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| Section | Objectives |
|---|---|
| Topic 1: Data Ingestion and Transformation | - Ingest data using Apache Spark and Databricks - Handle batch and streaming data pipelines - Transform and clean datasets using Spark SQL and DataFrame APIs |
| Topic 2: Security, Governance, Monitoring, and Optimization | - Cost optimization and performance tuning - Monitor and optimize Spark workloads - Implement Unity Catalog governance and access control |
| Topic 3: Data Modeling and Storage | - Design scalable data lakehouse architectures - Schema evolution and data partitioning strategies - Delta Lake table design and optimization |
| Topic 4: Production Pipelines and Orchestration | - Pipeline reliability and fault tolerance - Build and manage workflows using Databricks Jobs - Automate ETL pipelines and scheduling |
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NEW QUESTION # 161
A table named user_ltv is being used to create a view that will be used by data analysis on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
An analyze who is not a member of the auditing group executing the following query:
Which result will be returned by this query?
Answer: B
Explanation:
Given the CASE statement in the view definition, the result set for a user not in the auditing group would be constrained by the ELSE condition, which filters out records based on age. Therefore, the view will return all columns normally for records with an age greater than 18, as users who are not in the auditing group will not satisfy the is_member('auditing') condition. Records not meeting the age > 18 condition will not be displayed.
NEW QUESTION # 162
The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property"contains_pii" = true.
The following SQL DDL statement is executed to create a new table:
Which command allows manual confirmation that these three requirements have been met?
Answer: A
Explanation:
Explanation
This is the correct answer because it allows manual confirmation that these three requirements have been met.
The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property
"contains_pii" = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement. Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "DESCRIBE EXTENDED" section.
NEW QUESTION # 163
A data engineer wants to create a cluster using the Databricks CLI for a big ETL pipeline. The cluster should have five workers, one driver of type i3.xlarge, and should use the '14.3.x-scala2.12' runtime.
Which command should the data engineer use?
Answer: C
Explanation:
The Databricks CLI allows users to manage clusters using command-line commands. The correct command for creating a cluster follows a specific format.
Key Components in the Command:
Command Type: databricks compute create is the correct syntax for creating a new compute resource (cluster).
Runtime Version: '14.3.x-scala2.12' specifies the Databricks runtime to use.
Workers: --num-workers 5 sets the number of worker nodes to 5.
Node Type: --node-type-id i3.xlarge defines the hardware configuration.
Cluster Name: --cluster-name DataEngineer_cluster assigns a recognizable name to the cluster.
Evaluation of Options:
Option A (databricks clusters create ...)
Incorrect: databricks clusters create is not a valid command in the Databricks CLI v0.205.
The correct CLI command for cluster creation is databricks compute create.
Option B (databricks clusters add ...)
Incorrect: databricks clusters add is not a valid CLI command.
Option C (databricks compute add ...)
Incorrect: databricks compute add is not a valid CLI command.
Option D (databricks compute create ...)
Correct: databricks compute create is the correct command for creating a cluster.
Conclusion:
The correct command to create a cluster with five workers, an i3.xlarge node type, and Databricks runtime 14.3.x-scala2.12 is:
databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster Thus, the correct answer is D.
Reference:
Databricks CLI Documentation
NEW QUESTION # 164
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?
Answer: C
Explanation:
Explanation
This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills. Verified References:
[Databricks Certified Data Engineer Professional], under "Performance Tuning" section; Databricks Documentation, under "Skew" section.
NEW QUESTION # 165
The following table consists of items found in user carts within an e-commerce website.
The following MERGE statement is used to update this table using an updates view, with schema evaluation enabled on this table.
How would the following update be handled?
Answer: D
Explanation:
With schema evolution enabled in Databricks Delta tables, when a new field is added to a record through a MERGE operation, Databricks automatically modifies the table schema to include the new field. In existing records where this new field is not present, Databricks will insert NULL values for that field. This ensures that the schema remains consistent across all records in the table, with the new field being present in every record, even if it is NULL for records that did not originally include it.
:
Databricks documentation on schema evolution in Delta Lake: https://docs.databricks.com/delta/delta-batch.
html#schema-evolution
NEW QUESTION # 166
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