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| Section | Objectives |
|---|---|
| Topic 1: Deployment and operations | - Monitoring and maintenance
|
| Topic 2: Designing ML solutions | - ML architecture design
|
| Topic 3: ML model development | - Evaluation
|
| Topic 4: Data preparation and processing | - Data ingestion and pipelines
|
| Topic 5: ML pipeline automation and orchestration | - Pipeline design
|
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NEW QUESTION # 20
You need to use TensorFlow to train an image classification model. Your dataset is located in a Cloud Storage directory and contains millions of labeled images Before training the model, you need to prepare the dat a. You want the data preprocessing and model training workflow to be as efficient scalable, and low maintenance as possible. What should you do?
Answer: C
Explanation:
TFRecord is a binary file format that stores your data as a sequence of binary strings1. TFRecord files are efficient, scalable, and easy to process1. Sharding is a technique that splits a large file into smaller files, which can improve parallelism and performance2. Dataflow is a service that allows you to create and run data processing pipelines on Google Cloud3. Dataflow can create sharded TFRecord files from your images in a Cloud Storage directory4.
tf.data.TFRecordDataset is a class that allows you to read and parse TFRecord files in TensorFlow. You can use this class to create a tf.data.Dataset object that represents your input data for training. tf.data.Dataset is a high-level API that provides various methods to transform, batch, shuffle, and prefetch your data.
Vertex AI Training is a service that allows you to train your custom models on Google Cloud using various hardware accelerators, such as GPUs. Vertex AI Training supports TensorFlow models and can read data from Cloud Storage. You can use Vertex AI Training to train your image classification model by using a V100 GPU, which is a powerful and fast GPU for deep learning.
Reference:
TFRecord and tf.Example | TensorFlow Core
Sharding | TensorFlow Core
Dataflow | Google Cloud
Creating sharded TFRecord files | Google Cloud
[tf.data.TFRecordDataset | TensorFlow Core v2.6.0]
[tf.data: Build TensorFlow input pipelines | TensorFlow Core]
[Vertex AI Training | Google Cloud]
[NVIDIA Tesla V100 GPU | NVIDIA]
NEW QUESTION # 21
A logistics company needs a forecast model to predict next month's inventory requirements for a single item in
10 warehouses. A machine learning specialist uses Amazon Forecast to develop a forecast model from 3 years of monthly data. There is no missing data. The specialist selects the DeepAR+ algorithm to train a predictor. The predictor means absolute percentage error (MAPE) is much larger than the MAPE produced by the current human forecasters.
Which changes to the CreatePredictor API call could improve the MAPE? (Choose two.)
Answer: B,C
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/forecast/latest/dg/forecast.dg.pdf
NEW QUESTION # 22
Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: ['driversjicense', 'passport', 'credit_card']. Which loss function should you use?
Answer: C
Explanation:
Categorical cross-entropy is a loss function that is suitable for multi-class classification problems, where the target variable has more than two possible values. Categorical cross-entropy measures the difference between the true probability distribution of the target classes and the predicted probability distribution of the model. It is defined as:
L - sum(y_i * log(p_i))
where y_i is the true probability of class i, and p_i is the predicted probability of class i. Categorical cross-entropy penalizes the model for making incorrect predictions, and encourages the model to assign high probabilities to the correct classes and low probabilities to the incorrect classes.
For the use case of building a model that predicts whether images contain a driver's license, passport, or credit card, categorical cross-entropy is the appropriate loss function to use. This is because the problem is a multi-class classification problem, where the target variable has three possible values: ['drivers_license', 'passport', 'credit_card']. The label map is a list that maps the class names to the class indices, such that 'drivers_license' corresponds to index 0, 'passport' corresponds to index 1, and 'credit_card' corresponds to index 2. The model should output a probability distribution over the three classes for each image, and the categorical cross-entropy loss function should compare the output with the true labels. Therefore, categorical cross-entropy is the best loss function for this use case.
NEW QUESTION # 23
You work at an organization that maintains a cloud-based communication platform that integrates conventional chat, voice, and video conferencing into one platform. The audio recordings are stored in Cloud Storage. All recordings have an 8 kHz sample rate and are more than one minute long. You need to implement a new feature in the platform that will automatically transcribe voice call recordings into a text for future applications, such as call summarization and sentiment analysis. How should you implement the voice call transcription feature following Google- recommended best practices?
Answer: A
Explanation:
If possible, set the sampling rate of the audio source to 16000 Hz. Otherwise, set the sample_rate_hertz to match the native sample rate of the audio source (instead of re-sampling).
https://cloud.google.com/speech-to-text/docs/best-practices-provide-speech-data#sampling_rate
NEW QUESTION # 24
You work for an online travel agency that also sells advertising placements on its website to other companies. You have been asked to predict the most relevant web banner that a user should see next. Security is important to your company. The model latency requirements are 300ms@p99, the inventory is thousands of web banners, and your exploratory analysis has shown that navigation context is a good predictor. You want to Implement the simplest solution. How should you configure the prediction pipeline?
Answer: D
Explanation:
the inventory is thousands of web banners -> Bigtable
You want to Implement the simplest solution -> AI Platform Prediction
NEW QUESTION # 25
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