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NEW QUESTION: 1
You are creating a machine learning model. You have a dataset that contains null rows.
You need to use the Clean Missing Data module in Azure Machine Learning Studio to identify and resolve the null and missing data in the dataset.
Which parameter should you use?
A. Replace with mean
B. Remove entire column
C. Remove entire row
D. Hot Deck
Answer: B
Explanation:
Explanation/Reference:
Explanation:
Remove entire row: Completely removes any row in the dataset that has one or more missing values. This is useful if the missing value can be considered randomly missing.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data Testlet 1 Case study Overview You are a data scientist in a company that provides data science for professional sporting events. Models will use 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.

Assess 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
Media used for penalty event detection will be provided by consumer devices. Media may include

images and videos captured during the sporting event and shared 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 video transcripts or radio commentary, and logs from related social media feeds captured during the sporting events.
Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo

formats.
Penalty detection and sentiment
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.
Advertisements
During the initial weeks in production, the following was observed:
Ad response rated 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 uncorrelated features.
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 slow.
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 shows 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.
Ad response models must be trained at the beginning of each event and applied during the sporting

event.
Market segmentation models must optimize for similar ad response history.

Sampling must guarantee mutual and collective exclusively between 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.
Ad response models must support non-linear boundaries of features.

The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated

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:

NEW QUESTION: 2

A. Option A
B. Option C
C. Option D
D. Option B
E. Option E
Answer: A,C,D
Explanation:
A: Budget control is available for source documents and accounting journals.
B: Define budget control parameters
1. Click Budgeting > Setup > Budget control > Budget control configuration.
2. Select an account structure. If you have multiple active account structures in the chart of accounts, select the
account structure that will be used for profit and loss or expense accounts. This account structure includes the main
account range for expense accounts.
After you select an account structure, all the financial dimensions in that account structure that were defined for
budgeting are displayed in the Budget dimensions list.
3. Select a financial dimension and move it to the Budget control dimensions list.
Etc
D: 6. Select a budget manager, which is a user who can approve budget workflows. Another budget manager can be
defined by using a budget control rule.
7. In the Budget threshold field, enter the percentage of the budget that can be spent.
The threshold can be used to provide warning messages or to define budget permissions to prevent specific user
groups from exceeding the budget threshold. This threshold can exceed 100 percent.
8. Select the Display a message when exceeding budget threshold check box to display messages when the budget
threshold is exceeded.
Reference: Set up budget control [AX 2012]

NEW QUESTION: 3

A. Option B
B. Option C
C. Option D
D. Option A
Answer: C

NEW QUESTION: 4
View the Exhibit and examine the structure of ORDERS and CUSTOMERS tables.

There is only one customer with the cust_last_name column having value Roberts. Which INSERT statement should be used to add a row into the ORDERS table for the customer whose CUST_LAST_NAME is Roberts and CREDIT_LIMIT is 600?
A. INSERT INTO(SELECT o.order_id, o.order_date, o.order_mode, c.customer_id, o.order_totalFROM orders o, customers cWHERE o.customer_id = c.customer_idAND c.cust_last_name='Roberts' AND c.credit_limit=600 )VALUES (1, '10-mar-2007', 'direct', (SELECT customer_idFROM customersWHERE cust_last_name='Roberts' ANDcredit_limit=600), 1000);
B. INSERT INTO ordersVALUES(1, '10-mar-2007', 'direct',(SELECT customer_idFROM customersWHERE cust_last_name='Roberts' ANDcredit_limit=600), 1000);
C. INSERT INTO orders (order_id, order_data, order_mode,(SELECT customer_idFROM customersWHERE cust_last_name='Roberts' ANDcredit_limit=600), order_total)VALUES (1,
'10-mar-2007', 'direct', &customer_id, 1000).
D. INSERT INTO orders (order_id, order_data, order_mode,(SELECT customer_idFROM customersWHERE cust_last_name='Roberts' ANDcredit_limit=600), order_total)VALUES(1,
'10-mar-2007', 'direct', &&customer_id, 1000);
Answer: B

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