HCAHD information source - Apache Hadoop Developer Updated: 2024
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Exam Code: HCAHD Apache Hadoop Developer information source January 2024 by Killexams.com team
|Apache Hadoop Developer
Hitachi Developer information source
Other Hitachi examsHH0-210 HDS Certified Implementer - Enterprise
HH0-220 HDS Certified Implmenter-Modular
HH0-530 Hitachi Data Systems Certified Specialist Compute Platform
HH0-560 Hitachi Data Systems Certified Specialist - Content Platform
HH0-580 Hitachi Data Systems Certified Specialist Virtualization solutions implimentation
HH0-350 HDS Certified Specialist - NAS Architect
HCE-5710 Hitachi Data Systems Certified Expert - Replication solutions implementation
HCE-5420 Hitachi Data Systems Certified Specialist - Content Platform
HQT-4210 Hitachi Data Systems Certified Professional - NAS installation HAT
HQT-4180 Hitachi Vantara Qualified Professional VSP Midrange Family Installation
HQT-4120 Hitachi Vantara Qualified Professional VSP G200 to VSP G800 Storage Installation
HCAHD Apache Hadoop Developer
HCE-3700 Hitachi Vantara Certified Expert - Performance Architect
HCE-5920 Certified Specialist: Pentaho Data Integration Implementation
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Assuming the following Hive query executes successfully:
Which one of the following statements describes the result set?
A. A bigram of the top 80 sentences that contain the substring "you are" in the lines column of the input data A1 table.
B. An 80-value ngram of sentences that contain the words "you" or "are" in the lines column of the inputdata table.
C. A trigram of the top 80 sentences that contain "you are" followed by a null space in the lines column of the
D. A frequency distribution of the top 80 words that follow the subsequence "you are" in the lines column of the
Given the following Pig commands:
Which one of the following statements is true?
A. The $1 variable represents the first column of data in 'my.log'
B. The $1 variable represents the second column of data in 'my.log'
C. The severe relation is not valid
D. The grouped relation is not valid
What does Pig provide to the overall Hadoop solution?
A. Legacy language Integration with MapReduce framework
B. Simple scripting language for writing MapReduce programs
C. Database table and storage management services
D. C++ interface to MapReduce and data warehouse infrastructure
What types of algorithms are difficult to express in MapReduce v1 (MRv1)?
A. Algorithms that require applying the same mathematical function to large numbers of individual binary records.
B. Relational operations on large amounts of structured and semi-structured data.
C. Algorithms that require global, sharing states.
D. Large-scale graph algorithms that require one-step link traversal.
E. Text analysis algorithms on large collections of unstructured text (e.g, Web crawls).
See 3) below.
Limitations of Mapreduce C where not to use Mapreduce
While very powerful and applicable to a wide variety of problems, MapReduce is not the answer to every problem.
Here are some problems I found where MapReudce is not suited and some papers that address the limitations of
You need to create a job that does frequency analysis on input data. You will do this by writing a Mapper that uses
TextInputFormat and splits each value (a line of text from an input file) into individual characters. For each one of
these characters, you will emit the character as a key and an InputWritable as the value.
As this will produce proportionally more intermediate data than input data, which two resources should you expect to
A. Processor and network I/O
B. Disk I/O and network I/O
C. Processor and RAM
D. Processor and disk I/O
Which one of the following statements regarding the components of YARN is FALSE?
A. A Container executes a specific task as assigned by the ApplicationMaster
B. The ResourceManager is responsible for scheduling and allocating resources
C. A client application submits a YARW job to the ResourceManager
D. The ResourceManager monitors and restarts any failed Containers
You are developing a combiner that takes as input Text keys, IntWritable values, and emits Text keys, IntWritable
Which interface should your class implement?
Which one of the following Hive commands uses an HCatalog table named x?
A. SELECT * FROM x;
B. SELECT x.-FROM org.apache.hcatalog.hive.HCatLoader('x');
C. SELECT * FROM org.apache.hcatalog.hive.HCatLoader('x');
D. Hive commands cannot reference an HCatalog table
Given the following Pig command:
logevents = LOAD 'input/my.log' AS (date:chararray, levehstring, code:int, message:string);
Which one of the following statements is true?
A. The logevents relation represents the data from the my.log file, using a comma as the parsing delimiter
B. The logevents relation represents the data from the my.log file, using a tab as the parsing delimiter
C. The first field of logevents must be a properly-formatted date string or table return an error
D. The statement is not a valid Pig command
Consider the following two relations, A and B.
