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ACA Big Data Certification exam Dumps

ACA-BIGDATA1 exam Format | Course Contents | Course Outline | exam Syllabus | exam Objectives

Exam Specification: ACA-BIGDATA1 ACA Big Data Certification

Exam Name: ACA-BIGDATA1 ACA Big Data Certification
Exam Code: ACA-BIGDATA1
Exam Duration: 120 minutes
Passing Score: 70%
Exam Format: Multiple-choice, True/False, and Hands-on tasks
Exam Delivery: Proctored online or at a testing center

Course Outline:

1. Introduction to Big Data
- Understanding the concept of Big Data
- Exploring the characteristics and challenges of Big Data
- Overview of the Big Data ecosystem and technologies

2. Big Data Storage and Processing
- Understanding distributed file systems (e.g., Hadoop HDFS)
- Introduction to MapReduce and Apache Spark
- Exploring batch processing and stream processing frameworks

3. Data Ingestion and Integration
- Techniques for data ingestion from various sources
- Integration of structured and unstructured data
- Implementing data transformation and normalization

4. Big Data Analytics
- Introduction to data analytics and machine learning in Big Data
- Utilizing SQL and NoSQL databases for data analysis
- Implementing data visualization techniques

5. Data Governance and Security
- Ensuring data quality and data governance in Big Data projects
- Understanding data privacy and security considerations
- Implementing access control and encryption in Big Data environments

6. Big Data Infrastructure and Deployment
- Designing and configuring Big Data infrastructure
- Deploying and managing Big Data clusters
- Scaling and optimizing Big Data systems

7. Big Data Application Development
- Developing Big Data applications using programming languages (e.g., Java, Python)
- Utilizing Big Data frameworks and libraries (e.g., Apache Hadoop, Apache Spark)
- Implementing real-time data processing and analytics

Exam Objectives:

1. Understand the concepts, characteristics, and challenges of Big Data.
2. Identify and utilize Big Data storage and processing technologies.
3. Ingest and integrate data from various sources into Big Data systems.
4. Apply Big Data analytics techniques for data analysis and insights.
5. Ensure data governance, privacy, and security in Big Data projects.
6. Design, deploy, and manage Big Data infrastructure.
7. Develop Big Data applications using programming languages and frameworks.

Exam Syllabus:

Section 1: Introduction to Big Data (10%)
- Concept and characteristics of Big Data
- Challenges and opportunities in Big Data projects
- Overview of the Big Data ecosystem and technologies

Section 2: Big Data Storage and Processing (20%)
- Distributed file systems (e.g., Hadoop HDFS)
- MapReduce and Apache Spark for data processing
- Batch processing and stream processing frameworks

Section 3: Data Ingestion and Integration (15%)
- Techniques for data ingestion from various sources
- Integration of structured and unstructured data
- Data transformation and normalization

Section 4: Big Data Analytics (15%)
- Data analytics and machine learning in Big Data
- SQL and NoSQL databases for data analysis
- Data visualization techniques

Section 5: Data Governance and Security (10%)
- Data quality and data governance in Big Data projects
- Data privacy and security considerations
- Access control and encryption in Big Data environments

Section 6: Big Data Infrastructure and Deployment (15%)
- Designing and configuring Big Data infrastructure
- Deployment and management of Big Data clusters
- Scaling and optimization of Big Data systems

Section 7: Big Data Application Development (15%)
- Big Data application development using programming languages
- Utilizing Big Data frameworks and libraries
- Real-time data processing and analytics

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Question: 42
Where is the meta data(e.g.,table schemas) in Hive? Score 2
A. Stored as metadata on the NameNode
B. Stored along with the data in HDFS
C. Stored in the RDBMS like MySQL
D. Stored in ZooKeeper
Answer: C
Question: 43
Scenario: Jack is the administrator of project prj1. The project involves a large volume of sensitive data such as bank
account, medical record, etc. Jack wants to properly protect the data.
Which of the follow statements is necessary?
A. set ProjectACL=true;
B. add accountprovider ram;
C. set ProjectProtection=true;
D. use prj1;
Answer: C
Question: 44
If a task node of DataWorks is deleted from the recycle bin, it can still be restored.
A. True
B. False
Answer: A
Question: 45
When they use the MaxCompute tunnel command to upload the log.txt file to the t_log table, the t_log is a partition
table and the partitioning column is (p1 string, p2 string).
Which of the following commands is correct?
A. tunnel upload log.txt t_log/p1="b1�, p2="b2"
B. tunnel upload log.txt t_log/(p1="b1�, p2="b2")
C. tunnel upload log.txt t_log/p1="b1"/p2="b2"
Answer: A
Question: 46
DataV is a powerful yet accessible data visualization tool, which features geographic information systems allowing for
rapid interpretation of data to understand relationships, patterns, and trends. When a DataV screen is ready, it can
embed works to the existing portal of the enterprise through ______.
A. URL after the release
B. URL in the preview
C. MD5 code obtained after the release
D. Jar package imported after the release
Answer: A
Question: 47
You are working on a project where you need to chain together MapReduce, Hive jobs. You also need the ability to
use forks, decision points, and path joins.
Which ecosystem project should you use to perform these actions? Score 2
A. Spark
B. HUE
C. Zookeeper
D. Oozie
Answer: D
Question: 48
DataService Studio in DataWorks aims to build a data service bus to help enterprises centrally manage private and
public APIs. DataService Studio allows you to quickly create APIs based on data tables and register existing APIs with
the DataService Studio platform for centralized management and release.
Which of the following descriptions about DataService Studio in DataWorks is INCORRECT? Score 2
A. DataService Studio is connected to API Gateway. Users can deploy APIs to API Gateway with oneclick.
B. DataService Studio adopts the serverless architecture. All you need to care is the query logic of APIs, instead of the
infrastructure such as the running environment.
C. To meet the personalized query requirements of advanced users, DataService Studio provides the custom Python
script mode to allow you compile the API query by yourself. It also supports multi-table association, complex query
conditions, and aggregate functions.
D. Users can deploy any APIs created and registered in DataService Studio to API Gateway for management, such as
API authorization and authentication, traffic control, and metering
Answer: C
Question: 49
There are multiple connection clients for MaxCompute, which of the following is the easiest way to configure
workflow and scheduling for MaxCompute tasks? Score 2
A. Use DataWorks
B. Use Intelij IDEA
C. Use MaxCompute Console
D. No supported tool yetc
Answer: A
Question: 50
Synchronous development in DataWorks provides both wizard and script modes. Score 1
A. True
B. False
Answer: A
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