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The Databricks Certified Data Engineer Associate Exam certification exam focuses on various topics related to data engineering, including data ingestion, data processing, data warehousing, and data analytics. It also covers the fundamentals of big data processing and distributed computing, making it an ideal certification for professionals who want to work with large-scale data sets.
Databricks Certified Data Engineer Associate Exam Sample Questions (Q90-Q95):
NEW QUESTION # 90
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?
- A. The ability to collaborate in real time on a single notebook
- 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 set up alerts for query failures
Answer: D
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. Reference: 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
1learn.microsoft.com2medium.com3slideshare.net4docs.databricks.com5github.com6key2consulting.com
NEW QUESTION # 91
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The code block used by the data engineer is below:
If the data engineer only wants the query to process all of the available data in as many batches as required, which of the following lines of code should the data engineer use to fill in the blank?
- A. processingTime(1)
- B. trigger(continuous="once")
- C. trigger(availableNow=True)
- D. trigger(parallelBatch=True)
- E. trigger(processingTime="once")
Answer: C
Explanation:
Explanation
https://stackoverflow.com/questions/71061809/trigger-availablenow-for-delta-source-streaming-queries-in-pyspa
NEW QUESTION # 92
A data engineer is onboarding a new Bronze ingestion pipeline in Databricks with Unity Catalog. The team wants Databricks to handle storage layout, apply platform optimizations over time, and simplify lifecycle management so that when a table is dropped, its underlying data is also cleaned up according to Databricks- managed retention policies.
Which table type should the data engineer create for these ingestion tables?
- A. Managed tables so that Unity Catalog manages both metadata and underlying data lifecycle
- B. External tables with a LOCATION pointing to an external volume for full control of file layout
- C. Foreign tables federated from an external catalog to delegate optimization to the source system
- D. Temporary views over files to avoid table-level governance and lifecycle coupling
Answer: A
NEW QUESTION # 93 
Calculate the total sales amount for each region and store the results in a new dataframe called region_sales.
Given the expected result:
Which code will generate the expected result?
- A. region_sales = sales_df.groupBy("region").agg(sum("sales_amountM).alias("total_sales_amount"))
- B. region_sales= sales_df.groupBy("category").sum(nsales_amount").alias("t_otal_sales_amounl")
- C. region_sales = sales_df. sum ("salen_aiTiount") . groupBy ("region") .alias ("total_sale3_amount")
- D. region sales - sales_df.agg(sum("sales_amount").groupBy("region").alias("total sales amount"))
Answer: A
NEW QUESTION # 94
A data engineer is debugging a Python notebook in Databricks that processes a dataset using PySpark. The notebook fails with an error during a DataFrame transformation. The engineer wants to inspect the state of variables, such as the input DataFrame and intermediate results, to identify where the error occurs. Which tool should the engineer use to debug the notebook and inspect the values of variables like DataFrames?
- A. Use the Ganglia UI to monitor cluster resource usage and identify hardware issues
- B. Use the Python Notebook Interactive Debugger to set breakpoints and inspect variable values in real-time
- C. Use the Spark UI to analyze the execution plan and identify stages where the job failed
- D. Use the Databricks CLI to download and analyze driver logs for detailed error messages
Answer: B
Explanation:
The Python Notebook Interactive Debugger in Databricks allows setting breakpoints and inspecting variable values, including DataFrames, in real time. This makes it the correct tool for debugging transformation errors in a PySpark notebook.
NEW QUESTION # 95
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