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Free TensorFlow Developer practice test

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Every answer explained with source-backed reasoning No guessing Progress tracked Questions updated at May 12, 2026, 6:24 PM CDT
Exam breakdown Top domains in this TensorFlow Developer bank
TensorFlow Fundamentals 27%
About 40 items in this bank
Training And Evaluation 16%
About 23 items in this bank
Data Pipelines 15%
About 22 items in this bank

What TensorFlow Developer covers: TensorFlow Fundamentals (27%) • Training And Evaluation (16%) • Data Pipelines (15%)

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TensorFlow Developer

TensorFlow Developer Professional Certificate

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Question 1 of 10
Objective tf.018 Image Classification And Computer Vision

During feature extraction with a pretrained MobileNetV2 base, which setting keeps the pretrained base weights fixed?

Concept tested: Image Classification And Computer Vision

A. Correct: Setting the pretrained base model to non-trainable prevents its weights from being updated during feature extraction.

B. Incorrect: Run_eagerly changes execution style, not whether pretrained layers are frozen.

C. Incorrect: Classifier activation affects output behavior, not trainability of the base network.

D. Incorrect: Validation_steps controls evaluation iteration count and does not freeze weights.

Why this matters: This matters because feature extraction depends on preserving useful pretrained representations while training a smaller task-specific head.
Question 2 of 10
Objective tf.015 Data Pipelines

According to the TensorFlow data performance guide, which method can be used to enhance pipeline efficiency by caching preprocessed data in memory for reuse across multiple epochs?

Concept tested: Data Pipelines

A. Correct: Caching preprocessed data with Dataset.cache improves pipeline performance by avoiding redundant preprocessing across multiple epochs.

B. Incorrect: Dataset.batch groups elements into fixed-size batches but does not cache them for reuse, thus it doesn't enhance efficiency across epochs.

C. Incorrect: Dataset.prefetch overlaps preprocessing tasks with model execution to optimize input pipeline performance during training, but it does not cache data.

D. Incorrect: Dataset.map applies a function to each element of the dataset but does not involve caching preprocessed data for reuse.

Why this matters: This matters because the wrong choice changes how technicians or teams configure, troubleshoot, or support Dataset.cache.
Question 3 of 10
Objective tf.019 Images And Vision

When fine-tuning a pretrained model for an image classification task, which method is recommended to update only the last few layers of the classifier part?

Concept tested: Images And Vision

A. Correct: Unfreeze the last few layers and keep others frozen because this approach enables targeted updates to the classifier while maintaining the integrity of earlier feature detectors.

B. Incorrect: Train all layers simultaneously with a low learning rate because training all layers at once can lead to overfitting due to the large number of parameters being adjusted.

C. Incorrect: Replace the entire classifier with a new dense layer because replacing the classifier entirely would discard valuable pre-trained features and require extensive retraining.

D. Incorrect: Freeze all layers except for the first convolutional block because unfreezing only early layers is less effective for fine-tuning tasks that benefit from later feature extraction.

Why this matters: This matters because the wrong choice changes how technicians or teams configure, troubleshoot, or support Unfreeze the last few layers and keep others frozen.
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Question 4 of 10
Objective tf.027 Saving Serving And Deployment

According to the TensorFlow data input pipeline guide, which method is recommended for creating a TensorFlow dataset from a Python generator?

Concept tested: Saving Serving And Deployment

A. Correct: `from_generator()` is recommended for creating a TensorFlow dataset from a Python generator.

B. Incorrect: `from_tensor_slices()` creates datasets from NumPy arrays or tensors, not generators.

C. Incorrect: `make_one_shot_iterator()` is used to create an iterator that reads the entire dataset in one go and does not support dynamic data generation.

D. Incorrect: `initializable_iterator()` requires manual initialization with a session run call and does not directly work with Python generators.

Why this matters: This matters because the wrong choice changes how technicians or teams configure, troubleshoot, or support from_generator().
Question 5 of 10
Objective tf.002 TensorFlow Fundamentals

According to the TensorFlow variable guide, what property makes `tf.Variable` suitable for trainable parameters?

Concept tested: TensorFlow Fundamentals

A. Correct: The guide explains that a tf.Variable is a tensor whose value can be changed by running ops on it.

B. Incorrect: This describes initialization only and misses the key point that Variables keep changing during training.

C. Incorrect: Constants are immutable, while Variables are specifically meant to be updated.

D. Incorrect: Shape declaration is not the primary purpose of `tf.Variable`.

Why this matters: This matters because TensorFlow Fundamentals questions test whether Its value can be changed by running ops on it fits the scenario's constraints, not just whether the term sounds familiar.
Question 6 of 10
Objective tf.008 Training And Evaluation

When using the `evaluate` method in TensorFlow Keras to assess a model's performance, which parameter is used to specify the validation data?

