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Small business accounting software is the best way to record and manage the daily sales and purchase activities of any company. Application has a full control over the invoicing and inventory system of your business. Accounting information system can generate financial reports in several forms like inventory reports, sales reports, purchasing reports, ledger reports, company expenses reports and party reports. Billing management program can calculate ...

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Inventory control solution that lets you track tens of thousands of parts and products in multiple warehouses. It is the best choice for small and midsize businesses because of its seamless integration with Barcode, flexible feature set, and enormous library of training and support resources.

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Databricks DCDEP : Databricks Certified Data Engineer Professional test Questions and Practice Test

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Exam Number : DCDEP
Exam Name : Databricks Certified Data Engineer Professional
Vendor Name : Databricks
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DCDEP test Format | DCDEP Course Contents | DCDEP Course Outline | DCDEP test Syllabus | DCDEP test Objectives


Exam Code: DCDEP
Exam Name: Databricks Certified Data Engineer Professional
Type: Proctored certification
Total number of questions: 60
Time limit: 120 minutes
Question types: Multiple choice

Section 1: Databricks Tooling
- Explain how Delta Lake uses the transaction log and cloud object storage to guarantee atomicity and durability
- Describe how Delta Lakes Optimistic Concurrency Control provides isolation, and which transactions might conflict
- Describe basic functionality of Delta clone.
- Apply common Delta Lake indexing optimizations including partitioning, zorder, bloom filters, and file sizes
- Implement Delta tables optimized for Databricks SQL service
- Contrast different strategies for partitioning data (e.g. identify proper partitioning columns to use)

Section 2: Data Processing (Batch processing, Incremental processing, and Optimization)
- Describe and distinguish partition hints: coalesce, repartition, repartition by range, and rebalance
- Contrast different strategies for partitioning data (e.g. identify proper partitioning columns to use)
- Articulate how to write Pyspark dataframes to disk while manually controlling the size of individual part-files.
- Articulate multiple strategies for updating 1+ records in a spark table (Type 1)
- Implement common design patterns unlocked by Structured Streaming and Delta Lake.
- Explore and tune state information using stream-static joins and Delta Lake
- Implement stream-static joins
- Implement necessary logic for deduplication using Spark Structured Streaming
- Enable CDF on Delta Lake tables and re-design data processing steps to process CDC output instead of incremental feed from normal Structured Streaming read
- Leverage CDF to easily propagate deletes
- Demonstrate how proper partitioning of data allows for simple archiving or deletion of data
- Articulate, how “smalls” (tiny files, scanning overhead, over partitioning, etc) induce performance problems into Spark queries

Section 3: Data Modeling
- Describe the objective of data transformations during promotion from bronze to silver
- Discuss how Change Data Feed (CDF) addresses past difficulties propagating updates and deletes within Lakehouse architecture
- Apply Delta Lake clone to learn how shallow and deep clone interact with source/target tables.
- Design a multiplex bronze table to avoid common pitfalls when trying to productionalize streaming workloads.
- Implement best practices when streaming data from multiplex bronze tables.
- Apply incremental processing, quality enforcement, and deduplication to process data from bronze to silver
- Make informed decisions about how to enforce data quality based on strengths and limitations of various approaches in Delta Lake
- Implement tables avoiding issues caused by lack of foreign key constraints
- Add constraints to Delta Lake tables to prevent bad data from being written
- Implement lookup tables and describe the trade-offs for normalized data models
- Diagram architectures and operations necessary to implement various Slowly Changing Dimension tables using Delta Lake with streaming and batch workloads.
- Implement SCD Type 0, 1, and 2 tables

Section 4: Security & Governance
- Create Dynamic views to perform data masking
- Use dynamic views to control access to rows and columns

Section 5: Monitoring & Logging
- Describe the elements in the Spark UI to aid in performance analysis, application debugging, and tuning of Spark applications.
- Inspect event timelines and metrics for stages and jobs performed on a cluster
- Draw conclusions from information presented in the Spark UI, Ganglia UI, and the Cluster UI to assess performance problems and debug failing applications.
- Design systems that control for cost and latency SLAs for production streaming jobs.
- Deploy and monitor streaming and batch jobs

Section 6: Testing & Deployment
- Adapt a notebook dependency pattern to use Python file dependencies
- Adapt Python code maintained as Wheels to direct imports using relative paths
- Repair and rerun failed jobs
- Create Jobs based on common use cases and patterns
- Create a multi-task job with multiple dependencies
- Design systems that control for cost and latency SLAs for production streaming jobs.
- Configure the Databricks CLI and execute basic commands to interact with the workspace and clusters.
- Execute commands from the CLI to deploy and monitor Databricks jobs.
- Use REST API to clone a job, trigger a run, and export the run output



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Retail store owners operate on tight margins, and have many constantly moving parts. This becomes especially difficult to manage efficiently when using manual methods. A point-of-sale system is a great tool to manage all these moving parts while helping to ensure customer satisfaction, but finding the right POS for small business is no easy feat.

POS can help businesses in a variety of ways, including managing inventory, providing customer management data, identifying opportunities and weaknesses in the store and much, much more.

EMBRIODRY MANAGMENT SOLUTION
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Embroidery’s Industries can utilize this embroidery management tool in a wide variety of ways which will insure that jobs are scheduled, processed, and delivered on time and with the maximum efficiency. This embroidery management software was developed with your business in mind - whether it be big or small - and can be customized to any unique needs you may have.

You may be a business owner or one of the project managers in an embroidery department, our embroidery management software will take the stress out of organizing and coordinating the activities of both you and your clients. An embroidery business can benefit greatly from embroidery management systems that offer ways to harmonize and coordinate multiple departments. To accomplish this, the embroidery management systems are integrated to allow the user to track various job control systems which may incorporate multiple program management structures.

Embroidery Management Capabilities:
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  • Financial Reporting.
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It is a complete enterprise management solution that streamlines clinical, administrative, and financial operations of care delivery organizations, providing unprecedented opportunities for productivity, collaboration, and growth.

Whether your goal is to enhance the value of services to your patients, increase reimbursements, improve the overall efficiency of day-to-day operations, or simply stay ahead of compliance requirements,

  • Patient Management System.
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  • Doctor Services Report System.
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  • Lab Test System.