Google Machine Learning Engineer Exam Dumps 2022, Exercises of Computer Science

Download Google Machine Learning Engineer Exam Dumps 2022 and more Exercises Computer Science in PDF only on Docsity!

Google Machine Learning Engineer Exam Dumps 2022

Google Machine Learning Engineer Practice Tests 2022. Contains 110+ exam questions to pass the exam in first attempt. SkillCertPro offers real exam questions for practice for all major IT certifications.

 For a full set of 11 0 + questions. Go to https://skillcertpro.com/product/google-machine-learning-engineer-exam- questions/  SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.  It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.  SkillCertPro updates exam questions every 2 weeks.  You will get life time access and life time free updates  SkillCertPro assures 100% pass guarantee in first attempt.

Below are the free 10 sample questions.

Question 1:

Your team is designing a financial analysis model for a major Bank. The requirements are: Various banking applications will send transactions to the new system both in real-time and in batch in standard/normalized format The data will be stored in a repository Structured Data will be trained and retrained Labels are drawn from the data. You need to prepare the model quickly and decide to use Auto ML for structured Data. Which GCP Services could you use (pick 3)?

A. AutoML Tables B. AI Platform C. BigQuery ML D. Vertex AI

Answer: A, C, D

Explanation:

Auto ML Tables is aimed to automatically build and deploy models on your data in the fastest way possible.

It is integrated within BigQuery ML and is now available in the unified Vertex AI.

Vertex AI includes an AI Platform, too.

But AI Platform alone doesn‘t have any AutoML Services. So, B is wrong.

For any further detail:

https://cloud.google.com/automl-tables/docs/beginners-guide

https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml- syntax-create-automl

https://cloud.google.com/vertex-ai/docs/beginner/beginners-guide#text

Question 2:

You are starting to operate as a Data Scientist and are working on a deep neural network model with Tensorflow to optimize the level of customer satisfaction for after-sales services with the goal of creating greater client loyalty. You have to follow the entire lifecycle: model development, design, and training, testing, deployment, and retraining. You are looking for UI tools that can help you work and solve all issues faster. Which solutions can you adopt (pick 3)?

A. Tensorboard B. Jupyter notebooks C. KFServing D. Kubeflow UI E. Vertex AI

and made by CSV files. You are looking for the most convenient way to import and manage this type of data. Which is the best solution that you can adopt?

A. tf.RaggedTensor B. Tf.quantization C. tf.train.Feature D. tf.TFRecordReader

Answer: D

Explanation:

The TFRecord format is efficient for storing a sequence of binary and not-binary records using Protocol buffers for serialization of structured data.

A is wrong because RaggedTensor is a tensor with ragged dimensions, that is with different lengths like this: [[6, 4, 7, 4], [], [8, 12, 5], [9], []]

B is wrong because quantization is aimed to reduce CPU and TPU GCP latency, processing, and power.

C is wrong because tf.train is a feature for Graph-based Neural Structured model training

For any further detail:

https://www.tensorflow.org/tutorials/load_data/tfrecord

Question 4:

You work for an industrial company that wants to improve its quality system. It has developed its own deep neural network model with Tensorflow to identify the semi-finished products to be discarded with images taken from the production lines in the various production phases. You need to monitor the performance of your models and let them go faster.

Which is the best solution that you can adopt?

A. TFProfiler B. TF function C. TF Trace D. TF Debugger E. TF Checkpoint

Answer: A

Explanation:

TensorFlow Profiler is a tool for checking the performance of your TensorFlow models and helping you to obtain an optimized version.

In TensorFlow 2, the default is eager execution. So, one-off operations are faster, but recurring ones may be slower. So, you need to optimize the model.

B is wrong because the TF function is a transformation tool used to make graphs out of your programs. It helps to create performant and portable models but is not a tool for optimization.

C is wrong because TF tracing lets you record TensorFlow Python operations in a graph.

D is wrong because TF debugging is for Debugger V2 and creates a log of debug information.

