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You are asked to develop a simple tag counter that will look for the occurrence of certain words or phrases in a piece of text.

You are given a paragraph as a string and a list of tag groups as a list of lists of strings. Write a <code>tag_counter</code> function that returns a list of integers with the number of times each tag in the tag group was used in the text.

Note: Your function should be case-insensitive with regards to the tag groups.

Example:

Input:

<pre tabindex="0" class="chroma"><code>tag_groups = [
 [&#39;data scientist&#39;, &#39;data analyst&#39;], 
 [&#39;data engineer&#39;, &#39;data wrangler&#39;], 
 [&#39;machine learning engineer&#39;], 
 [&#39;data&#39; , &#39;engineer&#39;]]
text = &#34;Today, with the advent of data science, different roles have emerged in the industry. Job postings are abundant of many names such as data scientist, data engineer, data wrangler, deep learning specialist, and machine learning specialist to name a few.&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def tag_counter(tag_groups,text) -&gt; [1, 2, 0, 5]
</code></pre>

Write a function that returns a list of integers with the number of times each tag in the tag group was used in the text

Tag Counter

Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value.

How would you assess the validity of the result?

Experiment Validity

Let’s say we have a table with an <code>id</code> and <code>name</code> fields. The table holds over 100 million rows and we want to sample a random row in the table without throttling the database.

Write a query to randomly sample a row from this table.

Input:

<code>big_table</code> table

<table>
<thead>
<tr>
<th>Columns</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>

Write a query to randomly sample a row from a big table.

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