GOOGLE PROFESSIONAL-MACHINE-LEARNING-ENGINEER TEST VALID & PROFESSIONAL-MACHINE-LEARNING-ENGINEER EXAMCOLLECTION VCE

Google Professional-Machine-Learning-Engineer Test Valid & Professional-Machine-Learning-Engineer Examcollection Vce

Google Professional-Machine-Learning-Engineer Test Valid & Professional-Machine-Learning-Engineer Examcollection Vce

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Google Professional-Machine-Learning-Engineer Certification Exam is intended for machine learning engineers, data scientists, and software developers who want to demonstrate their expertise in building and deploying machine learning models on Google Cloud Platform. Professional-Machine-Learning-Engineer exam covers a wide range of topics, including data preparation and analysis, feature engineering, model selection and training, model evaluation and optimization, and deploying and managing machine learning models on Google Cloud Platform.

Google Professional Machine Learning Engineer exam is an excellent way for experienced machine learning professionals to demonstrate their expertise in this field and to enhance their career prospects. Whether you are looking to advance your career or to simply stay up-to-date with the latest trends and technologies in machine learning, this certification program is a valuable investment in your professional development.

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Google Professional Machine Learning Engineer Sample Questions (Q288-Q293):

NEW QUESTION # 288
You are developing an ML model using a dataset with categorical input variables. You have randomly split half of the data into training and test sets. After applying one-hot encoding on the categorical variables in the training set, you discover that one categorical variable is missing from the test set. What should you do?

  • A. Apply one-hot encoding on the categorical variables in the test data.
  • B. Use sparse representation in the test set
  • C. Randomly redistribute the data, with 70% for the training set and 30% for the test set
  • D. Collect more data representing all categories

Answer: A

Explanation:
The best option for dealing with the missing categorical variable in the test set is to apply one-hot encoding on the categorical variables in the test data. This option has the following advantages:
* It ensures the consistency and compatibility of the data format for the ML model, as the one-hot encoding transforms the categorical variables into binary vectors that can be easily processed by the model. By applying one-hot encoding on the categorical variables in the test data, you can match the number and order of the features in the test data with the training data, and avoid any errors or discrepancies in the model prediction.
* It preserves the information and relevance of the data for the ML model, as the one-hot encoding creates a separate feature for each possible value of the categorical variable, and assigns a value of 1 to the
* feature corresponding to the actual value of the variable, and 0 to the rest. By applying one-hot encoding on the categorical variables in the test data, you can retain the original meaning and importance of the categorical variable, and avoid any loss or distortion of the data.
The other options are less optimal for the following reasons:
* Option A: Randomly redistributing the data, with 70% for the training set and 30% for the test set, introduces additional complexity and risk. This option requires reshuffling and splitting the data again, which can be tedious and time-consuming. Moreover, this option may not guarantee that the missing categorical variable will be present in the test set, as it depends on the randomness of the data distribution. Furthermore, this option may affect the quality and validity of the ML model, as it may change the data characteristics and patterns that the model has learned from the original training set.
* Option B: Using sparse representation in the test set introduces additional overhead and inefficiency.
This option requires converting the categorical variables in the test set into sparse vectors, which are vectors that have mostly zero values and only store the indices and values of the non-zero elements.
However, using sparse representation in the test set may not be compatible with the ML model, as the model expects the input data to have the same format and dimensionality as the training data, which uses one-hot encoding. Moreover, using sparse representation in the test set may not be efficient or scalable, as it requires additional computation and memory to store and process the sparse vectors.
* Option D: Collecting more data representing all categories introduces additional cost and delay. This option requires obtaining and labeling more data that contains the missing categorical variable, which can be expensive and time-consuming. Moreover, this option may not be feasible or necessary, as the missing categorical variable may not be available or relevant for the test data, depending on the data source or the business problem.


NEW QUESTION # 289
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?

  • A. There is training-serving skew in your production environment.
  • B. 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.
  • C. The RAND() function generated a number that is less than 0.2 in both instances, so every record in the validation table will also be in the training table.
  • D. There is not a sufficient amount of training data.

Answer: B

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.
Reference:
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 # 290
You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%. How can you make your production model more accurate?

  • A. Split the training and test data based on time rather than a random split to avoid leakage
  • B. Add more data to your test set to ensure that you have a fair distribution and sample for testing
  • C. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.
  • D. Normalize the data for the training, and test datasets as two separate steps.

Answer: C


NEW QUESTION # 291
An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.
What should the Specialist do to meet these requirements?

  • A. Create word embedding vectors that store edit distance with every other word.
  • B. Download word embeddings pre-trained on a large corpus.
  • C. Produce a set of synonyms for every word using Amazon Mechanical Turk.
  • D. Create one-hot word encoding vectors.

Answer: D

Explanation:
Explanation/Reference: https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-object2vec-adds-new- features-that-support-automatic-negative-sampling-and-speed-up-training/


NEW QUESTION # 292
You work for a biotech startup that is experimenting with deep learning ML models based on properties of biological organisms. Your team frequently works on early-stage experiments with new architectures of ML models, and writes custom TensorFlow ops in C++. You train your models on large datasets and large batch sizes. Your typical batch size has 1024 examples, and each example is about 1 MB in size. The average size of a network with all weights and embeddings is 20 GB. What hardware should you choose for your models?

  • A. A cluster with 4 n1-highcpu-96 machines, each with 96 vCPUs and 86 GB RAM
  • B. A cluster with 2 a2-megagpu-16g machines, each with 16 NVIDIA Tesla A100 GPUs (640 GB GPU memory in total), 96 vCPUs, and 1.4 TB RAM
  • C. A cluster with an n1-highcpu-64 machine with a v2-8 TPU and 64 GB RAM
  • D. A cluster with 2 n1-highcpu-64 machines, each with 8 NVIDIA Tesla V100 GPUs (128 GB GPU memory in total), and a n1-highcpu-64 machine with 64 vCPUs and 58 GB RAM

Answer: B


NEW QUESTION # 293
......

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