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

Section	Objectives
Topic 1: Data Ingestion and ELT Development	- Handling structured and semi-structured data - ETL patterns and transformations - Data ingestion using Spark SQL and PySpark
Topic 2: Data Governance and Quality	- Data quality concepts and management - Unity Catalog basics - Data access control and governance
Topic 3: Databricks Lakehouse Platform Fundamentals	- Workspace, architecture, and core platform concepts - Clusters, notebooks, and basic Databricks environment usage
Topic 4: Productionizing Data Pipelines	- Scheduling and monitoring jobs - Pipeline deployment and operationalization - Databricks Workflows / Jobs orchestration
Topic 5: Data Processing and Transformations	- PySpark DataFrame transformations - Apache Spark SQL operations (joins, aggregations, filtering) - User-defined functions (UDFs) - Delta Lake fundamentals (tables, transactions, optimization)

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q55-Q60):

NEW QUESTION # 55
Which of the following code blocks will remove the rows where the value in column age is greater than 25 from the existing Delta table my_table and save the updated table?

A. UPDATE my_table WHERE age > 25;
B. DELETE FROM my_table WHERE age > 25;
C. UPDATE my_table WHERE age <= 25;
D. DELETE FROM my_table WHERE age <= 25;
E. SELECT * FROM my_table WHERE age > 25;

Answer: B

Explanation:
The DELETE command in Delta Lake allows you to remove data that matches a predicate from a Delta table. This command will delete all the rows where the value in the column age is greater than 25 from the existing Delta table my_table and save the updated table. The other options are either incorrect or do not achieve the desired result. Option A will only select the rows that match the predicate, but not delete them. Option B will update the rows that match the predicate, but not delete them. Option D will update the rows that do not match the predicate, but not delete them. Option E will delete the rows that do not match the predicate, which is the opposite of what we want. Reference: Table deletes, updates, and merges - Delta Lake Documentation

NEW QUESTION # 56
A data engineer team has decided to implement a new data platform on Databricks and is currently deciding how to store each kind of data on each data layer. What is the appropriate layer and data pairing for medallion architecture?

A. Silver Layer - Raw data from deposit account application
B. Gold Layer - Deduplicated money transfer transaction
C. Silver Layer - Cleansed master customer data
D. Bronze Layer - Summary of cash deposit amount for each country and city

Answer: C

Explanation:
In the Medallion architecture, the Silver layer holds cleansed and conformed data (such as master customer data), which has been cleaned, validated, and standardized after ingestion from the Bronze layer.

NEW QUESTION # 57
Which of the following is stored in the Databricks customer's cloud account?

A. Data
B. Notebooks
C. Cluster management metadata
D. Repos
E. Databricks web application

Answer: A

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 # 58
A data engineer is developing an ETL process based on Spark SQL. The execution fails. The data engineer checks the Spark Ul and can see the ERRORS as follows:

Which two corrective actions should the data engineer perform to resolve this issue?
Choose 2 answers - (Q) Narrow the filters in order to collect less data in the query

A. Upsize the worker nodes and activate autoshuffle partitions
B. Upsize the driver node and deactivate autoshuffle partitions
C. Fix the shuffle partitions to 50 to ensure the allocation
D. Cache the dataset in order to boost the query performance

Answer: A,B

NEW QUESTION # 59
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

A. python
(spark.readStream
.table("silver_table")
.groupBy("department")
.agg(avg("salary").alias("avg_salary"))
.writeStream
.format("delta")
.option("checkpointLocation", "/checkpoint/gold")
.outputMode("append")
.table("gold_table"))
B. python
(spark.readStream
.table("silver_table")
.withColumn("full_name", concat(col("first_name"), lit(" "), col("last_name")))
.writeStream
.format("delta")
.option("checkpointLocation", "/checkpoint/gold")
.outputMode("append")
.table("gold_table"))
C. python
(spark.readStream
.table("bronze_table")
.filter(col("valid") == True)
.writeStream
.format("delta")
.option("checkpointLocation", "/checkpoint/silver")
.outputMode("append")
.table("silver_table"))
D. python
(spark.readStream
.table("silver_table")
.groupBy("department")
.agg(avg("salary").alias("avg_salary"))
.writeStream
.format("delta")
.option("checkpointLocation", "/checkpoint/gold")
.outputMode("complete")
.table("gold_table"))
E. python
(spark.readStream
.table("raw_data")
.writeStream
.format("delta")
.option("checkpointLocation", "/checkpoint/bronze")
.outputMode("append")
.table("bronze_table"))

Answer: D

Explanation:
The best practice is to use " Complete " as output mode instead of " append " when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option E.

NEW QUESTION # 60
......

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