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In this article, we have present the most insightful and MUST attempt **questions on TensorFlow** with multiple options to choose from. Select an answer to find out if you got it right and get explanation for the answer.

This will help you get prepared for Interviews on TensorFlow, Google's Machine Learning framework.

## To do minimum pool (minpool) in TensorFlow, which op needs to be used?

tf.reduce_min

tf.maxpool

tf.minpool

tf.pool

Minimum pooling is not supported in TensorFlow so minpool() op does not exist. We can create an op for Minimum pooling using tf.reduce_min().

## Is TensorFlow available/ supported in C++?

#### the question text

Limited support

Full support

No

Depends on version

TensorFlow is available as C++ API though the most used API is the Python API. TensorFlow does not test its C++ API across builds so it is not recommended as it might be broken and has a limited support.

## Which padding type is not supported in TensorFlow's Conv2D op and variants?

VALID_EXPLICIT

SAME

VALID

EXPLICIT

TensorFlow's convolution ops like Conv2D supports implicit padding and has 3 types namely SAME, VALID and EXPLICIT.

## TensorFlow has an explicit pad type tf.pad. Which padding mode is not supported in it?

EXPLICIT

CONSTANT

REFLECT

SYMMETRIC

TensorFlow's pad op support 3 modes namely CONSTANT, REFLECT and SYMMETRIC. Types like SAME, VALID and EXPLICIT are not present as EXPLICIT is the default type.

## What is the latest stable version of TensorFlow?

v2.5.0

v1.15.5

v2.0.0

v1.4.2

As of 2021, the latest stable version of TensorFlow is v2.5.0.

## What is TensorBoard?

Visualization tool for TF

Debug tool for TF

Optimization tool for TF

Tool for training models

TensorBoard is TensorFlow's visualization toolkit for visualizing the models and various operations.

## Which library by Google is used to optimize memory allocation in TensorFlow?

TCMalloc

jemalloc

GCC

OneDNN

TCMalloc is a library by Google that is used to optimize memory allocation calls like malloc, realloc. It is recommended to use with TensorFlow but it not a necessary dependency.

## Which library by Intel is used to optimize TensorFlow?

OneDNN

nauta

MLIR

cerl

OneDNN is a library by Intel that is linked with TensorFlow to optimize Inference performance. OneDNN is officially supported by TensorFlow.

## What is servables in TensorFlow?

Objects to perform computation

Prepare production environment

Serve new algorithms

Support graph optimizations

Servables are objects to perform computation. It can contain a single operation or an entire model. Servable is a part of TensorFlow Serving which are used to design production environment.

## Which function destroyes a variable in TensorFlow?

#### the question text

tf.Session.close()

tf.Variable()

tf.debugging.assert_near

tf.Session.reset()

A variable in TensorFlow is created using tf.Variable.initializer() and it has its scope in current session. The variable is destroyed when tf.Session.close() is called.

## Value of which one of the following cannot be changed?

tf.placeholder

tf.variable

tf.constant

tf.Conv2D

The value of tf.placeholder cannot be changed once created.

## How to convert Numpy array to TensorFlow tensor?

tf.convert_to_tensor()

tf.make_ndarray()

tf.constant()

np.array()

tf.convert_to_tensor() is used to convert various objects to Tensor objects. The function can accept Tensor objects, numpy array, Python lists and Python scalars.

## tf.dtypes defines the datatype in a TensorFlow tensor. How many datatypes are supported in TensorFlow?

30

8

16

42

tf.dtypes supports 30 different types including tf.qint32, tf.qint8, float32 and other types.

## What is the step known as where graph optimizations are done by TensorFlow in runtime?

Graph layout pass

Graph tuning pass

Graph compression pass

Double pass

Graph layout pass is the step where operations in a model are optimized by TensorFlow in runtime before execution. These optimizations include merging multiple operations, removing redundant operations, replacing operations with optimized versions and others.

## What is MLIR in TensorFlow used for?

Defining common optimizations

Writing models

An graph optimization technique

Another programming language

MLIR (Multi-level intermediate representation) is an intermediate representation (IR) system between a language (like C) or library (like TensorFlow) and the compiler backend (like LLVM). It allows code reuse between different compiler stack of different languages and other performance and usability benefits.

## Which function in TensorFlow is used to convert FP32 data to INT8 data?

QuantizeV2

Dequantize

QuantizedConv2D

Convert

QuantizeV2 operation in TensorFlow is used to convert FP32 data to INT8 data. It represents the quantized operation (first operation) in Quantization.

## How to use TensorFlow v1.x API in TensorFlow v2.x build?

tf.compat.v1

tf.compat.v2

tf.v1

tf.forward_compatible

compat.v1 and compat.v2 provides TensorFlow v1.x and v2.x APIs for forward and backward compatibility.

## How to get the TensorFlow version?

tf.version.VERSION

tf.version

tf.VERSION

Not possible

tf.version.VERSION is used to get the version number of TensorFlow. It returns a value like 2.5.0. tf.version have other attributes like COMPILER_VERSION, GIT_VERSION, GRAPH_DEF_VERSION, GRAPH_DEF_VERSION_MIN_CONSUMER and GRAPH_DEF_VERSION_MIN_PRODUCER.

## What is eager execution in TensorFlow?

#### the question text

Execute operations immediately

Create graph before execution

Do advanced optimization

Compile time memory allocation

Eager execution in TensorFlow is an approach where operations are executed immediately without creating graph of the model. It eliminates graph optimizations. Eager execution is enabled by default in TensorFlow v2.x.

## To do matrix multiplication in TensorFlow, which op needs to be used?

tf.matmul

tf.conv2d

tf.maxpool

tf.contrib.layers.fully_connected

tf.matmul() op is used to do matrix multiplication in TensorFlow.

## What data format is used in TensorFlow in its ops by default?

NHWC

NCHW

NCHWc8

NCDHW

NHWC data format is used in TensorFlow. NHWC stands for Number of batches, Height, Widht, Channel.

With these questions at OpenGenus, you must have a strong hold on TensorFlow. Enjoy.