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MLA-C01 real exams

Exam Code: MLA-C01

Exam Name: AWS Certified Machine Learning Engineer - Associate

Version: V13.25

Q & A: 72 Questions and Answers

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NEW QUESTION: 1
The high incidence of speech articulation disorders among young children suggests that such "disorders" are _______ developmental phenomena, since they generally occur less frequently among _______ age groups.
A. actually normal . . other
B. very serious . . most
C. relatively rare . . certain
D. commonly misunderstood . . older
E. clinically acceptable . . younger
Answer: A
Explanation:
Explanation/Reference:
Explanation:
The first operative word in the sentence is since, which means "because" here. The fact that the disorders appear frequently only among young children would explain the conclusion that these "disorders" are not disorders at all, but rather normal developmental phenomena that children grow out of. Choice C conveys this idea. The second key word is "disorders" (in quotes). The fact that the word appears in quotes provides a clue that the term might be a misnomer - an inappropriate label. Again, choice C conveys this idea.

NEW QUESTION: 2
Choose the following components that are necessary for the configuration of a mirror service. (Choose 2)
A. A mirror destination to indicate which ports to monitor.
B. A mirror source to indicate on which sap or sdp traffic should be replicated
C. A mirror destination to indicate on which sap or sdp traffic should be replicated.
D. A mirror source to indicate which ports to monitor.
Answer: C,D

NEW QUESTION: 3
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
You have a Microsoft SQL Server data warehouse instance that supports several client applications.
The data warehouse includes the following tables: Dimension.SalesTerritory, Dimension.Customer, Dimension.Date, Fact.Ticket, and Fact.Order. The Dimension.SalesTerritory and Dimension.Customer tables are frequently updated. The Fact.Order table is optimized for weekly reporting, but the company wants to change it to daily. The Fact.Order table is loaded by using an ETL process. Indexes have been added to the table over time, but the presence of these indexes slows data loading.
All data in the data warehouse is stored on a shared SAN. All tables are in a database named DB1. You have a second database named DB2 that contains copies of production data for a development environment. The data warehouse has grown and the cost of storage has increased. Data older than one year is accessed infrequently and is considered historical.
You have the following requirements:
Implement table partitioning to improve the manageability of the data warehouse and to avoid the need

to repopulate all transactional data each night. Use a partitioning strategy that is as granular as possible.
Partition the Fact.Order table and retain a total of seven years of data.

Partition the Fact.Ticket table and retain seven years of data. At the end of each month, the partition

structure must apply a sliding window strategy to ensure that a new partition is available for the upcoming month, and that the oldest month of data is archived and removed.
Optimize data loading for the Dimension.SalesTerritory, Dimension.Customer, and Dimension.Date

tables.
Incrementally load all tables in the database and ensure that all incremental changes are processed.

Maximize the performance during the data loading process for the Fact.Order partition.

Ensure that historical data remains online and available for querying.

Reduce ongoing storage costs while maintaining query performance for current data.

You are not permitted to make changes to the client applications.
You need to implement the data partitioning strategy.
How should you partition the Fact.Order table?
A. Create 17,520 partitions.
B. Create 730 partitions.
C. Create 2,557 partitions.
D. Use a granularity of two days.
Answer: C
Explanation:
Explanation/Reference:
Explanation:
We create on partition for each day, which means that a granularity of one day is used. 7 years times 365 days is 2,555. Make that 2,557 to provide for leap years.
From scenario: Partition the Fact.Order table and retain a total of seven years of data.
The Fact.Order table is optimized for weekly reporting, but the company wants to change it to daily.
Maximize the performance during the data loading process for the Fact.Order partition.
Reference: https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables- partition

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