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NI CLAD : Certified LabVIEW Associate Developer (CLAD) Exam

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Test Number : CLAD
Test Name : Certified LabVIEW Associate Developer (CLAD)
Vendor Name : NI
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CLAD test Format | CLAD Course Contents | CLAD Course Outline | CLAD test Syllabus | CLAD test Objectives


The best preparation for this test is LabVIEW programming experience applying the concepts presented in LabVIEW Core1 and Core2 courses. Class attendance alone will not be sufficient. Typical CLAD candidates have taken NIs LabVIEW Core 1 and Core 2 courses (or equivalent) and have at least 6 to 9 months of experience using LabVIEW.
This prep guide provides an overview of the exam, including test logistics and coverage. The relevant knowledge, skills and abilities (KSAs) tested by the CLAD test are listed in the KSAs: Knowledge, Skills and Abilities tested by the CLAD section. A detailed course List table follows the KSA descriptions.
This prep guide also includes example questions. This is not a trial exam. The questions are included as a study aid. They reflect the scope of the CLAD test and familiarize the test-taker with NIs approach to testing the KSAs relevant to a Certified LabVIEW Associate Developer.
Scoring
A score of 70% or higher is required to pass the exam.

Individuals may take the CLAD test at any PearsonVUE testing center by scheduling the test at http://www.pearsonvue.com/ni/. When you schedule the exam, be sure to specify whether you wish to take the test using LabVIEW NXG or LabVIEW 20xx (where xx indicates current year). The images and terminology of the test will reflect the LabVIEW editor you choose, but the test content will otherwise be identical. Simply choose the LabVIEW editor with which you are most comfortable.
Passing the CLAD test using either editor earns the same CLAD Certification.
You cannot use LabVIEW or any other resources during the exam. The CLAD test provides screenshots from the LabVIEW environment and LabVIEW Help where appropriate. Refer to the Example Questions & Resources section for examples.
To take the exam, you must agree to a Non-Disclosure Agreement (NDA). The NDA states that you will not copy, reproduce, or communicate any section of the test in any form, including verbal or electronic. Failure to comply with the NDA will result in penalties ranging from a failed test to a lifetime ban from LabVIEW Certification

The CLAD test centers around jobs requiring programming in professional settings, including Automated Test, High-Channel-Count Data Acquisition, or Domain Support. Most of these jobs include collecting and/or processing data in the form of signals from sensors. To verify the relevant knowledge, skills and abilities, the CLAD uses an NI-DAQmx system as representative hardware. NI-DAQmx was chosen because it can be simulated on all versions of LabVIEW. If you have access to LabVIEW, you have the resources to prepare for the exam. You do not need to purchase specific hardware.
Appendix I of the document provides instructions for setting up simulated hardware sufficient for test preparation. The appendix also includes a list of the DAQmx functions possibly used in the exam. The test does not test DAQmx function-specific settings. The questions use NI-DAQmx to test knowledge and skills necessary for common data acquisition tasks, such as calculating trial rates, determining the correct order of operations, and programming basic file I/O.
A person using LabVIEW at the Associated Developer level will be able to:
Use software architectures from a single VI to a simple State Machine or Event-Driven UI
Handler.
Collect data from sensors using NI Hardware.
Use Array functions extensively to extract and manipulate a single channel of data from multiple-channel data represented by a 1D waveform array or a 2D numeric array.
Use loops to run a test a set number of times or until a condition is met, to establish a voltage ramp, or conduct other repeated tasks.
Create and modify SubVIs, clusters, and Type Defs to simplify their code and contribute to larger projects.

Hardware (10% of test questions)
Connecting Hardware: Sensors, DAQ, Devices under test (DUT.
Acquiring and validating a signal
Processing signals
Using appropriate trial rates
LabVIEW Programming Environment (25% of test questions)
Setting up and using a LabVIEW Project to:
o Add, delete, and move elements
o Use libraries and appropriate types of folders
o Avoid cross-linking
Data Types:
o Recognize data types on the front panel
o Recognize data types on the block diagram from terminals and wires
o Choose appropriate controls, indicators, data types, & functions for a given scenario
Predicting order-of-execution and behavior of
o A non-looping VI
o A Simple State Machine
o An Event-driven UI Handler
o Parallel Loops (without queues)
Using basic functions to create a simple Acquire-Analyze-Visualize application
Troubleshooting by identifying and correcting the cause of a broken arrow or incorrect data
Error handling using error clusters and merge error functions to ensure errors are handled well
Navigating LabVIEW help to get more information about inputs, outputs, and functions
LabVIEW Programming Fundamentals (50% of test questions)
Loops
o Create continuous HW acquisition or generation loop by applying a
Open-Configure-Perform Operation-Close model.
o Retain data in shift registers
o Use input and output terminals effectively, including:
=> Determining the last value output
=> Indexing input and output terminals
=> Concatenating output
=> Using conditional output
=> Using shift registers, both initialized and uninitialized.
o Use timing of loops appropriately, including:
=> Software timing
=> Hardware timing
o Use For Loops and While Loops appropriately

