DP-100시험난이도 & Microsoft DP-100적중율높은덤프공부 – DP-100덤프문제
DP-100시험난이도, DP-100적중율 높은 덤프공부, DP-100덤프문제, DP-100최신 덤프샘플문제, DP-100인증덤프 샘플문제, DP-100최신 업데이트버전 인증덤프, DP-100최신 인증시험 기출자료, DP-100높은 통과율 시험대비 공부문제, DP-100퍼펙트 덤프 최신 데모문제, DP-100최신 덤프자료, DP-100합격보장 가능 시험덤프, DP-100퍼펙트 덤프공부문제
Microsoft DP-100 시험난이도 오르지 못할 산도 정복할수 있는게 저희 제품의 우점입니다, Microsoft DP-100 적중율 높은 덤프공부 DP-100 적중율 높은 덤프공부 최신버전 덤프는 여러분들이 한방에 시험에서 통과하도록 도와드립니다, PassTIP에서는 Microsoft인증 DP-100시험을 도전해보시려는 분들을 위해 퍼펙트한 Microsoft인증 DP-100덤프를 가벼운 가격으로 제공해드립니다.덤프는Microsoft인증 DP-100시험의 기출문제와 예상문제로 제작된것으로서 시험문제를 거의 100%커버하고 있습니다, Microsoft인증 DP-100시험준비자료는 PassTIP에서 마련하시면 기적같은 효과를 안겨드립니다.
여운이는 과거지, 노엘이 어깨 위에서 호들갑을 떨었다, 냉철하고 이성적인 인물(https://www.passtip.net/DP-100-pass-exam.html)로 소문난 분, 재연은 충직한 부하가 되어 고개를 꾸벅 숙였다, 지애가 우물쭈물 말을 흐리자, 정환이 지애를 가리며 물었다, 마치 원한에 가득한 악귀 같았다.
제압하는 것이 아니라, 확실하게 끝을 내는 동작이 뒤를 이었고 거대한 장정 하나DP-100적중율 높은 덤프공부가 툭 쓰러졌다, 어느새 들어온 마조람이 말을 잇지 못하는 시클라멘 대신 설명을 이었다, 어차피 다시 해도 똑같을 거예요, 혜정의 안색이 금세 하얗게 질렸다.
그건 이 방에 들어와서 알게 됐습니다, 적평 때문에 역시 늦잠을 잔 그도DP-100덤프문제뛰진 않았으나 최대한 빠른 걸음으로 급하게 영소의 방으로 왔다, 뭐라고 부르든 상관없었다, 어쩌면 이대로 무사히 버텨낼 수 있지 않을까 하는 희망.
제가 지금 좀 혼자 있고 싶거든요, 실장이 실세라면 그는 백전노장이었다, 클리셰는 미들DP-100시험난이도랜드의 서쪽 성문을 바라보며 낮은 목소리로 중얼거렸다, 총관께선 너무하시네요, 아직 기억도 온전치 않은 사람을 그리고 몸도 아직 거동이 불편하거늘 내어보낼 생각을 하시다니요.
미안해요, 괜한 분란에 말려들어서 피곤한 일을 만들었어요, 괜찮으면 걸쳐요, 동업자DP-100시험난이도일수록 사적인 관계는 맺고 싶지 않았던 나비는 단호한 표정으로 그에게 선을 그었다, 근데 그거 말고요, 쓰레기 같은 자식, 모친이 죽기 전, 부친을 만났다고 했었다.
괜찮은 줄 알았다, 자꾸만 심장이 긴장한 듯 뛰었다, 그것이 승상이라 하더DP-100시험난이도라도, 만약에 스타일러스 펜이 있을 경우 펜을 사용할수 없습니다.감독관마다 다르지만 몇몇 감독관들은 계산기를 반드시 책상에 놓고 사용하라고 합니다.
최신버전 DP-100 시험난이도 덤프샘플문제 체험하기
하지만, 클리셰의 손바닥은 깨끗했다, 먹빛 눈동자에 해란의 얼굴이 고스란히 비쳤다.
Designing and Implementing a Data Science Solution on Azure 덤프 다운받기
NEW QUESTION 21
You create a binary classification model by using Azure Machine Learning Studio.
You must tune hyperparameters by performing a parameter sweep of the model. The parameter sweep must meet the following requirements:
* iterate all possible combinations of hyperparameters
* minimize computing resources required to perform the sweep
* You need to perform a parameter sweep of the model.
Which parameter sweep mode should you use?
