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NEW QUESTION: 1
A company is bidding to win a special contract.
Which of the following is NOT a relevant cost to the company of undertaking the contract?
A. The cost of hiring a machine which will be hired if the contract is won.
B. The depreciation charge on the tools which will be used during the contract.
C. The cost of a training course for staff which will be undertaken if the contract is won.
D. The purchase cost of direct materials not currently in inventory.
Answer: B
NEW QUESTION: 2
When using WLAN Tester 2.0 tool, the RBI speed is best controlled at 0.8m/s~1.2m/s. which of the following about the speed of the RBI is correct? (Multiple choice)
A. speed too slow, the sampling data volume is too large, heat map calculation time is longer
B. Signal collection is a process, it needs enough time to save
C. This is to ensure that normal walking speed with ordinary people coincide
D. speed too fast, heat map is not accurate, may appear heat map abnormal situation
Answer: A,B,D
NEW QUESTION: 3
Normalizing data within a database could include all or some of the following except which one?
A. Eliminating duplicate key fields by putting them into separate tables.
B. Eliminate duplicative columns from the same table.
C. Eliminates Functional dependencies on non-key fields by putting them in a separate table. At this level, all non-key fields are dependent on the primary key.
D. Eliminates functional dependencies on a partial key by putting the fields in a separate table from those that are dependent on the whole key
Answer: A
Explanation:
Explanation/Reference:
Explanation:
Normalizing data within a database does not eliminate duplicate key fields by putting them into separate tables.
An entity is in First Normal Form (1NF) when all tables are two-dimensional with no repeating groups.
A row is in first normal form (1NF) if all underlying domains contain atomic values only. 1NF eliminates repeating groups by putting each into a separate table and connecting them with a one-to-many relationship. Make a separate table for each set of related attributes and uniquely identify each record with a primary key.
Eliminate duplicative columns from the same table.
Create separate tables for each group of related data and identify each row with a unique column or set
of columns (the primary key).
An entity is in Second Normal Form (2NF) when it meets the requirement of being in First Normal Form (1NF) and additionally:
Does not have a composite primary key. Meaning that the primary key cannot be subdivided into
separate logical entities.
All the non-key columns are functionally dependent on the entire primary key.
A row is in second normal form if, and only if, it is in first normal form and every non-key attribute is fully
dependent on the key.
2NF eliminates functional dependencies on a partial key by putting the fields in a separate table from
those that are dependent on the whole key. An example is resolving many:many relationships using an intersecting entity
An entity is in Third Normal Form (3NF) when it meets the requirement of being in Second Normal Form (2NF) and additionally:
Functional dependencies on non-key fields are eliminated by putting them in a separate table. At this
level, all non-key fields are dependent on the primary key.
A row is in third normal form if and only if it is in second normal form and if attributes that do not
contribute to a description of the primary key are move into a separate table. An example is creating look-up tables.
Incorrect Answers:
A: Normalizing data within a database does eliminate duplicative columns from the same table.
B: Normalizing data within a database does eliminate functional dependencies on a partial key by putting the fields in a separate table from those that are dependent on the whole key.
C: Normalizing data within a database does eliminate Functional dependencies on non-key fields by putting them in a separate table.
References:
http://psoug.org/reference/normalization.html
http://searchsqlserver.techtarget.com/definition/normalization?vgnextfmt=print
NEW QUESTION: 4
Case Study: 1 - Flowlogistic
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market.
Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
Databases
8 physical servers in 2 clusters
SQL Server - user data, inventory, static data
3 physical servers
Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
Application servers - customer front end, middleware for order/customs 60 virtual machines across 20 physical servers Tomcat - Java services Nginx - static content Batch servers Storage appliances iSCSI for virtual machine (VM) hosts Fibre Channel storage area network (FC SAN) ?SQL server storage Network-attached storage (NAS) image storage, logs, backups Apache Hadoop /Spark servers Core Data Lake Data analysis workloads
20 miscellaneous servers
Jenkins, monitoring, bastion hosts,
Business Requirements
Build a reliable and reproducible environment with scaled panty of production. Aggregate data in a centralized Data Lake for analysis Use historical data to perform predictive analytics on future shipments Accurately track every shipment worldwide using proprietary technology Improve business agility and speed of innovation through rapid provisioning of new resources Analyze and optimize architecture for performance in the cloud Migrate fully to the cloud if all other requirements are met Technical Requirements Handle both streaming and batch data Migrate existing Hadoop workloads Ensure architecture is scalable and elastic to meet the changing demands of the company.
Use managed services whenever possible
Encrypt data flight and at rest
Connect a VPN between the production data center and cloud environment SEO Statement We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability.
Additionally, I don't want to commit capital to building out a server environment.
Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?
A. Store the common data encoded as Avro in Google Cloud Storage.
B. Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.
C. Store the common data in BigQuery and expose authorized views.
D. Store the common data in BigQuery as partitioned tables.
Answer: C