Arrays
o View data from an n-channel HW acquisition VI (using the DAQmx Read VI) using a
waveform graph, waveform chart, or numeric/waveform array indicator.
o Extract a single channel of data (waveform or 1D Array) from a:
=> 1D waveform array representing acquired data from multiple channels
=> 2D numeric array representing acquired data from multiple channels
=> 1D numeric array representing single measurement from multiple channels
o Use a For Loop with auto-indexing and conditional tunnels to:
=> Iterate through an array
=> Iterate processing code on each channel of data in a 1D waveform array
=> Generate an array of data that meets required conditions
o Identify by sight and be able to use and predict the behavior of the following array
functions and VIs:
=> Array Size
=> Index Array
=> Replace Subset
=> Insert Into Array
=> Delete From Array
=> Initialize Array
=> Build Array
=> Array Subset
=> Max & Min
=> Sort 1D Array
=> Search 1D Array
=> Split 1D Array
Writing conditional code to perform an action based on the value of a user input or a measurement result.
studying and Writing data to a file
o Use Open/Act/Close model for file I/O
o Write data to a text file using high-level file I/O functions
o Continuously stream data to a text file or a TDMS file
o Append data to an existing data file
o Log data using simple VIs

Acquire data from DAQmx functions
o Display data on a graph
o Save data to a CSV file
o Choose single measurement/multiple channel and single channel/multiple
measurements configurations appropriately
Programming Best Practices (15% of test questions)
SubVIs Reusing Code
o Create SubVIs to increase readability and scalability of VIs
o Configure the subVI connector pane using best practices
o Choose appropriate code as a SubVI source
Clusters Grouping Data of Mixed Data Types
o Create, manipulate, analyze, and use cluster data in common scenarios
o Group related data by creating a cluster to Strengthen data organization and VI readability
Type Defs Propagate Data Type Changes
o Create Type Defs and use Type Defs in multiple places
o Update Type Defs to propagate changes to all instances of the Type Def



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Making Chips To final Their Lifetime | CLAD cheat sheet and braindumps

Chips are supposed to last their lifetime, but that expectation varies greatly depending upon the conclusion market, no matter if the device is used for safeguard- or mission-critical applications, and even whether it may also be quite simply changed or remotely mounted.

It additionally is dependent upon how these chips are used, even if they're a vital a part of a posh system, and no matter if the cost of persistent monitoring and feedback may also be amortized across the price of the usual gadget. It’s now not a simple equation, and there are not any standard solutions, notably at superior nodes where extra circuitry can increase power consumption and reduce efficiency.

“To be capable of predict reliability and efficiency adjustments by using having a baseline knowing of chips and being capable of extrapolate from that to precisely predict when things are going to birth failing is the Holy Grail,” noted Steve Pateras, senior director of marketing for check items in Synopsys‘ Design group. “You deserve to be capable of do performance and safety optimization and eventually preserve reliability on an ongoing groundwork. And if a vehicle goes to birth failing, you wish to get off the street.”

during the past, reliability frequently was regarded a part of the manufacturing procedure. Chips can be baked in an oven for a duration of time or discipline to carefully monitored vibration to examine when a device would fail. while distinctive eventualities can be simulated just before manufacturing, chips might nevertheless fail, but the defectivity expense would lower based upon how tightly quite a lot of tactics were managed and how a great deal margin may be added to supply some type of failover. this is evident these days with ECC reminiscence.

The problem going forward, mainly at advanced nodes, is to be capable of leverage present circuitry and limiting the quantity of redundancy, that may affect power and performance. This capability failover must be more centered to crucial capabilities, and it requires tons extra figuring out of what is occurring on a chip or in a kit at any element in time.

“With a chip in the container for a long period of time, then scientifically we be aware of what the physics of failure in the back of that are,” stated Noam Brousard, system vp at proteanTecs. “How do they display screen and measure it over time and deduce the implications of these results? an easy approach is to constantly look at various how the chip is behaving and if its efficiency is degrading over time. This falls short on multiple fronts. When monitoring the functional response to degradation they might only word the effects of the degradation as soon as they basically cause a failure, or they received’t know the precise physical cause for the degradation. and maybe they received’t be able to use this talents to foretell when in the future the failure will ensue. an additional approach is to make use of telemetry, in accordance with what they name agents, embedded in the silicon. these are all the time monitoring the physical parameters of the silicon on the transistor level, mapping their degradation over time. you can plug that statistics into established formulation for decent provider injection or NBTI, which trigger ageing in chips, and run ML algorithms that display screen the degradation rate and from there which you could calculate and predict time to failure ahead of its exact incidence.”

