CCP Data Engineer (DE575) Practice Exam

CCP Data Engineer (DE575) Practice Exam

 

The CCP Data Engineer Exam (DE575) is a performance-based exam designed to assess your ability to develop data engineering solutions using Cloudera's platform. For this exam, candidates are tasked with addressing five to ten distinct customer issues, each accompanied by a sizable dataset, and utilizing a CDH cluster within a four-hour timeframe. The objective is to devise and execute precise technical solutions that fulfill all specified criteria. 

Demonstrating sufficient industry expertise, candidates must analyze each problem to determine the optimal approach within the given time constraints. Execution of the solutions is performed on a live cluster under strict conditions, including time restrictions and supervision by a proctor.

 

Who should take the exam?

Applicants for the CCP Data Engineer role should possess extensive experience in creating data engineering solutions and demonstrate a proficient command of the mentioned skills. No additional prerequisites are required.

The CCP Data Engineer examination aims to pinpoint proficient data specialists aspiring to distinguish themselves and garner acknowledgment from employers seeking their expertise. Apart from hands-on involvement in the domain, individuals aspiring to attain this certification are advised to commence with Cloudera's Spark and Hadoop Developer training course.

 

Exam Details 

• Exam Code: DE575
• Exam Name: CCP Data Engineer Exam
• Exam Languages: English
• Exam Questions: 5–10 performance-based (hands-on) tasks on pre-configured Cloudera Enterprise cluster. 
• Time: 240 minutes
• Passing Score: 70%

 

Exam Course Outline 

The Exam covers the given topics  - 

Topic 1: Understand Data Ingest

The skills to transfer data between external systems and your cluster. This includes the following:

• Import and export data between an external RDBMS and your cluster, including the ability to import specific subsets, change the delimiter and file format of imported data during ingest, and alter the data access pattern or privileges.
• Ingest real-time and near-real time (NRT) streaming data into HDFS, including the ability to distribute to multiple data sources and convert data on ingest from one format to another.
• Load data into and out of HDFS using the Hadoop File System (FS) commands.

 

Topic 2: Learn about Transform, Stage, Store

Convert a set of data values in a given format stored in HDFS into new data values and/or a new data format and write them into HDFS or Hive/HCatalog. This includes the following skills:

• Convert data from one file format to another
• Write your data with compression
• Convert data from one set of values to another (e.g., Lat/Long to Postal Address using an external library)
• Change the data format of values in a data set
• Purge bad records from a data set, e.g., null values
• Deduplication and merge data
• Denormalize data from multiple disparate data sets
• Evolve an Avro or Parquet schema
• Partition an existing data set according to one or more partition keys
• Tune data for optimal query performance

 

Topic 3: Learn about Data Analysis

Filter, sort, join, aggregate, and/or transform one or more data sets in a given format stored in HDFS to produce a specified result. All of these tasks may include reading from Parquet, Avro, JSON, delimited text, and natural language text. The queries will include complex data types (e.g., array, map, struct), the implementation of external libraries, partitioned data, compressed data, and require the use of metadata from Hive/HCatalog.

• Write a query to aggregate multiple rows of data
• Write a query to calculate aggregate statistics (e.g., average or sum)
• Write a query to filter data
• Write a query that produces ranked or sorted data
• Write a query that joins multiple data sets
• Read and/or create a Hive or an HCatalog table from existing data in HDFS

 

Topic 4: Understand Workflow

The ability to create and execute various jobs and actions that move data towards greater value and use in a system. This includes the following skills:

• Create and execute a linear workflow with actions that include Hadoop jobs, Hive jobs, Pig jobs, custom actions, etc.
• Create and execute a branching workflow with actions that include Hadoop jobs, Hive jobs, Pig jobs, custom action, etc.
• Orchestrate a workflow to execute regularly at predefined times, including workflows that have data dependencies