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| Section | Objectives |
|---|---|
| Topic 1: Data Modeling and Transformation | - Spark SQL transformations - Performance optimization techniques - Dimensional modeling concepts |
| Topic 2: Data Ingestion and Processing | - Structured Streaming fundamentals - Batch and streaming ingestion with Auto Loader - ETL pipeline design patterns |
| Topic 3: Production Pipelines and Orchestration | - Job scheduling and monitoring - Error handling and recovery strategies - Databricks Workflows |
| Topic 4: Databricks Lakehouse Platform Architecture | - Data governance concepts (Unity Catalog basics) - Medallion architecture (Bronze, Silver, Gold) - Workspace and cluster architecture |
| Topic 5: Delta Lake and Data Management | - Schema evolution and enforcement - Delta Lake transactions and ACID properties - Time travel and versioning |
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NEW QUESTION # 221
A data engineer manages a Unity Catalog table customer_data in schema finance that includes sensitive fields like ssn and credit_score. Intern Group should only see masked values, while Analyst Group should only access rows for their assigned region. The data engineer needs to restrict access based on user role and region without duplicating data. How should the data engineer enforce this security policy?
Answer: A
Explanation:
Unity Catalog row filters can restrict which rows are visible based on attributes such as the user's assigned region, while column masks can dynamically obfuscate sensitive fields like ssn and credit_score based on user roles. This enforces fine-grained, role-and region-based access control directly at the table level without duplicating data or relying on custom views.
NEW QUESTION # 222
A large company seeks to implement a near real-time solution involving hundreds of pipelines with parallel updates of many tables with extremely high volume and high velocity data.
Which of the following solutions would you implement to achieve this requirement?
Answer: A
Explanation:
High Concurrency clusters in Databricks are designed for multiple concurrent users and workloads. They provide fine-grained sharing of cluster resources and are optimized for operations such as running multiple parallel queries and updates. This would be suitable for a solution that involves many pipelines with parallel updates, especially with high volume and high velocity data.
NEW QUESTION # 223
A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:
Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?
Answer: B
Explanation:
This is the correct answer because checkpointing is a critical feature of Structured Streaming that provides fault tolerance and recovery in case of failures. Checkpointing stores the current state and progress of a streaming query in a reliable storage system, such as DBFS or S3. Each streaming query must have its own checkpoint directory that is unique and exclusive to that query. If two streaming queries share the same checkpoint directory, they will interfere with each other and cause unexpected errors or data loss.
NEW QUESTION # 224
When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?
Answer: B
Explanation:
In the context of cluster performance and resource utilization, a CPU utilization rate of around
75% is generally considered a good indicator of efficient resource usage. This level of CPU utilization suggests that the cluster is being effectively used without being overburdened or underutilized. A consistent 75% CPU utilization indicates that the cluster's processing power is being effectively employed while leaving some headroom to handle spikes in workload or additional tasks without maxing out the CPU, which could lead to performance degradation. A five Minute Load Average that remains consistent/flat (Option A) might indicate underutilization or a bottleneck elsewhere.
Monitoring network I/O (Options B and C) is important, but these metrics alone don't provide a complete picture of resource utilization efficiency.
Total Disk Space (Option D) remaining constant is not necessarily an indicator of proper resource utilization, as it's more related to storage rather than computational efficiency.
NEW QUESTION # 225
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?
Answer: C
NEW QUESTION # 226
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