A. C = DOIN B BY a1, A by b2;
B. C = JOIN A by al, B by b2;
C. C = JOIN A a1, B b2;
D. C = JOIN A SO, B $1;
Given the following Hive commands:
Which one of the following statements Is true?
A. The file mydata.txt is copied to a subfolder of /apps/hive/warehouse
B. The file mydata.txt is moved to a subfolder of /apps/hive/warehouse
C. The file mydata.txt is copied into Hive's underlying relational database 0.
D. The file mydata.txt does not move from Its current location in HDFS
In a MapReduce job, the reducer receives all values associated with same key.
Which statement best describes the ordering of these values?
A. The values are in sorted order.
B. The values are arbitrarily ordered, and the ordering may vary from run to run of the same MapReduce job.
C. The values are arbitrary ordered, but multiple runs of the same MapReduce job will always have the same ordering.
D. Since the values come from mapper outputs, the reducers will receive contiguous sections of sorted values.
* Input to the Reducer is the sorted output of the mappers.
* The framework calls the application's Reduce function once for each unique key in the sorted order.
For the given demo input the first map emits:
< Hello, 1>
< World, 1>
< Bye, 1>
< World, 1>
The second map emits:
< Hello, 1>
< Hadoop, 1>
< Goodbye, 1>
< Hadoop, 1>
Which describes how a client reads a file from HDFS?
A. The client queries the NameNode for the block location(s). The NameNode returns the block location(s) to the
client. The client reads the data directory off the DataNode(s).
B. The client queries all DataNodes in parallel. The DataNode that contains the requested data responds directly to the
client. The client reads the data directly off the DataNode.
C. The client contacts the NameNode for the block location(s). The NameNode then queries the DataNodes for block
locations. The DataNodes respond to the NameNode, and the NameNode redirects the client to the DataNode that
holds the requested data block(s). The client then reads the data directly off the DataNode.
D. The client contacts the NameNode for the block location(s). The NameNode contacts the DataNode that holds the
requested data block. Data is transferred from the DataNode to the NameNode, and then from the NameNode to the
Reference: 24 Interview Questions & Answers for Hadoop MapReduce developers, How the Client communicates with
For each input key-value pair, mappers can emit:
A. As many intermediate key-value pairs as designed. There are no restrictions on the types of those key-value pairs
(i.e., they can be heterogeneous).
B. As many intermediate key-value pairs as designed, but they cannot be of the same type as the input key-value pair.
C. One intermediate key-value pair, of a different type.
D. One intermediate key-value pair, but of the same type.
E. As many intermediate key-value pairs as designed, as long as all the keys have the same types and all the values
have the same type.
Mapper maps input key/value pairs to a set of intermediate key/value pairs.
Maps are the individual tasks that transform input records into intermediate records. The transformed intermediate
records do not need to be of the same type as the input records. A given input pair may map to zero or many output
Reference: Hadoop Map-Reduce Tutorial
You write MapReduce job to process 100 files in HDFS. Your MapReduce algorithm uses TextInputFormat: the
mapper applies a regular expression over input values and emits key-values pairs with the key consisting of the
matching text, and the value containing the filename and byte offset. Determine the difference between setting the
number of reduces to one and settings the number of reducers to zero.
A. There is no difference in output between the two settings.
B. With zero reducers, no reducer runs and the job throws an exception. With one reducer, instances of matching
patterns are stored in a single file on HDF
C. With zero reducers, all instances of matching patterns are gathered together in one file on HDF
D. With one reducer, instances of matching patterns are stored in multiple files on HDF
E. With zero reducers, instances of matching patterns are stored in multiple files on HDF
F. With one reducer, all instances of matching patterns are gathered together in one file on HDF
* It is legal to set the number of reduce-tasks to zero if no reduction is desired.
In this case the outputs of the map-tasks go directly to the FileSystem, into the output path set by setOutputPath(Path).
The framework does not sort the map-outputs before writing them out to the FileSystem.
* Often, you may want to process input data using a map function only. To do this, simply set mapreduce.job.reduces
to zero. The MapReduce framework will not create any reducer tasks. Rather, the outputs of the mapper tasks will be
the final output of the job.
In this phase the reduce(WritableComparable, Iterator, OutputCollector, Reporter) method is called for each
The output of the reduce task is typically written to the FileSystem via OutputCollector.collect(WritableComparable,
Applications can use the Reporter to report progress, set application-level status messages and update Counters, or just
indicate that they are alive.
The output of the Reducer is not sorted.
