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NEW QUESTION: 1
You need to write a method that retrieves data from a Microsoft Access 2013 database. The method must meet the following requirements:
Be read-only.
Be able to use the data before the entire data set is retrieved.
Minimize the amount of system overhead and the amount of memory usage.
Which type of object should you use in the method?
A. SqlDataAdapter
B. DataContext
C. OleDbDataReader
D. DbDataAdapter
Answer: C
Explanation:
OleDbDataReader Class
Provides a way of reading a forward-only stream of data rows from a data source.
Example:
OleDbConnection cn = new OleDbConnection();
OleDbCommand cmd = new OleDbCommand();
DataTable schemaTable;
OleDbDataReader myReader;
//Open a connection to the SQL Server Northwind database.
cn.ConnectionString = "Provider=SQLOLEDB;Data Source=server;User ID=login;
Password=password;Initial Catalog=Northwind";

NEW QUESTION: 2
Which message-handling behavior describes how Cisco Unity Connection Single Inbox works for Outlook users who do not have ViewMail installed?
A. Cisco Unity Connection voice messages are treated as voice messages.
B. Cisco Unity Connection voice messages are treated as emails with a WAV file attachment.
C. Cisco Unity Connection adds a Voice Outbox folder to the Outlook mailbox.
D. Replies to Cisco Unity Connection voice messages are sent to Exchange as well as the Cisco Unity Connection mailbox for the recipient.
E. Cisco Unity Connection voice messages are treated as emails without a WAV file attachment.
Answer: B
Explanation:
Explanation
Cisco unity here acts as an IMAP server for the outlook user who don't have view mail installed so user send their request as an IMAP client and unity will revert back with email and wav file attached to play.

NEW QUESTION: 3
3回の反復後、アジャイルチームを妨げる問題は、以前の反復で直面した問題と本質的に似ているように見えます。
過去の反復の回顧を調べると、「私たちにとってうまくいったこと」と「私たちにとってうまくいかなかったこと」というタイトルの列に編成された情報が明らかになります。この情報に基づいて、チームがキャプチャしなかったと推測できます。
A. ユーザーストーリー
B. 要件
C. アクションアイテム
D. マイルストーン
Answer: C

NEW QUESTION: 4
A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non- fraudulent observations and 1,000 fraudulent observations.
The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is 99.1%, but the Data Scientist has been asked to reduce the number of false negatives.

Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Choose two.)
A. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
B. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
C. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.
D. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.
E. Change the XGBoost eval_metric parameter to optimize based on AUC instead of error.
Answer: A,E