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The Google Professional-Machine-Learning-Engineer Exam consists of a variety of question types, including multiple choice, multiple select, and scenario-based questions. Professional-Machine-Learning-Engineer exam covers a range of topics, including data preparation, model training, model evaluation, model deployment, and monitoring and maintenance of machine learning models. Candidates are also expected to have a solid understanding of the Google Cloud Platform and its machine learning services, such as Cloud Machine Learning Engine and AutoML.
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Another significant challenge of undertaking a Google Professional-Machine-Learning-Engineer exam is defining clear goals. Many students get bogged down by the volume of material they need to learn and lose sight of their goals. Thus, our Google Professional-Machine-Learning-Engineer Real Exam Questions in three formats provide you with the clear cut Professional-Machine-Learning-Engineer preparation materials and defined goals to comprehensively prepare in the shortest possible time.
Google Professional Machine Learning Engineer certification is highly valued by employers, as it demonstrates the candidate's ability to design, build, and deploy machine learning models using the Google Cloud Platform. Google Professional Machine Learning Engineer certification is also a testament to the candidate's commitment to staying up-to-date with the latest advancements in the field of machine learning.
NEW QUESTION # 275
You need to execute a batch prediction on 100 million records in a BigQuery table with a custom TensorFlow DNN regressor model, and then store the predicted results in a BigQuery table. You want to minimize the effort required to build this inference pipeline. What should you do?
Answer: C
Explanation:
* Option A is correct because importing the TensorFlow model with BigQuery ML, and running the ml.
predict function is the easiest way to execute a batch prediction on a large BigQuery table with a custom TensorFlow model, and store the predicted results in another BigQuery table. BigQuery ML allows you to import TensorFlow models that are stored in Cloud Storage, and use them for prediction with SQL queries 1 . The ml.predict function returns a table with the predicted values, which can be saved to another BigQuery table 2 .
* Option B is incorrect because using the TensorFlow BigQuery reader to load the data, and using the BigQuery API to write the results to BigQuery requires more effort to build the inference pipeline than option A. The TensorFlow BigQuery reader is a way to read data from BigQuery into TensorFlow datasets, which can be used for training or prediction 3 . However, this option also requires writing code to load the TensorFlow model, run the prediction, and use the BigQuery API to write the results back to BigQuery 4 .
* Option C is incorrect because creating a Dataflow pipeline to convert the data in BigQuery to TFRecords, running a batch inference on Vertex AI Prediction, and writing the results to BigQuery requires more effort to build the inference pipeline than option A. Dataflow is a service for creating and running data processing pipelines, such as ETL (extract, transform, load ) or batch processing 5 .
Vertex AI Prediction is a service for deploying and serving ML models for online or batch prediction.
However, this option also requires writing code to create the Dataflow pipeline, convert the data to TFRecords, run the batch inference, and write the results to BigQuery.
* Option D is incorrect because loading the TensorFlow SavedModel in a Dataflow pipeline, using the BigQuery I/O connector with a custom function to perform the inference within the pipeline, and writing the results to BigQuery requires more effort to build the inference pipeline than option A. The BigQuery I/O connector is a way to read and write data from BigQuery within a Dataflow pipeline.
However, this option also requires writing code to load the TensorFlow SavedModel, create the custom function for inference, and write the results to BigQuery.
References:
Importing models into BigQuery ML
Using imported models for prediction
TensorFlow BigQuery reader
BigQuery API
Dataflow overview
[Vertex AI Prediction overview]
[Batch prediction with Dataflow]
[BigQuery I/O connector]
[Using TensorFlow models in Dataflow]
NEW QUESTION # 276
You are experimenting with a built-in distributed XGBoost model in Vertex AI Workbench user-managed notebooks. You use BigQuery to split your data into training and validation sets using the following queries:
CREATE OR REPLACE TABLE 'myproject.mydataset.training' AS
(SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() <= 0.8);
CREATE OR REPLACE TABLE 'myproject.mydataset.validation' AS
(SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() <= 0.2);
After training the model, you achieve an area under the receiver operating characteristic curve (AUC ROC) value of 0.8, but after deploying the model to production, you notice that your model performance has dropped to an AUC ROC value of 0.65. What problem is most likely occurring?
Answer: D
Explanation:
The most likely problem is that the tables that you created to hold your training and validation records share some records, and you may not be using all the data in your initial table. This is because the RAND() function generates a random number between 0 and 1 for each row, and the probability of a row being in both the training and validation tables is 0.2 * 0.8 = 0.16, which is not negligible. This means that some of the records that you use to validate your model are also used to train your model, which can lead to overfitting and poor generalization. Moreover, the probability of a row being in neither the training nor the validation table is 0.2 *
0.2 = 0.04, which means that you are wasting some of the data in your initial table and reducing the size of your datasets. A better way to split your data into training and validation sets is to use a hash function on a unique identifier column, such as the following queries:
CREATE OR REPLACE TABLE 'myproject.mydataset.training' AS (SELECT * FROM 'myproject.
mydataset.mytable' WHERE MOD(FARM_FINGERPRINT(id), 10) < 8); CREATE OR REPLACE TABLE
'myproject.mydataset.validation' AS (SELECT * FROM 'myproject.mydataset.mytable' WHERE MOD (FARM_FINGERPRINT(id), 10) >= 8); This way, you can ensure that each row has a fixed 80% chance of being in the training table and a 20% chance of being in the validation table, without any overlap or omission.
