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NEW QUESTION: 1
Which three statements about Oracle RAC background processes are correct?
A. LMS (Global Cache Service Process) manages non-Cache Fusion resource requests such as library and row cache requests
B. LCKO (Instance Enqueue Process) manages background slave process creation and communication on remote instances
C. ACMS (AtomicControlfile to Memory Service) is an agent that contributes to ensuring that a distributed SGA memory updateis either globally committedon success or globally aborted if a failure occurs
D. LMON (Global Enqueue Service Monitor) monitors global enqueues and resources across the cluster and performs global enqueue recovery operations
E. LMD (Global Enqueue Service Daemon) manages incoming remote resource requests withineach instance
F. GTXO-j (Global Transaction Process) controls the flow of messages to remote instances, manages global data block access,and transmits block images between the buffer caches or different instances
Answer: C,D,E
NEW QUESTION: 2
You need to implement a scaling strategy for the local penalty detection data.
Which normalization type should you use?
A. Cosine
B. Weight
C. Batch
D. Streaming
Answer: C
Explanation:
Explanation
Post batch normalization statistics (PBN) is the Microsoft Cognitive Toolkit (CNTK) version of how to evaluate the population mean and variance of Batch Normalization which could be used in inference Original Paper.
In CNTK, custom networks are defined using the BrainScriptNetworkBuilder and described in the CNTK network description language "BrainScript." Scenario:
Local penalty detection models must be written by using BrainScript.
References:
https://docs.microsoft.com/en-us/cognitive-toolkit/post-batch-normalization-statistics
Topic 1, 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: 3
The DBA tells you that the system is not overloaded but you can tell that the system us actively swapping. What command would you run to show this information to the DBA?
A. # iostat 5 10
B. # vmstat 5 10
C. # cat /proc/meminfo
D. # iotop
Answer: A
Explanation:
*iostat - Report Central Processing Unit (CPU) statistics and input/output statistics for devices, partitions and network filesystems (NFS).
*The iostat command is used for monitoring system input/output device loading by observing the time the devices are active in relation to their average transfer rates. The iostat command generates reports that can be used to change system configuration to better balance the input/output load between physical disks.
Incorrect:
Not A: Related to kernel and processes. *iotop - simple top-like I/O monitor *iotop watches I/O usage information output by the Linux kernel (requires 2.6.20 or later) and displays a table of current I/O usage by processes or threads on the system.
*iotop displays columns for the I/O bandwidth read and written by each process/thread during the sampling period. It also displays the percentage of time the thread/process spent while swapping in and while waiting on I/O. For each process, its I/O priority (class/level) is shown. In addition, the total I/O bandwidth read and written during the sampling period is displayed at the top of the interface.
Not C: related to RAM usage. *The entries in the /proc/meminfo can help explain what's going on with your memory usage, if you know how to read it. *High-Level Statistics MemTotal: Total usable ram (i.e. physical ram minus a few reserved bits and the kernel binary code) MemFree: Is sum of LowFree+HighFree (overall stat) MemShared: 0; is here for compat reasons but always zero. Buffers: Memory in buffer cache. mostly useless as metric nowadays Cached: Memory in the pagecache (diskcache) minus SwapCache SwapCache: Memory that once was swapped out, is swapped back in but still also is in the swapfile (if memory is needed it doesn't need to be swapped out AGAIN because it is already in the swapfile. This saves I/O)
Not D:vmstat - Report virtual memory statistics
NEW QUESTION: 4
How do temporary and permanent differences between taxable income and pre-tax financial income differ?
A. Temporary differences do not give rise to future taxable or deductible amounts.
B. Temporary differences include items that enter into prepay financial income but never into taxable income.
C. Only temporary differences have deferred tax consequences
D. Only permanent differences have deferred tax consequences
Answer: C
Explanation:
Permanent differences have no deferred tax consequences because they affect only the period in which they occur. Permanent differences include1) items that enter into pre-tax financial income but never into taxable income and2) items that enter into taxable income but never into pre-tax financial income. In contrast, temporary differences result in taxable or deductible amounts in some future year(s), when the reported amount of assets are recovered and the reported amount of liabilities are settled. Temporary differences therefore do have deferred tax consequences while permanent differences do not.