Questions and Answers: 166
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NEW QUESTION: 1
회사의 웹 사이트는 트랜잭션 데이터 스토리지에 Amazon RDS MySQL Multi-AZ DB 인스턴스를 사용하고 있습니다.
내부 배치 처리를 위해 데이터를 페치하기 위해이 DB 인스턴스를 쿼리하는 다른 내부 시스템이 있습니다. RDS DB 인스턴스는 내부 시스템 페치 데이터를 크게 느리게 합니다. 이는 웹 사이트의 읽기 및 쓰기 성능에 영향을 미치며 사용자의 응답 시간이 느려집니다.
웹 사이트 성능을 향상시키는 솔루션은 무엇입니까?
A. Amazon ElastiCache를 사용하여 웹 사이트에 대한 쿼리 응답을 캐시하십시오.
B. 현재 RDS MySQL Multi.AZ DB 인스턴스에 가용 영역을 추가합니다.
C. MySQL 데이터베이스 대신 RDS PostgreSQL DB 인스턴스를 사용하십시오.
D. RDS DB 인스턴스에 읽기 전용 복제본을 추가하고 읽기 전용 복제본을 쿼리하도록 내부 시스템을 구성합니다.
Answer: D
Explanation:
Amazon RDS Read Replicas
Enhanced performance
You can reduce the load on your source DB instance by routing read queries from your applications to the read replica. Read replicas allow you to elastically scale out beyond the capacity constraints of a single DB instance for read-heavy database workloads. Because read replicas can be promoted to master status, they are useful as part of a sharding implementation.
To further maximize read performance, Amazon RDS for MySQL allows you to add table indexes directly to Read Replicas, without those indexes being present on the master.
https://aws.amazon.com/rds/features/read-replicas/
NEW QUESTION: 2
What is the variability for any given age, when estimating a client's age-predicted maximum heart rate?
A. 10 to 12 beats per minute
B. 22 to 24 beats per minute
C. 18 to 20 beats per minute
D. 2 to 4 beats per minute
Answer: A
NEW QUESTION: 3
群衆感情モデルの評価戦略を定義する必要があります。
順番に実行する必要がある3つのアクションはどれですか？回答するには、適切なアクションをアクションのリストから回答エリアに移動し、正しい順序に並べます。
Answer:
Explanation:
Explanation:
Scenario:
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.
Note: Evaluate the changed in correlation between model error rate and centroid distance In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation.
References:
https://en.wikipedia.org/wiki/Nearest_centroid_classifier
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/sweep-clustering
Topic 2, Two Case study
Overview
You are a data scientist for Fabrikam Residences, a company specializing in quality private and commercial property in the United States. Fabrikam Residences is considering expanding into Europe and has asked you to investigate prices for private residences in major European cities. You use Azure Machine Learning Studio to measure the median value of properties. You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.
Datasets
There are two datasets in CSV format that contain property details for two cities, London and Paris, with the following columns:
The two datasets have been added to Azure Machine Learning Studio as separate datasets and included as the starting point of the experiment.
Dataset issues
The AccessibilityToHighway column in both datasets contains missing values. The missing data must be replaced with new data so that it is modeled conditionally using the other variables in the data before filling in the missing values.
Columns in each dataset contain missing and null values. The dataset also contains many outliers. The Age column has a high proportion of outliers. You need to remove the rows that have outliers in the Age column. The MedianValue and AvgRoomsinHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.
Model fit
The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.
Experiment Requirements
You must set up the experiment to cross-validate the Linear Regression and Bayesian Linear Regression modules to evaluate performance.
In each case, the predictor of the dataset is the column named MedianValue. An initial investigation showed that the datasets are identical in structure apart from the MedianValue column. The smaller Paris dataset contains the MedianValue in text format, whereas the larger London dataset contains the MedianValue in numerical format. You must ensure that the datatype of the MedianValue column of the Paris dataset matches the structure of the London dataset.
You must prioritize the columns of data for predicting the outcome. You must use non-parameters statistics to measure the relationships.
You must use a feature selection algorithm to analyze the relationship between the MedianValue and AvgRoomsinHouse columns.
Model training
Given a trained model and a test dataset, you need to compute the permutation feature importance scores of feature variables. You need to set up the Permutation Feature Importance module to select the correct metric to investigate the model's accuracy and replicate the findings.
You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful.
You are concerned that the model might not efficiently use compute resources in hyperparameter tuning. You also are concerned that the model might prevent an increase in the overall tuning time. Therefore, you need to implement an early stopping criterion on models that provides savings without terminating promising jobs.
Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio. You must create three equal partitions for cross-validation. You must also configure the cross-validation process so that the rows in the test and training datasets are divided evenly by properties that are near each city's main river. The data that identifies that a property is near a river is held in the column named NextToRiver. You want to complete this task before the data goes through the sampling process.
When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent.
Data visualization
You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.
You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.
NEW QUESTION: 4
An organization has a domain name of example.com. Which two SRV records are valid for a SIP and H 323 communication? (Choose two)
A. _h323ls._udp.example.com
B. _collab-edge._tls.example.com
C. _sips._udp.example.com
D. _h323ls_tcp.example.com
E. _sips_tcp.example.com
Answer: A,E