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NEW QUESTION: 1
Refer to the exhibit.

One of the backend systems invoked by an API implementation enforces rate limits on the number of requests a particular client can make. Both the backend system and the API implementation are deployed to several non-production environments in addition to production.
Rate limiting of the backend system applies to all non-production environments. The production environment, however, does NOT have any rate limiting.
What is the most effective approach to conduct performance tests of the API implementation in a staging (non-production) environment?
A. Conduct scaled-down performance tests in the staging environment against the rate limited backend system then upscale performance results to full production scale
B. Use MUnit to simulate standard responses from the backend system then conduct performance tests to identify other bottlenecks in the system
C. Create a mocking service that replicates the backend system's production performance characteristics. Then configure the API implementation to use the mocking service and conduct the performance tests
D. Include logic within the API implementation that bypasses invocations of the backend system in a performance test situation. Instead invoking local stubs that replicate typical backend system responses then conduct performance tests using this API Implementation
Answer: C

NEW QUESTION: 2
In building a daily route schedule, which of the following factors provides the greatest challenge for a transportation manager?
A. Data cleanup
B. Customer base
C. Random demand
D. Fixed traffic patterns
Answer: C

NEW QUESTION: 3
A Maximo application is deployed on an Application Server (JVM). During normal use of the application, the system performance is inconsistent.
Which one of the application server components would most impact this?
A. Deployment manager configuration setting
B. JVM heap memory setting
C. Maximo database connection settings
D. HTTP Server timeout setting
Answer: B

NEW QUESTION: 4
Tran Holten, a quantitative analyst with Smith, Kleen & Beetchnutty Brokerage, has just been informed of an important error in one of his recent statistical endeavors. Specifically, in one hypothesis test, Mr. Holten rejected a null hypothesis that later was proven to be true. Which of the following best describes this type of error in hypothesis testing? Further, if the confidence level of the test were increased, would the probability of this error increase, decrease, or remain unchanged?
A. Type I error; remain unchanged
B. Type II error; remain unchanged
C. Type II error; decrease
D. Type I error; decrease
E. Type II error; increase
F. Type I error; increase
Answer: D
Explanation:
Explanation/Reference:
Explanation:
In this example, Tran Holten has incorrectly rejected a null hypothesis. This type of error in hypothesis testing is called a Type I error. In hypothesis testing, the Type I error is given much more attention than the Type II error. In most hypothesis tests, the probability of a null hypothesis is equal to the significance level of the test. A significance level of 0.01, for example, indicates that a 1% chance exists that the null hypothesis will be rejected when it is indeed true. Another way to think of the probability of a Type I error is to observe the following relationship:
{Probability of a Type I error = (1 - confidence level)}.
For example, a confidence level of 95% leaves a 5% probability of a Type I error occurring. If this confidence level were to increase to say, 98%, then the probability of a Type I error would reduce to 2%.
As you can see, there is a relationship between the confidence level of a test and the probability of a Type I error. If the confidence level of the test in this example were to increase, then the probability of a Type I error would decrease.