TensorFlow – One of the most famous deep learning framework.
Below is a list of Interview questions on TensorFlow and Keras
Sr No |
Question |
Option A |
Option B |
Option C |
Option D |
Answer |
1 |
Which tool is best suited for solving |
R |
Sk-learn |
Excel |
TensorFlow |
D |
2 |
A tensor is similar to |
Data Array |
ANN Model |
SQL query |
Pythoncode |
A |
3 |
How calculations work in TensorFlow |
Through vector multiplications |
Through RDDs |
Through Computational Graphs |
Through map reduce tasks |
C |
4 |
In TensorFlow, what is the used of a |
The current work space session for storing |
We launch the graph in a session |
A session is used to download the data |
A session is used for exporting data out of |
B |
5 |
What does feed_dict do? |
Feeds external data into computational |
Creates a new place holder |
Creates a new tensor |
Creates a new session |
A |
6 |
out=tf.add(tf.matmul(X,W), b) |
Logistic Regression Equaltion |
Deep ANN equation |
Random Forest Equation |
Linear Regression equation |
D |
7 |
tf.reduce_sum(tf.square(out-Y)) |
Linear Model equation |
Maximum Entropy loss function |
Squared Error loss function |
Feed_dict process |
C |
8 |
How can we improve the calculation speed in TensorFlow, without losing accuracy? |
Using GPU |
By doing random sampling on Tensors |
By removing few nodes from computational |
by removing the hidden layers |
A |
9 |
Kears is a deep learning framework on which tool |
R |
TensorFlow |
SAS |
Azure |
B |
10 |
What is the meaning of model=sequentil() in Keras? |
No such code in Keras |
Keras should be used only for sequential |
Keras builds sequential models |
creates a computational graph |
C |
11 |
Which tool is NOT Suited for building |
Python |
TensorFlow |
Excel |
Keras |
C |
12 |
Can we have multidimentional tensors |
No tensor can have maximum two dimentions |
Possible only in image data |
Yes possible |
Possible only in geo tagged data |
C |
13 |
Why Tensorflow uses computational |
Tensors are nothing but computational |
Gaphs are easy to plot |
There is no such concept of computational |
Calculations can be done in parallel |
D |
14 |
How do we perform caculations in |
We launch the computational graph in a |
We launch the sesssion inside a |
By creating multiple tensors |
By creating data frames |
A |
15 |
How do you feed external data into |
by using impoar data command |
by using feed_dict |
by using read data function |
Not possible |
B |
16 |
out=tf.sigmoid(tf.add(tf.matmul(X,W), |
Logistic Regression Equaltion |
Deep ANN equation |
Random Forest Equation |
Linear Regression equation |
A |
17 |
C=-tf.reduce_sum(Y*tf.log(out)) |
C is a logistc regression line equation |
C is a squared error loss function |
C is a cross entropy loss functiom |
C is a linear regression line equation |
C |
18 |
Can we use GPU for faster computations in TensorFlow |
No, not possible |
Possible only on cloud |
Possible only with small datasets |
Yes, possible |
D |
19 |
Which tool is a deep learning wrapper on TensorFlow |
Python |
Keras |
PyTourch |
Azure |
B |
20 |
How deep learning models are built on Keras |
by using sequential models |
by using feed_dict |
by creating place holders and computational |
by creating data frames |
A |
More interview questions in our android app
statinfer app – A new way to learn and test your knowledge on data science
https://play.google.com/store/apps/details?id=com.learn.statinfer1
A fun way of learning data science. App contains interview questions
]]>
]]>
Retail_invoice = LOAD '/Retail_invoice_hdfs' USING PigStorage('\t') as (uniq_idi:chararray, InvoiceNo:chararray, StockCode:chararray, Description:chararray,Quantity:INT);
DESCRIBE Retail_invoice;
Retail_Customer = LOAD '/Retail_Customer_hdfs' USING PigStorage('\t') as (uniq_idc:chararray, InvoiceDate:chararray, UnitPrice:INT, CustomerID:chararray,Country:chararray);
DESCRIBE Retail_Customer;
Left_join = JOIN Retail_invoice BY uniq_idi LEFT OUTER, Retail_Customer BY uniq_idc;
DESCRIBE Left_join;
DUMP Left_join;
Right_join = JOIN Retail_invoice BY uniq_idi RIGHT OUTER, Retail_Customer BY uniq_idc;
DESCRIBE Right_join;
DUMP Right_join;
Full_join = JOIN Retail_invoice BY uniq_idi FULL, Retail_Customer BY uniq_idc;
DESCRIBE Full_join;
DUMP Full_join;