References:
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
* Google Cloud launches machine learning engineer certification
* BigQuery ML: Splitting data for training and testing
* BigQuery: FARM_FINGERPRINT function
NEW QUESTION # 277
You are an ML engineer at a global car manufacture. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?
Answer: C
Explanation:
https://developers.google.com/machine-learning/crash-course/feature-crosses/check-your-understanding
NEW QUESTION # 278
You work at a bank. You need to develop a credit risk model to support loan application decisions You decide to implement the model by using a neural network in TensorFlow Due to regulatory requirements, you need to be able to explain the models predictions based on its features When the model is deployed, you also want to monitor the model's performance overtime You decided to use Vertex Al for both model development and deployment What should you do?
Answer: C
Explanation:
To develop a credit risk model that meets the regulatory requirements and can be monitored over time, you should follow these steps:
* Use Vertex Explainable AI with the sampled Shapley method. Vertex Explainable AI is a service that provides feature attributions for machine learning models, which can help you understand how each feature contributes to the prediction1. The sampled Shapley method is atechnique that estimates the Shapley values for each feature, which are based on the marginal contribution of each feature to the prediction across all possible feature subsets2. The sampled Shapley method is suitable for neural networks and other complex models, as it can capture the non-linear and interaction effects of the features3.
* Enable Vertex AI Model Monitoring to check for feature distribution drift. Vertex AI Model Monitoring is a service that helps you track and manage the performance and quality of your deployed models over time4. Feature distribution drift is a type of data drift that occurs when the distribution of the input features changes significantly from the training data, which can affect the model accuracy and reliability. By checking for feature distribution drift, you can detect when your model needs to be retrained or updated with new data.
References:
* 1: Introduction to Vertex Explainable AI | Vertex AI | Google Cloud
* 2: Shapley value - Wikipedia
* 3: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
* 4: Introduction to Vertex AI Model Monitoring | Vertex AI | Google Cloud
* [5]: Monitor models for data drift | Vertex AI | Google Cloud
NEW QUESTION # 279
You need to train a natural language model to perform text classification on product descriptions that contain millions of examples and 100,000 unique words. You want to preprocess the words individually so that they can be fed into a recurrent neural network. What should you do?
Answer: A
Explanation:
Option A is incorrect because creating a one-hot encoding of words, and feeding the encodings into your model is not an efficient way to preprocess the words individually for a natural language model. One-hot encoding is a method of representing categorical variables as binary vectors, where each element corresponds to a category and only one element is 1 and the rest are 01. However, this method is not suitable for high-dimensional and sparse data, such as words in a large vocabulary, because it requires a lot of memory and computation, and does not capture the semantic similarity or relationship between words2.
Option B is correct because identifying word embeddings from a pre-trained model, and using the embeddings in your model is a good way to preprocess the words individually for a natural language model. Word embeddings are low-dimensional and dense vectors that represent the meaning and usage of words in a continuous space3. Word embeddings can be learned from a large corpus of text using neural networks, such as word2vec, GloVe, or BERT4. Using pre-trained word embeddings can save time and resources, and improve the performance of the natural language model, especially when the training data is limited or noisy5.
Option C is incorrect because sorting the words by frequency of occurrence, and using the frequencies as the encodings in your model is not a meaningful way to preprocess the words individually for a natural language model. This method implies that the frequency of a word is a good indicator of its importance or relevance, which may not be true. For example, the word "the" is very frequent but not very informative, while the word "unicorn" is rare but more distinctive. Moreover, this method does not capture the semantic similarity or relationship between words, and may introduce noise or bias into the model.
Option D is incorrect because assigning a numerical value to each word from 1 to 100,000 and feeding the values as inputs in your model is not a valid way to preprocess the words individually for a natural language model. This method implies an ordinal relationship between the words, which may not be true. For example, assigning the values 1, 2, and 3 to the words "apple", "banana", and "orange" does not make sense, as there is no inherent order among these fruits. Moreover, this method does not capture the semantic similarity or relationship between words, and may confuse the model with irrelevant or misleading information.
Reference:
One-hot encoding
Word embeddings
Word embedding
Pre-trained word embeddings
Using pre-trained word embeddings in a Keras model
[Term frequency]
[Term frequency-inverse document frequency]
[Ordinal variable]
[Encoding categorical features]
NEW QUESTION # 280
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