Weak law of large numbers for triangular arrays Let $X$ be a random variable with $E[X] = \mu < \infty$.
For $n=1,2,\dots$, construct a triangular array of random variables as
\begin{equation}
Y_{n,i} = X_i \frac{\sqrt{\mu}}{\sqrt{\sum_{j=1}^n X_j/n}}.
\end{equation}
Then, does the following hold?
\begin{equation}
\frac{1}{n} \sum_{i=1}^n Y_{n,i} \overset{p}{\to} \mu,
\end{equation}
where $\overset{p}{\to}$ denotes the convergence in probability.
Or more generally, for any bounded measurable function $f$, does the following hold?
\begin{equation}
\frac{1}{n} \sum_{i=1}^n f(Y_{n,i}) \overset{p}{\to} E[f(X)].
\end{equation}
If not, is there any necessary condition?
Thanks in advance.
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It appears that in the OP's question the condition that the $X_i$ be independent copies of $X$ is missing; let us assume this condition. To make (1) meaningful and (2) nontrivial, we also need to assume that $\mu>0$. In addition, we need to define $Y_{n,i}$ on the event $\{\sum_{j=1}^n X_j\le0\}$; let then define $Y_{n,i}$ as $0$ on this event (of vanishing probability as $n\to\infty$). 
Note first that (3) will fail to hold in general. E.g., suppose that $\PP(X=1)=\mu=1-\PP(X=0)$, where $\mu$ is any transcendental (non-algebraic) number in the interval $(0,1)$; for instance, one may take $\mu=e-2$. Then $\E X=\mu$ and all the nonzero values taken by $Y_{n,i}$ with nonzero probability are transcendental. Let now $f(y):=\ii{y=1}$ for all real $y$, where $\ii\cdot$ denotes the indicator. Then $\frac1n\sum_{i=1}^n f(Y_{n,i})$ is $0$ almost surely, whereas $\E f(X)=\PP(X=1)=\mu\ne0$, so that (3) fails to hold.  

The positive news is that, as will be shown here, (3) will hold -- even with the almost sure (a.s.) convergence (rather than just in probability) -- if $f$ is continuous, which will be henceforth assumed. Take any positive real numbers $a$ and $\eta$. Since $f$ is continuous, it is uniformly continuous on the interval $[-2a,2a]$. So, for some $\ep\in(0,1]$ and all $x,y$ in $[-2a,2a]$ such that $|x-y|\le a\ep$, we have $|f(y)-f(x)|<\eta$. Take now any $\de>0$ such that 
\begin{equation*}
 |x-\mu|<\de\implies\big|\sqrt{\tfrac\mu x}-1\big|<\ep.  \tag{4}
\end{equation*}
Now, introduce events 
\begin{equation*}
 A_n:=A_{n,\de}:=\{|\bar X_n-\mu|<\de\},\quad B_i:=\{|X_i|\le a\},
\end{equation*}
where $\bar X_n:=\frac1n\,\sum_{j=1}^n X_i$. By the strong law of large numbers,  $\ii{A_n^c}\to0$; everywhere here ${}^c$ denotes the complement and the convergence is for $n\to\infty$; the convergence of sequences of random variables (r.v.'s) is a.s. 
On the event $A_n\cap B_i$, we have $|X_i|\le a\le 2a$ and, by (4),
\begin{equation*}
 |Y_{n,i}-X_i|=|X_i|\Big|\frac{\sqrt{\mu}}{\sqrt{\bar X_n}}-1\Big|\le a\ep   
\end{equation*}
and hence also 
$|Y_{n,i}|\le a(1+\ep)\le2a$. 
So, by the mentioned uniform continuity of $f$, 
\begin{equation*}
 |f(Y_{n,i})-f(X_i)|<\eta\quad\text{on the event}\quad A_n\cap B_i. 
\end{equation*}
Introduce now the r.v.'s 
\begin{equation*}
 Z_n:=\frac1n\sum_1^n f(Y_{n,i}),\quad 
 T_n:=\frac1n\sum_1^n f(Y_{n,i})\ii{B_i}, 
\end{equation*}
\begin{equation*}
 V_n:=\frac1n\sum_1^n f(X_i),\quad 
 W_n:=\frac1n\sum_1^n f(X_i)\ii{B_i}. 
\end{equation*}
Since the function $f$ was assumed to be bounded, we have $|f|\le M$ for some real $M>0$. Therefore, 
\begin{align*}
 &|Z_n-T_n|\le\frac Mn\sum_1^n \ii{B_i^c}\to M\PP(B_1^c)=M\PP(|X|>a), \\ 
 &|T_n-T_n\ii{A_n}|=|T_n|\ii{A_n^c}\le M\ii{A_n^c}\to0, \\ 
 &|T_n\ii{A_n}-W_n\ii{A_n}|\le \frac1n\sum_1^n |f(Y_{n,i})-f(X_i)|\ii{A_n\cap B_i}\le\eta, \\  &|W_n\ii{A_n}-W_n|=|W_n|\ii{A_n^c}\le M\ii{A_n^c}\to0, \\  
 &|W_n-V_n|\le\frac Mn\sum_1^n \ii{B_i^c}\to M\PP(B_1^c)=M\PP(|X|>a), \\  
 &V_n\to\E f(X).   
\end{align*}
Thus, 
\begin{equation*}
 \limsup_n\Big|\frac1n\sum_1^n f(Y_{n,i})-\E f(X)\Big|
 =\limsup_n\Big|Z_n-\E f(X)\Big|
 \le2M\PP(|X|>a)+\eta. 
\end{equation*}
Letting now $a\to\infty$ and $\eta\downarrow0$, we see that indeed a.s. 
\begin{equation*}
 \frac1n\sum_1^n f(Y_{n,i})\to\E f(X).  
\end{equation*}