In Hadoop 2.0, which one of the following statements is true about a standby NameNode?
The Standby NameNode:
A. Communicates directly with the active NameNode to maintain the state of the active NameNode.
B. Receives the same block reports as the active NameNode.
C. Runs on the same machine and shares the memory of the active NameNode.
D. Processes all client requests and block reports from the appropriate DataNodes.
In the reducer, the MapReduce API provides you with an iterator over Writable values.
What does calling the next () method return?
A. It returns a reference to a different Writable object time.
B. It returns a reference to a Writable object from an object pool.
C. It returns a reference to the same Writable object each time, but populated with different data.
D. It returns a reference to a Writable object. The API leaves unspecified whether this is a reused object or a new
E. It returns a reference to the same Writable object if the next value is the same as the previous value, or a new
Writable object otherwise.
Calling Iterator.next() will always return the SAME EXACT instance of IntWritable, with the contents of that instance
replaced with the next value.
Reference: manupulating iterator in mapreduce
Voltaire Hitachi Ltd.
Voltaire executives declined to disclose the size of Hitachi's investment, but said Hitachi intended to work with Voltaire on development of new InfiniBand products.
Hitachi's interest in the company comes from Voltaire's development of a complete InfiniBand router, including hardware, software and drivers, said Arun Jain, vice president of marketing at Voltaire, based here and in Israel.
Jain said other developers have focused on only one part of the solution. He also said Voltaire is already shipping a 1x InfiniBand router, which helps connect InfiniBand server and storage components to standard TCP/IP networks.
Jain said sales are very slow because the market is waiting for 4x equipment. Voltaire plans to have a 4x router in evaluation next quarter and begin shipments early next year, he said.
Jain admitted that backpedaling by Intel and Microsoft, lack of silicon for InfiniBand and the soft economy have held back the adoption of InfiniBand. "But our partners are saying they plan to put InfiniBand in their products quickly," he said.
Voltaire currently has a limited sales force and is awaiting the availability of its 4x router before developing an indirect sales channel, Jain said. He expects most of the company's sales to go through the channel when that happens.
Hitachi Vantara, a leading developer of IoT and data analytics technologies, wants to integrate new automated data management via a planned acquisition of Waterline Data, a startup developer of automated data cataloging technology.
Data management and analytics technology developer Hitachi Vantara Wednesday said it plans to acquire Waterline Data, a startup developer of intelligent data cataloging technology.
With the acquisition, Waterline Data's unique data cataloging technology will be integrated with Hitachi Vantara's data operations technology to automate management of data across all of a business' data sets, said Lothar Schubert, head of product marketing for Santa Clara, Calif.-based Hitachi Vantara.
Waterline Data will bring to Hitachi Vantara strong and differentiated capabilities in data cataloging, particularly to Hitachi Vantara's Lumada big data and IoT platform and its Pentaho data management and analytics platform, Schubert told CRN.
"It provides common metadata for our Lumada portfolio and extends and differentiates our data operations," he said.
Waterline Data has developed its own intellectual property it calls Data Fingerprinting that helps with the automation, discovery and classification of data, Schubert said.
"Waterline Data uses a combination of AI and rules-based systems to identify data," he said. "It crawls data and learns its metadata. So it can see if data is an insurance claim, a Social Security number, or a customer's data. And it can catalog data across terabytes of capacity. It helps in discovering, tagging and managing data, which is a big deal in GDPR and the California Consumer Privacy Act."
Waterline Data is a startup, but already has products and an existing customer base, Schubert said. "So it's a proven technology already," he said.
The company's technology integrates with most standard and open-source databases, hyperscaler and data warehousing technologies, Schubert said.
"Customers tend to be heterogeneous in their data technology," he said. "They need to connect across a heterogeneous infrastructure."
Hitachi Vantara plans to continue making Waterline Data's technology available as a stand-alone offering while working to integrate that technology into its Lumada and Pentaho platforms, Schubert said. "We will leave no customer behind," he said.
Schubert declined to talk about Waterline Data's size or the financial terms of the acquisition.
The 2018 Open Source Jobs Report published by The Linux Foundation and Dice.com provides many useful insights into which skills are the most marketable, the technologies most affecting hiring decisions, and which incentives are most effective for retaining open source talent. Taken together the many insights in the study provide a useful roadmap for recently-graduated students and experienced open source, developers, and technical professionals. The study is based on a survey of over 750 hiring managers representing a cross-section of corporations, small & medium businesses (SMBs), government agencies and staffing firms worldwide in addition to 6,500 open source professionals. Additional details regarding the methodology can be found in the report downloadable here (PDF, 14 pp., opt-in). The following findings show how strong demand is for developers with open source expertise and which skills are in the most demand.