Concept tested: Training And Evaluation

A. Correct: The `validation_data` parameter specifies the data to be used for evaluation during training. This allows you to assess model performance on unseen data, which helps prevent overfitting.

B. Incorrect: The `metrics` parameter defines the metrics that should be calculated during evaluation but does not specify validation data.

C. Incorrect: The `loss` parameter specifies the loss function used for training and does not relate to validation data.

D. Incorrect: The `batch_size` parameter controls the number of samples processed before updating model weights, which is unrelated to specifying validation data.

Why this matters: This matters because the wrong choice changes how technicians or teams configure, troubleshoot, or support validation_data=(x_val, y_val).
Question 7 of 10
Objective tf.023 Text Structured And Sequence Data

Why wrap an LSTM with Bidirectional in the TensorFlow text classification model?

Concept tested: Text Structured And Sequence Data

A. Correct: Bidirectional runs a recurrent layer over the sequence in both directions.

B. Incorrect: Label conversion is not the role of a recurrent layer wrapper.

C. Incorrect: Dataset caching is handled by tf.data pipeline methods.

D. Incorrect: Freezing filters is a transfer-learning image-model concern.

Why this matters: This matters because directionality changes what context a sequence model can use for classification.
Question 8 of 10
Objective tf.016 Image Classification And Computer Vision

In the image classification tutorial, how is the prediction array used to choose the model result for an input image?

Concept tested: Image Classification And Computer Vision

A. Correct: The largest value in the prediction output identifies the class the model considers most likely.

B. Incorrect: Averaging scores would lose the per-class ranking needed for classification.

C. Incorrect: Class_names only maps indices to labels; it does not decide the prediction.

D. Incorrect: Training labels are not used to classify a new input image.

Why this matters: This matters because interpreting model outputs correctly is part of turning raw predictions into useful application behavior.
Question 9 of 10
Objective tf.012 Data Pipelines

In TensorFlow, which method is used to apply a function that reshapes each element of a dataset before it reaches the model?

Concept tested: Data Pipelines

A. Correct: Dataset.map applies a function to each element of the dataset, allowing for transformations such as reshaping before feeding into the model.

B. Incorrect: Dataset.shuffle randomly shuffles elements in the dataset but does not apply a transformation function.

C. Incorrect: Dataset.batch groups elements from the dataset into batches but does not reshape individual elements.

D. Incorrect: Dataset.prefetch fetches the next element of an input pipeline ahead of time to improve performance, without applying transformations.

Why this matters: This matters because the wrong choice changes how technicians or teams configure, troubleshoot, or support Dataset.map.
Question 10 of 10
Objective tf.020 Images And Vision

According to the TensorFlow load images tutorial, which method is used to load image datasets from a directory containing subdirectories of images labeled by class name?

Concept tested: Images And Vision

A. Correct: It correctly identifies the method used to load image datasets from a directory containing subdirectories of images labeled by class name, as per the TensorFlow tutorial.

B. Incorrect: Tf.data.Dataset.from_tensor_slices() is used for creating datasets from tensor slices and not specifically for loading images from directories.

C. Incorrect: Tf.io.read_file() reads the contents of a file into a string Tensor but does not load image datasets from directories.

D. Incorrect: Tf.image.decode_jpeg() decodes JPEG-encoded images to 3-D uint8 tensors and is used for decoding images, not loading them from directories.

Why this matters: This matters because the wrong choice changes how technicians or teams configure, troubleshoot, or support tf.keras.preprocessing.image.ImageDataGenerator.flow_from_dir....
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148 verified questions are currently in the live bank. Questions updated at May 12, 2026, 6:24 PM CDT. The daily set rotates at 10:00 AM local time, and each explanation links back to the source used to write it. Use the web set for quick practice, then switch to the app when available for larger banks and deeper review.

Careers and fields this exam supports

TensorFlow Developer fits hands-on model builders who want stronger practical fluency with training, evaluation, and neural-network workflows.

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  • Where it shows up: deep learning, model training, neural networks, and applied ML development.
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What matters more on TensorFlow Developer

TensorFlow Developer is easiest once you understand what this exam is really rewarding beyond surface memorization.

  • Current emphasis in this bank: TensorFlow Fundamentals (27%).
  • Questions in this DeepLearning.AI lane usually separate the right answer from the merely familiar answer by scenario fit, scope, and the exact decision the exam is testing.
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Common mistakes on TensorFlow Developer

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  • Memorizing isolated terms without checking why the right answer wins over the other answer choices in the same scenario.
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