Synopsys’ Pateras consents. “part of here is now not simply deciding what’s going to turn up,” said Pateras. “It’s presenting tips so that, in some cases, the infrastructure is performing corrections. You deserve to have redundancy, and demanding processors have to be dual-lockstep. You also need to be monitoring this kind of activity.”

Fig. 1: Industry estimates on expected lifetimes of chips. Source: Industry estimates/Semiconductor Engineering

Fig. 1: Industry estimates on expected lifetimes of chips. Source: Industry estimates/Semiconductor Engineering

Fig. 1: trade estimates on anticipated lifetimes of chips. source: industry estimates/Semiconductor Engineering

records managementA key element to lifecycle administration is being capable of collect and flow facts freely up and down the design-through-manufacturing chain. in the past, a lot of this statistics has been saved in silos, and whereas that proved an effective option to manipulate individual method steps in the past, the starting to be complexity of chips, programs and techniques makes that information effective for making certain reliability across all of those contraptions.

“from time to time, if you know how an etch tool or a lithography device behaved, that may influence what extra exams you need to do,” talked about John Kibarian, president and CEO of PDF options. “checks can advantage from understanding what took place in the processing tools upstream. customers could make extra informed decisions about what to verify, when to examine, and whether to put further stressing on definite elements on account of a particular etcher or processing ability. So the value of sharing all that statistics can make every individual gadget greater useful. It’s now not all about extra checking out. It’s about more suitable trying out and smarter checking out.”

That requires a lots more built-in approach, however there's a doubtlessly big payback. “There are cases the place it doesn’t basically make sense to do a remaining look at various if you’re going to do a system-degree look at various, anyway,” spoke of Doug Lefever, president and CEO of Advantest america. “after which there's migration of things upstream into wafer form and everything in between. it could be myopic for us to believe about just average ATE insertions. You need a greater holistic view, including things like virtual insertions, where you could take statistics from the fab or a previous test and mix that with an RMA or in situ things within the field, and use that statistics to do a application-based insertion and not using a hardware at all. We’re taking a look at that at this time.”

The information path can also be extended to the conclusion consumer, as neatly. “that you can mix the records all the manner returned to production to music down an issue in a vehicle according to definite traits,” talked about Uzi Baruch, vice chairman and time-honored supervisor of the automobile business unit at OptimalPlus, which is a division of countrywide contraptions. “This goes past check records. you can take measurements on the motor itself to work out the foundation explanation for dimensional facts and you'll examine measurements.”

but it surely’s additionally essential to be able to filter out data that is not basic to making a choice on even if a tool is functioning effectively, where the anomalies are, and charting that to how that equipment is performing over time. As devices turn into more advanced, specially at superior nodes the place there are diverse vigour rails and voltages, all of this turns into much more difficult.

“once in a while you aren’t even privy to the issue,” spoke of Baruch. “The facts from a motor vehicle has to be connected again to the production line in case complications do reveal up. however you also have to screen out stuff comparable to climate facts, where there is no direct correlation to how the gadget is functioning. if you’re doing root-trigger analysis, you want data it really is significant.”

Disaggregation issuesThis doubtlessly becomes extra intricate as quite a lot of distinctive tactics are unbundled from an SoC. The rising cost of developing chips at the most superior nodes, coupled with the decreasing vigour/performance advancements at each and every node, are forcing a number of chipmakers to offload a lot of services that don’t scale smartly, or which aren’t crucial, into separate chips in a package.

“we can create heterogeneous integration that allows for us to position chips right into a equipment and put in completely diverse category of chips — whether it’s analog, combined signal, digital, or even sensors — all on the equal platform for this universal integration conversion that they see nowadays,” referred to Ingu Yin Chang, senior vice chairman of ASE, in a fresh presentation. “There are a considerable number of types of integration, whether that’s silicon photonics for onboard optical options, and power integration, where you try to obtain definite vigour for prime-performance computing. however die partitioning can increase yields, the potential to combine high-bandwidth reminiscence, and to use numerous IP to in the reduction of the vigour.”

All of those elements can have an effect on the existence expectancy of a gadget. there's less heat, much less circuit getting older, a reduction in electromigration and other real consequences, and potentially an extended lifetime for dielectrics and different films. but there still are some kinks to work out of the supply chain.