Against a backdrop of increasing concerns about software security, the U.S. government has recently taken a series of actions to Strengthen software security. This effort started with the White House Executive Order 14028 on Improving the Nation’s Cybersecurity and was followed by two Office of Management and Budget memorandums, M-22-18 and M-23-16, that set schedules and requirements for security compliance. The White House released a National Cybersecurity Strategy and then followed up with an implementation plan, with many of the elements of the plan already underway.
Each of these actions include details about how the government and its suppliers can Strengthen the security of the open source software components that make up a large percentage of the code being used in government applications. But even beyond these government-wide actions, two accurate developments show the government is investing in modern, proactive strategies for improving open source software security.
1. The first U.S. government open source program office
The Center for Medicaid and Medicare Services (CMS) recently established the first U.S. government open source program office (OSPO), where the agency is implementing a developer-minded, private-sector-styled strategy to modernize their approach to open source software. The designation of its first dedicated open source program office is an encouraging signal that the federal government recognizes the strategic value of open source and the innovation it can bring to our government agencies.
As Andrea Fletcher, chief digital strategy officer for CMS, explained it: “We already have a lot of really fantastic open source programs… [and are focused on] pushing these programs forward and how we release our software and our code to the greater healthcare ecosystem… We’re pushing this out over the next couple of years to see what it looks like for an agency to have policies around inbounding and outbounding code.”
Questions for readers:
2. Investing in the security of the open source software supply chain
The Office of the National Cyber Director recently released an RFI on Open-Source Software Security and Memory-Safe Programming Languages, seeking ideas from the public and private sector on how to use government resources to invest in improving open source software security.
One particularly interesting section of the RFI requested ideas around incentives for securing the open source ecosystem.
The core challenge of securing the open source software ecosystem is that it is unlike any other supply chain that is so critical to the global economy because the “suppliers” are largely independent and often unpaid developers (usually called “maintainers”).
A accurate study by Tidelift found that 60% of open source software maintainers described themselves as unpaid hobbyists. The reason the government is focused on maintainer incentives is because unpaid hobbyist maintainers often lack the time and motivation to implement the secure development practices that government and industry require.
So it is significant to see this RFI looking to address incentives for improving open source software security—potentially by paying maintainers to do the work to implement secure development practices like those recommended in the NIST Secure Software Development Framework.
Questions for readers:
It’s becoming clear that many within the U.S. government, in agencies like CISA, ONCD, CMS, NIST, and OMB, are applying modern thinking to how they manage the security risks of building with open source so they can still take advantage of the enormous innovative potential it provides.
Is your organization following the lead? Are you thinking about centralizing how you manage your open source policies and practices like CMS is? Are you keeping track of emerging cybersecurity policies and standards impacting open source so you can ensure you are following security best practices NIST recommends, and not endangering your government revenue by missing key deadlines and requirements from OMB? And are you thinking proactively about the volunteer suppliers who you count on for the open source code you use, like ONCD is, and how you can ensure the maintainers who create it are incented to keep it secure into the future?
Open source can be a powerful and positive innovative force when managed effectively, and a liability when not. These are the types of questions you should be asking in order to stay in tune with leading government initiatives that will help point the way to your organization’s success with open source.
Rob Wickham is vice president of public sector at Tidelift and a 20 year veteran of supporting the U.S. federal government by delivering innovative technology solutions that address critical capabilities in the areas of cybersecurity, DevSecOps, hybrid architectures, and zero trust. He is a frequent panel participant speaking on subjects including zero trust, identity and access management, and emerging technology trends like software supply chain security, and vulnerability prevention.
The integration of GDCV on Hitachi UCP enables enterprises to modernize applications, optimize infrastructure, and bolster security in hybrid cloud environments. By merging the adaptable cloud infrastructure of Hitachi UCP with the versatility and scalability of GDCV, the solution facilitates the deployment and management of workloads in on-premises data centers, cloud environments, or edge locations.
Additionally, the launch of GDVC on Hitachi UCP includes its inclusion in Google’s Anthos Ready platform partners program. This program validates hardware that seamlessly collaborates with GDCV.
The new solution offer following advantages
* Easy Workload Mobility: The solution offers a single framework for easy deployment and control over applications on both in-house and cloud setups, with a safe way to transition to Google Cloud by intelligently distributing workloads for smooth data and infrastructure migration.