- A. Random seed
- B. Entire grid
- C. Random grid
- D. Random sweep
- E. Sweep clustering
Answer: C
Explanation:
Explanation
Maximum number of runs on random grid: This option also controls the number of iterations over a random sampling of parameter values, but the values are not generated randomly from the specified range; instead, a matrix is created of all possible combinations of parameter values and a random sampling is taken over the matrix. This method is more efficient and less prone to regional oversampling or undersampling.
If you are training a model that supports an integrated parameter sweep, you can also set a range of seed values to use and iterate over the random seeds as well. This is optional, but can be useful for avoiding bias introduced by seed selection.
Topic 2, Case Study 1
Overview
You are a data scientist in a company that provides data science for professional sporting events. Models will be global and local market data to meet the following business goals:
*Understand sentiment of mobile device users at sporting events based on audio from crowd reactions.
*Access a user’s tendency to respond to an advertisement.
*Customize styles of ads served on mobile devices.
*Use video to detect penalty events.
Current environment
Requirements
* Media used for penalty event detection will be provided by consumer devices. Media may include images and videos captured during the sporting event and snared using social media. The images and videos will have varying sizes and formats.
* The data available for model building comprises of seven years of sporting event media. The sporting event media includes: recorded videos, transcripts of radio commentary, and logs from related social media feeds feeds captured during the sporting events.
*Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo Formats.
Advertisements
* Ad response models must be trained at the beginning of each event and applied during the sporting event.
* Market segmentation nxxlels must optimize for similar ad resporr.r history.
* Sampling must guarantee mutual and collective exclusivity local and global segmentation models that share the same features.
* Local market segmentation models will be applied before determining a user’s propensity to respond to an advertisement.
* Data scientists must be able to detect model degradation and decay.
* Ad response models must support non linear boundaries features.
* The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviates from 0.1+/-5%.
* The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following diagram:
Penalty detection and sentiment
Findings
*Data scientists must build an intelligent solution by using multiple machine learning models for penalty event detection.
*Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
*Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
*Notebooks must execute with the same code on new Spark instances to recode only the source of the data.
*Global penalty detection models must be trained by using dynamic runtime graph computation during training.
*Local penalty detection models must be written by using BrainScript.
* Experiments for local crowd sentiment models must combine local penalty detection data.
* Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.
* All shared features for local models are continuous variables.
* Shared features must use double precision. Subsequent layers must have aggregate running mean and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
*Ad response rates declined.
*Drops were not consistent across ad styles.
*The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that come from location sources are being used as raw features. A suggested experiment to remedy the bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
*Initial data discovery shows a wide range of densities of target states in training data used for crowd sentiment models.
*All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are running too stow.
*Audio samples show that the length of a catch phrase varies between 25%-47%, depending on region.
*The performance of the global penalty detection models show lower variance but higher bias when comparing training and validation sets. Before implementing any feature changes, you must confirm the bias and variance using all training and validation cases.
NEW QUESTION 22
You create a new Azure subscription. No resources are provisioned in the subscription.
You need to create an Azure Machine Learning workspace.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. Run Python code that uses the Azure ML SDK library and calls the Workspace.get method with name, subscriptionjd, and resource.group parameters.
- B. Use an Azure Resource Management template that includes a
Microsoft.MachineLearningServices/workspaces resource and its dependencies. - C. Use the Azure Command Line Interface (CLI) with the Azure Machine Learning extension to call the az group create function with -name and -location parameters, and then the az ml workspace create function, specifying -w and -g parameters for the workspace name and resource group.
- D. Run Python code that uses the Azure ML SDK library and calls the Workspacesreate method with name, subscriptionjd, resource_group, and location parameters.
- E. Navigate to Azure Machine Learning studio and create a workspace.
Answer: A,D,E
NEW QUESTION 23
You create a binary classification model to predict whether a person has a disease.
You need to detect possible classification errors.
Which error type should you choose for each description? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: True Positive
A true positive is an outcome where the model correctly predicts the positive class Box 2: True Negative A true negative is an outcome where the model correctly predicts the negative class.
Box 3: False Positive
A false positive is an outcome where the model incorrectly predicts the positive class.
Box 4: False Negative
A false negative is an outcome where the model incorrectly predicts the negative class.
Note: Let’s make the following definitions:
“Wolf” is a positive class.
“No wolf” is a negative class.
We can summarize our “wolf-prediction” model using a 2×2 confusion matrix that depicts all four possible outcomes:
Reference:
https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative
NEW QUESTION 24
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