
NEW QUESTION # 32
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Production mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?
- A. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
- B. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped.
- C. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
- D. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
- E. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
Answer: B
Explanation:
Explanation
In a Delta Live Table pipeline running in Continuous Pipeline Mode, when you click Start to update the pipeline, the following outcome is expected: All datasets defined using STREAMING LIVE TABLE and LIVE TABLE against Delta Lake table sources will be updated at set intervals. The compute resources will be deployed for the update process and will be active during the execution of the pipeline. The compute resources will be terminated when the pipeline is stopped or shut down. This mode allows for continuous and periodic updates to the datasets as new data arrives or changes in the underlying Delta Lake tables occur. The compute resources are provisioned and utilized during the update intervals to process the data and perform the necessary operations.
NEW QUESTION # 33
Which of the following benefits is provided by the array functions from Spark SQL?
- A. An ability to work with data within certain partitions and windows
- B. An ability to work with time-related data in specified intervals
- C. An ability to work with complex, nested data ingested from JSON files
- D. An ability to work with an array of tables for procedural automation
- E. An ability to work with data in a variety of types at once
Answer: C
Explanation:
Explanation
Array functions in Spark SQL are primarily used for working with arrays and complex, nested data structures, such as those often encountered when ingesting JSON files. These functions allow you to manipulate and query nested arrays and structures within your data, making it easier to extract and work with specific elements or values within complex data formats. While some of the other options (such as option A for working with different data types) are features of Spark SQL or SQL in general, array functions specifically excel at handling complex, nested data structures like those found in JSON files.
NEW QUESTION # 34
A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following commands could the data engineering team use to access sales in PySpark?
- A. spark.delta.table("sales")
- B. spark.sql("sales")
- C. spark.table("sales")
- D. SELECT * FROM sales
- E. There is no way to share data between PySpark and SQL.
Answer: A
NEW QUESTION # 35
A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task.
Which of the following approaches can the data engineer use to set up the new task?
- A. They can create a new job from scratch and add both tasks to run concurrently.
- B. They can create a new task in the existing Job and then add the original task as a dependency of the new task.
- C. They can clone the existing task in the existing Job and update it to run the new notebook.
- D. They can create a new task in the existing Job and then add it as a dependency of the original task.
- E. They can clone the existing task to a new Job and then edit it to run the new notebook.
Answer: D
Explanation:
Explanation
To set up the new task to run a new notebook prior to the original task in a single-task Job, the data engineer can use the following approach: In the existing Job, create a new task that corresponds to the new notebook that needs to be run. Set up the new task with the appropriate configuration, specifying the notebook to be executed and any necessary parameters or dependencies. Once the new task is created, designate it as a dependency of the original task in the Job configuration. This ensures that the new task is executed before the original task.
NEW QUESTION # 36
A data engineer is attempting to drop a Spark SQL table my_table. The data engineer wants to delete all table metadata and data.
They run the following command:
DROP TABLE IF EXISTS my_table
While the object no longer appears when they run SHOW TABLES, the data files still exist.
Which of the following describes why the data files still exist and the metadata files were deleted?
- A. The table's data was larger than 10 GB
- B. The table's data was smaller than 10 GB
- C. The table did not have a location
- D. The table was external
- E. The table was managed
Answer: D
Explanation:
Explanation
The reason why the data files still exist while the metadata files were deleted is because the table was external.
When a table is external in Spark SQL (or in other database systems), it means that the table metadata (such as schema information and table structure) is managed externally, and Spark SQL assumes that the data is managed and maintained outside of the system. Therefore, when you execute a DROP TABLE statement for an external table, it removes only the table metadata from the catalog, leaving the data files intact. On the other hand, for managed tables (option E), Spark SQL manages both the metadata and the data files. When you drop a managed table, it deletes both the metadata and the associated data files, resulting in a complete removal of the table.
NEW QUESTION # 37
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?
- A. The ability to set up alerts for query failures
- B. The ability to distribute complex data operations
- C. The ability to manipulate the same data using a variety of languages
- D. The ability to support batch and streaming workloads
- E. The ability to collaborate in real time on a single notebook
Answer: D
NEW QUESTION # 38
Which of the following describes a scenario in which a data engineer will want to use a single-node cluster?
- A. When they are concerned about the ability to automatically scale with larger data
- B. When they are manually running reports with a large amount of data
- C. When they are working interactively with a small amount of data
- D. When they are working with SQL within Databricks SQL
- E. When they are running automated reports to be refreshed as quickly as possible
Answer: C
Explanation:
Explanation
A Single Node cluster is a cluster consisting of an Apache Spark driver and no Spark workers. A Single Node cluster supports Spark jobs and all Spark data sources, including Delta Lake. A Standard cluster requires a minimum of one Spark worker to run Spark jobs.
