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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Python for Data Engineering10-15%- Use PySpark for data processing
- Implement user-defined functions (UDFs)
- Work with Spark APIs in Python
Topic 2: Spark SQL and DataFrames15-20%- Aggregate and group data
- Write and execute Spark SQL queries
- Join and union DataFrames
- Handle null values and data quality
Topic 3: Delta Lake Fundamentals20-25%- Write to and read from Delta tables
- Understand ACID transactions and time travel
- Create and manage Delta tables
- Explain Delta Lake features and benefits
Topic 4: Data Pipeline Architecture15-20%- Design data pipelines for batch and streaming
- Understand ELT vs ETL patterns
- Implement incremental data processing
- Monitor and optimize pipeline performance
Topic 5: Apache Spark Data Processing Fundamentals20-25%- Work with structured data types (arrays, maps, structs)
- Apply transformations and actions on DataFrames
- Use Spark SQL for data processing
- Create and use Spark DataFrames
Topic 6: Lakehouse Platform Concepts10-15%- Describe key Databricks Lakehouse platform components
- Explain data governance and security concepts
- Understand the Lakehouse architecture and its benefits

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q129-Q134):

NEW QUESTION # 129
An engineering manager uses a Databricks SQL query to monitor ingestion latency for each data source. The manager checks the results of the query every day, but they are manually rerunning the query each day and waiting for the results.
Which of the following approaches can the manager use to ensure the results of the query are updated each day?

Answer: A

Explanation:
Explanation
https://docs.databricks.com/en/sql/user/queries/schedule-query.html


NEW QUESTION # 130
A data engineer needs Task C to run only when Task A succeeds and Task B fails.
Which dependency configuration implements this conditional logic?

Answer: B


NEW QUESTION # 131
Which of the following is stored in the Databricks customer's cloud account?

Answer: E

Explanation:
The only option that is stored in the Databricks customer's cloud account is data. Data is stored in the customer's cloud storage service, such as AWS S3 or Azure Data Lake Storage. The customer has full control and ownership of their data and can access it directly from their cloud account.
Option A is not correct, as the Databricks web application is hosted and managed by Databricks on their own cloud infrastructure. The customer does not need to install or maintain the web application, but only needs to access it through a web browser.
Option B is not correct, as the cluster management metadata is stored and managed by Databricks on their own cloud infrastructure. The cluster management metadata includes information such as cluster configuration, status, logs, and metrics. The customer can view and manage their clusters through the Databricks web application, but does not have direct access to the cluster management metadata.
Option C is not correct, as the repos are stored and managed by Databricks on their own cloud infrastructure.
Repos are version-controlled repositories that store code and data files for Databricks projects. The customer can create and manage their repos through the Databricks web application, but does not have direct access to the repos.
Option E is not correct, as the notebooks are stored and managed by Databricks on their own cloud infrastructure. Notebooks are interactive documents that contain code, text, and visualizations for Databricks workflows. The customer can create and manage their notebooks through the Databricks web application, but does not have direct access to the notebooks.
Databricks Architecture
Databricks Data Sources
Databricks Repos
[Databricks Notebooks]
[Databricks Data Engineer Professional Exam Guide]


NEW QUESTION # 132
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 of the following keywords can be used to compact the small files?

Answer: D

Explanation:
The keyword that can be used to compact the small files associated with a Delta table is OPTIMIZE. The OPTIMIZE command performs file compaction on a Delta table by rewriting a set of small files into a set of larger files1. This can improve the performance of queries that scan the table by reducing the number of files that need to be read and the amount of metadata that needs to be processed1. The OPTIMIZE command can also optionally sort the data within each file by a given set of columns, which can further improve the query performance by enabling data skipping and predicate pushdown1. The OPTIMIZE command can be applied to the whole table or to a specific partition of the table1.
The other keywords are not suitable for compacting the small files associated with a Delta table. REDUCE is a keyword used in the SQL syntax for aggregating data using a user-defined function2. COMPACTION is not a valid keyword in SQL or Python. REPARTITION is a keyword used in the Python syntax for changing the number of partitions of a DataFrame or an RDD3. VACUUM is a keyword used to remove files that are no longer referenced by a Delta table and are older than a retention threshold4.
References:
* 1: OPTIMIZE | Databricks on AWS
* 2: REDUCE | Databricks on AWS
* 3: repartition | Databricks on AWS
* 4: VACUUM | Databricks on AWS


NEW QUESTION # 133
A Python file is ready to go into production and the client wants to use the cheapest but most efficient type of cluster possible. The workload is quite small, only processing 10GBs of data with only simple joins and no complex aggregations or wide transformations. Which cluster meets the requirement?

Answer: A

Explanation:
A job cluster with spot instances enabled is the cheapest and efficient option for small, production workloads. Spot instances reduce costs, and a job cluster ensures the cluster only runs during the workload execution.


NEW QUESTION # 134
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