* Configuration Flexibility: Customers are given various options to support multiple configurations and applications with differing performance, availability and scalability needs, including the flexibility for Hitachi Vantara customers to utilize GDCV with the high-performance Hitachi Virtual Storage Platform (VSP) as part of our certified driver within the Anthos Ready Storage Partnership.
* Security and Compliance: Modern security controls for software and hardware are used to follow guidelines and needs, managed centrally across different places to ensure security and adherence to business and regulatory compliance requirements.
"Our collaboration brings together Hitachi Vantara's highly available, high-performance integrated cloud infrastructure and GDCV's robust container orchestration and management capabilities. As generative AI reshapes the digital landscape, GDCV on Hitachi UCP enables organizations to confidently leverage the potential of hybrid cloud environments. This turnkey solution equips them to thrive and make the most of the ever-expanding data-driven opportunities in the digital era,” said Dan McConnell, senior vice president, product management for storage and data infrastructure, Hitachi Vantara.
The Metabo and Hitachi brands were hardly strangers before the 2018 rebranding. In fact, the Hitachi Group added the German Metabo company to its portfolio a couple of years before it offloaded its power tools division in 2017. Though Hitachi Power Tools was quickly re-branded as Metabo HPT after that sale, it operated as a separate entity from Metabo until Koki America merged the two in North American markets in 2022.
All the corporate maneuvering didn't amount to much more than a name change from the Hitachi Power Tools team. Even the history page on Metabo HPT's website confirms that the rebrand was intended to change nothing about the Hitachi brand but its name, with the company remaining dedicated to producing the same high-quality tools and well-loved tool kits it always has.
So little has changed since the re-brand that you can still use Hitachi's interchangeable MultiVolt batteries in new Metabo HPT devices. That should come as a comfort to longtime Hitachi Power Tools users.
There has, however, been some confusion between Metabo and Metabo HPT in North America and abroad amid the rebrands and mergers. Despite the NA name merger, it seems there's still not much in the way of compatibility between Metabo and Metabo HPT tools since they continue to operate on different battery systems. It remains to be seen if that will ever change or merge together.
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OpenAI plans to launch a store for GPTs, custom apps based on its text-generating AI models (e.g. GPT-4), sometime in the coming week.
In an email viewed by TechCrunch, OpenAI said that developers building GPTs will have to review the company's updated usage policies and GPT brand guidelines to ensure that their GPTs are compliant before they're eligible for listing in the store -- aptly called the GPT Store. They'll also have to verify their user profile and ensure that their GPTs are published as "public."
The GPT Store was announced last year during OpenAI's first annual developer conference, DevDay, but delayed in December -- almost certainly due to the leadership shakeup that occurred in November, just after the initial announcement. (The short version of the story is, CEO Sam Altman was forced out by OpenAI's board of directors and then -- after investors and employees panicked -- brought back on with a new board in place.)
GPTs don’t require coding experience and can be as simple or complex as a developer wishes. For example, a GPT can be trained on a cookbook collection so that it can answer questions about ingredients for a specific recipe. Or a GPT could ingest a company’s proprietary codebases so that developers can check their style or generate code in line with best practices.
Developers can simply type the capabilities they want their GPT to offer in plain language and OpenAI's GPT-building tool, GPT Builder, will attempt to make an AI-powered chatbot to perform those. Since shortly after DevDay, developers have been able to make and share GPTs with others via the ChatGPT website directly but not publicly list them.
Still unclear is whether the GPT Store will launch with a revenue-sharing scheme of any sort. As of November, Altman and CTO Mira Murati told my colleague Devin Coldewey that there wasn't a firm plan for GPT monetization, and the email about the GPT Store's coming launch makes no mention of what developers can expect on the payments front -- if anything.
An OpenAI spokesperson told TechCrunch that more will be revealed next week.
As I wrote for TechCrunch's semiregular AI newsletter a while back, OpenAI’s shift from AI model provider to platform has been an interesting one to be sure — but not exactly unanticipated. The startup telegraphed its ambitions in March with the launch of plug-ins for ChatGPT, its AI-powered chatbot, which brought third parties into OpenAI’s model ecosystem for the first time.
GPTs effectively democratize generative AI app creation -- at least for apps that use OpenAI’s family of models. In fact, GPTs could kill consultancies whose business models revolve around building what are essentially GPTs for customers.
Is that a good thing? I’d argue not necessarily. But we'll have to wait to see how it all plays out.
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