NEW QUESTION # 39
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. JSON data is a text-based format
- B. Auto Loader only works with string data
- C. All of the fields had at least one null value
- D. There was a type mismatch between the specific schema and the inferred schema
- E. Auto Loader cannot infer the schema of ingested data
Answer: A
Explanation:
Explanation
JSON data is a text-based format that uses strings to represent all values. When Auto Loader infers the schema of JSON data, it assumes that all values are strings. This is because Auto Loader cannot determine the type of a value based on its string representation. https://docs.databricks.com/en/ingestion/auto-loader/schema.html Forexample, the following JSON string represents a value that is logically a boolean: JSON "true" Use code with caution. Learn more However, Auto Loader would infer that the type of this value is string. This is because Auto Loader cannot determine that the value is a boolean based on its string representation. In order to get Auto Loader to infer the correct types for columns, the data engineer can provide type inference or schema hints. Type inference hints can be used to specify the types of specific columns. Schema hints can be used to provide the entire schema of the data. Therefore, the correct answer is B. JSON data is a text-based format.
NEW QUESTION # 40
A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos.
Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?
- A. Databricks Repos is wholly housed within the Databricks Lakehouse Platform
- B. Databricks Repos allows users to revert to previous versions of a notebook
- C. Databricks Repos automatically saves development progress
- D. Databricks Repos provides the ability to comment on specific changes
- E. Databricks Repos supports the use of multiple branches
Answer: E
NEW QUESTION # 41
A data engineer is maintaining a data pipeline. Upon data ingestion, the data engineer notices that the source data is starting to have a lower level of quality. The data engineer would like to automate the process of monitoring the quality level.
Which of the following tools can the data engineer use to solve this problem?
- A. Delta Live Tables
- B. Delta Lake
- C. Data Explorer
- D. Auto Loader
- E. Unity Catalog
Answer: B
NEW QUESTION # 42
A data analyst has developed a query that runs against Delta table. They want help from the data engineering team to implement a series of tests to ensure the data returned by the query is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following operations could the data engineering team use to run the query and operate with the results in PySpark?
- A. spark.table
- B. spark.sql
- C. spark.delta.table
- D. SELECT * FROM sales
- E. There is no way to share data between PySpark and SQL.
Answer: B
Explanation:
Explanation
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
df = spark.sql("SELECT * FROM sales")
print(df.count())
NEW QUESTION # 43
In order for Structured Streaming to reliably track the exact progress of the processing so that it can handle any kind of failure by restarting and/or reprocessing, which of the following two approaches is used by Spark to record the offset range of the data being processed in each trigger?
- A. Write-ahead Logs and Idempotent Sinks
- B. Checkpointing and Idempotent Sinks
- C. Structured Streaming cannot record the offset range of the data being processed in each trigger.
- D. Checkpointing and Write-ahead Logs
- E. Replayable Sources and Idempotent Sinks
Answer: D
Explanation:
Explanation
The engine uses checkpointing and write-ahead logs to record the offset range of the data being processed in each trigger. -- in the link search for "The engine uses " youll find the answer.https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#:~:text=The%20engin
NEW QUESTION # 44
A data engineer has a Job with multiple tasks that runs nightly. Each of the tasks runs slowly because the clusters take a long time to start.
Which of the following actions can the data engineer perform to improve the start up time for the clusters used for the Job?
- A. They can use endpoints available in Databricks SQL
- B. They can configure the clusters to be single-node
- C. They can configure the clusters to autoscale for larger data sizes
- D. They can use clusters that are from a cluster pool
- E. They can use jobs clusters instead of all-purpose clusters
Answer: E
NEW QUESTION # 45
A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).
Which of the following code blocks creates this SQL UDF?
- A.
- B.
- C.
- D.
- E.
Answer: B
Explanation:
Explanation
https://www.databricks.com/blog/2021/10/20/introducing-sql-user-defined-functions.html
NEW QUESTION # 46
A data engineer wants to create a new table containing the names of customers that live in France.
They have written the following command:
A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (PII).
Which of the following lines of code fills in the above blank to successfully complete the task?
- A. PII
- B. TBLPROPERTIES PII
- C. "COMMENT PII"
- D. There is no way to indicate whether a table contains PII.
- E. COMMENT "Contains PII"
Answer: E
Explanation:
Explanation
Ref:https://www.databricks.com/discover/pages/data-quality-management
CREATE TABLE my_table (id INT COMMENT 'Unique Identification Number', name STRING COMMENT 'PII', age INT COMMENT 'PII') TBLPROPERTIES ('contains_pii'=True) COMMENT 'Contains PII';
NEW QUESTION # 47
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?
- A.
- B.
- C.
- D.
- E.
Answer: C
NEW QUESTION # 48
Which of the following data workloads will utilize a Gold table as its source?
- A. A job that aggregates uncleaned data to create standard summary statistics
- B. A job that ingests raw data from a streaming source into the Lakehouse
- C. A job that enriches data by parsing its timestamps into a human-readable format
- D. A job that cleans data by removing malformatted records
- E. A job that queries aggregated data designed to feed into a dashboard
Answer: E
NEW QUESTION # 49
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