“The difficult part is that reminiscence stack is supplied through one consumer, the SoC is provided through a special consumer, after which the packaging house offers the silicon interposer and packages every thing collectively,” mentioned Alan Liao, director of product advertising at FormFactor. “With only one chip, which you can tell if it’s first rate or bad. but when a packaging condominium takes one die from business A and one die from business B and integrates them collectively, if that complete kit fails, then who takes the responsibility? so you deserve to get the critical records to determine the enterprise model. They look at various memory, SoCs, and the interposer, and as soon as they are put together they test everything once more. always that occurs on the verify house. They bring together the entire records and analyze it.”

here's simpler stated than accomplished in complicated packages. drive on the testing side must be exact.

“If the drive isn’t ample, you may additionally not get a solid contact,” mentioned Liao. “If it’s too excessive, then the entire bumps are going to cave in. but there are just too many bumps to check on the identical time, so we’re taking a look at advanced MEMS processing in their fab to provide probes for these elements. It’s challenging, however so far from a probe card standpoint it looks to be okay. but if they preserve pushing in that route, likely new substances and new tactics can be essential.”

Microbumps are more and more problematic. The conception at the back of microbumps is that more connections will also be made to a chip, which in flip enables greater indicators to be routed between distinctive die. however bumps also are littered with some of the same complications as other styles of scaling.

“The advantage of microbumps is that with very nice resolution, that you may delivery processing on chip A and run it to chip B, and the good judgment obtainable from the chip above may well be closer than in case you were to route a signal on a single chip,” talked about Marc Swinnen, director of product advertising at Ansys. “here is the place where we’re heading with comprehensive 3D design. the first generation of this is bumping, where in precept that you can break up a single die and without problems double the reticle dimension, or probably triple it if you put bumps on both sides of a chip. but reliability is a massive problem. there's mechanical stress and warpage, and there's a potential for thermal mismatch.”

transferring left, right and centerOne big alternate that could be critical to control the complete lifecycle of chips is a free circulation of data throughout diverse ingredients of the flow and out to the container, with a full loop lower back the entire solution to the architects of the device.

“It’s now not very nearly when chips are in the container,” stated Synopsys’ Pateras. “It starts from design, via manufacturing, via exams, bring-up, and in the end in box. if you start accumulating suggestions and figuring out early on, that can be used in the later levels. so as you get to the traceability, that you would be able to do correlations and baselines for prediction when you've got the design and manufacturing facts analyzed. We’re proposing an strategy that has two fundamental add-ons. One is visibility into the device. So it’s displays and sensors intelligently embedded into the chips. and then these screens and sensors will provide prosperous statistics about what’s happening, and they can extract that information. And all of those different phases of life cycles — design, simulation, manufacturing, yield ramp, check, convey-up optimization, and then in the box — deliver rich records that they will analyze and eventually feed returned all the way through the life cycle stages.”

The expanding adoption of AI/ML in manufacturing can aid greatly in predicting how devices will age, however its reach throughout dissimilar facets on a board or in a package is limited to the volume of respectable information obtainable.

“computer gaining knowledge of is a good option for the floor defect detection,” stated Tim Skunes, vice chairman of know-how and business construction at CyberOptics. “And we're a variety of laptop studying algorithms for their MRS (multi-reflection suppression) expertise. They design very complex imaging methods, and in any given imaging device there are likely 1,000 distinctive variables that can affect the remaining imaging efficiency. so you’ve acquired design for manufacturability, and how you partition the tolerances turns into extraordinarily critical.”

almost all essential device and tools carriers are working with some level of desktop gaining knowledge of these days, and looking out the way to prolong that even extra.

“desktop discovering quite enters into each their MEMS fab processing in addition to their design manner,” observed FormFactor’s Liao. “So every of those probe playing cards, is custom made to a specific software or chip. Being able to optimize that probe card is the important thing to getting excessive yield on the wafer, and they practice a large consumer database for a kind of utility or design. they can utilize their library with laptop getting to know and find that in the past there have been 10 designs that matched this. Now that's immediately carried out at the back of the scenes the usage of their design device.”

ConclusionThe emphasis on reliability, traceability, and predictability is turning out to be throughout assorted markets. while the car business changed into the catalyst for all of this, it has unfold to other markets the place chipmakers need to utterly leverage their investment in chip design. Having it fail upfront within the box has economic consequences, both via a take into account or a alternative.

Aligning all of the essential pieces throughout all system steps is a great challenge, and one so as to require a lot tighter integration of a complex and world provide chain. If that can be completed, big efficiencies are to be won on the company facet and demanding advances feasible on the technology side — and a whole bunch of latest opportunities to enable this shift.


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