E is wrong because Checkpoints catch the value of all parameters in a serialized SavedModel format.

Question 5:

You work for an important Banking group. The purpose of your current project is the automatic and smart acquisition of data from documents and modules of different types. You work on big datasets with a lot of private information that cannot be

Question 6:

You have a customer ranking ML model in production for an e-commerce site; the model used to work very well. You use GCP managed services, specifically AI Platform and Vertex AI. Suddenly, there is a sensible degradation in the quality of the inferences. You perform various checks, but the model seems to be perfectly fine. Finally, you control the input data and notice that the frequency distributions have changed for a specific feature. Which GCP service can be helpful for you to manage features in a more organized way?

A. Regularization against overfitting B. Feature Store C. Hyperparameters tuning D. Model Monitoring

Answer: B

Explanation:

Feature engineering means transforming input data, often strings, into a feature vector.

Lots of effort is spent in mapping categorical values in the best way: we have to convert strings to numeric values. We have to define a vocabulary of possible values, usually mapped to integer values.

We remember that in an ML model everything must be translated into numbers. Therefore it is easy to run into problems of this type.

Vertex Feature Store is a service to organize and store ML features through a central store.

This allows you to share and optimize ML features important for the specific environment and to reuse them at any time.

All these translate into the greater speed of the creation of ML services. But these also allow minimizing problems such as processing skew, which occurs when the distribution of data in production is different from that of training, often due to errors in the organization of the features.

For example, Training-serving skew may happen when your training data uses a different unit of measure than prediction requests.

So, Training-serving skew happens when you generate your training data differently than you generate the data you use to request predictions. For example, if you use an average value, and for training purposes, you average over 10 days, but you average over the last month when you request prediction.

A and C are wrong because the model is OK. So both Regularization against overfitting and Hyperparameters are tuned.

D is wrong because Monitor is suitable for Training-serving skew prevention, not organization.

Question 7:

You have a customer ranking ML model in production for an e-commerce site; the model used to work very well. You use GCP managed services, specifically AI Platform and Vertex AI. Suddenly there is a sensible degradation in the quality of the inferences. You perform various checks, but the model seems to be perfectly fine. Which of the following methods could you use to avoid such problems?

A. Regularization against overfitting B. Feature Store C. Hyperparameter tuning D. Model Monitoring

Answer: D

Explanation:

C. SageMaker D. Kubeflow

Answer: A

Explanation:

TensorFlow Extended (TFX) is a set of open-source libraries to build and execute ML pipelines in production. Its main functions are:

Metadata management

Model validation

Deployment

Production execution.

The libraries can also be used individually.

B is wrong because AI Platform is an integrated suite of ML managed products, and you are looking for a library.

AI Platform main functions are:

Train an ML model

Evaluate and tune model

Deploy models

Manage prediction: Batch, Online and monitoring

Manage model versions: workflows and retraining

Question 9:

Your company runs a big retail website. You develop many ML models for all the business activities. You migrated to Google Cloud when you were using Vertex AI. Your models are developed with PyTorch, TensorFlow and BigQueryML. You also use BigTable and CloudSQL, and of course Cloud Storage. In many cases,

the same data is used for multiple models and projects. And your data is continuously updated, sometimes in streaming mode. Which is the best way to organize the input data?

A. Dataflow per Data Transformation sia in streaming che batch B. CSV C. BigQuery D. Datasets E. BigTable

Answer: D

Explanation:

Vertex AI integrates the following elements:

Datasets: data, metadata and annotations, structured or unstructured. For all kinds of libraries.

Training pipelines to build an ML model

ML models, imported or created in the environment

Endpoints for inference

Because Datasets are suitable for all kinds of libraries, it is a useful abstraction for this requirement.

Question 10:

You are a Data Scientist and working on a project with PyTorch. You need to save the model you are working on because you have to cope with an urgency. You, therefore, need to resume your work later. What command will you use for this operation?

A. callbacks.ModelCheckpoint (keras) B. save