[Oct-2021] Download Real Google Professional-Machine-Learning-Engineer Exam Dumps Test Engine Exam Questions
New Professional-Machine-Learning-Engineer exam dumps Use Updated Google Exam
Professional Machine Learning Engineer - Google Certified salary
The estimated average salary of Professional Machine Learning Engineer - Google is listed below:
- India: 8,580,000 INR
- Europe: 97,000 EURO
- England: 87,200 POUND
- United States: 114,000 USD
The benefit of obtaining the Professional Machine Learning Engineer - Google Certification
- Professional Cloud Architect was the highest paying certification of 2020 and 2019
- 87% of Google Cloud certified individuals are more confident about their cloud skills
- More than 1 in 4 of Google Cloud certified individuals took on more responsibility or leadership roles at work
How to book the Professional Machine Learning Engineer - Google
To apply for the Professional Machine Learning Engineer - Google, You have to follow these steps:
- Step 1: Go to the Google Official Site
- Step 2: Read the instruction carefully
- Step 3: Follow the given steps
- Step 4: Apply for the Professional Machine Learning Engineer Exam
NEW QUESTION 37
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. Normalize the data for the training, and test datasets as two separate steps.
- 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. Add more data to your test set to ensure that you have a fair distribution and sample for testing
Answer: C
NEW QUESTION 38
You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations Al to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?
- A. Use the "Frequently Bought Together' recommendation type to increase the shopping cart size for each order.
- B. Import your user events and then your product catalog to make sure you have the highest quality event stream
- C. Use the "Other Products You May Like" recommendation type to increase the click-through rate
- D. Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.
Answer: B
NEW QUESTION 39
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. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.
- C. Normalize the data for the training, and test datasets as two separate steps.
- D. Add more data to your test set to ensure that you have a fair distribution and sample for testing
Answer: D
NEW QUESTION 40
You started working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?
- A. Address data leakage by applying nested cross-validation during model training.
- B. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.
- C. Address the model overfitting by using a less complex algorithm.
- D. Address data leakage by removing features highly correlated with the target value.
Answer: A
NEW QUESTION 41
You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent.
Which data transformation strategy would likely improve the performance of your classifier?
- A. Use one-hot encoding on all categorical features.
- B. Z-normalize all the numeric features.
- C. Oversample the fraudulent transaction 10 times.
- D. Write your data in TFRecords.
Answer: C
NEW QUESTION 42
You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?
Choose 2 answers
- A. Set the early stopping parameter to TRUE
- B. Decrease the maximum number of trials during subsequent training phases.
- C. Change the search algorithm from Bayesian search to random search.
- D. Decrease the range of floating-point values
- E. Decrease the number of parallel trials
Answer: C,D
NEW QUESTION 43
You developed an ML model with Al Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine (GKE). Your goal is to improve the serving latency without changing the underlying infrastructure. What should you do?
- A. Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes
- B. Significantly increase the max_batch_size TensorFlow Serving parameter
- C. Switch to the tensorflow-model-server-universal version of TensorFlow Serving
- D. Significantly increase the max_enqueued_batches TensorFlow Serving parameter
Answer: B
NEW QUESTION 44
Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors. While exploring the data, the Specialist notices that the magnitude of the input features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the model.
What should the Specialist do to prepare the data for model training?
- A. Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.
- B. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.
- C. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.
- D. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.
Answer: B
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/machine-learning/latest/dg/data-transformations-reference.html
NEW QUESTION 45
You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?
- A. Use Principal Component Analysis to eliminate the least informative features.
- B. Use L1 regularization to reduce the coefficients of uninformative features to 0.
- C. Use an iterative dropout technique to identify which features do not degrade the model when removed.
- D. After building your model, use Shapley values to determine which features are the most informative.
Answer: D
NEW QUESTION 46
A data scientist wants to use Amazon Forecast to build a forecasting model for inventory demand for a retail company. The company has provided a dataset of historic inventory demand for its products as a .csv file stored in an Amazon S3 bucket. The table below shows a sample of the dataset.
How should the data scientist transform the data?
- A. Use a Jupyter notebook in Amazon SageMaker to transform the data into the optimized protobuf recordIO format. Upload the dataset in this format to Amazon S3.
- B. Use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. Upload both datasets as .csv files to Amazon S3.
- C. Use a Jupyter notebook in Amazon SageMaker to separate the dataset into a related time series dataset and an item metadata dataset. Upload both datasets as tables in Amazon Aurora.
- D. Use AWS Batch jobs to separate the dataset into a target time series dataset, a related time series dataset, and an item metadata dataset. Upload them directly to Forecast from a local machine.
Answer: C
NEW QUESTION 47
You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories. What should you do?
- A. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.
- B. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
- C. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic
- D. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run
Answer: B
NEW QUESTION 48
You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations Al to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?
- A. Use the "Other Products You May Like" recommendation type to increase the click-through rate
- B. Import your user events and then your product catalog to make sure you have the highest quality event stream
- C. Use the "Frequently Bought Together' recommendation type to increase the shopping cart size for each order.
- D. Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.
Answer: C
Explanation:
Frequently bought together' recommendations aim to up-sell and cross-sell customers by providing product.
NEW QUESTION 49
You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?
- A. Use Al Platform Notebooks to run the classification model with pandas library
- B. Configure AutoML Tables to perform the classification task
- C. Use Al Platform to run the classification model job configured for hyperparameter tuning
- D. Run a BigQuery ML task to perform logistic regression for the classification
Answer: D
Explanation:
BigQuery ML supports supervised learning with the logistic regression model type.
NEW QUESTION 50
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?
- A. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Memorystore for writing and for reading the user's navigation context, and then deploy the model on Google Kubernetes Engine.
- B. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Cloud Bigtable for writing and for reading the user's navigation context, and then deploy the model on AI Platform Prediction.
- C. Embed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction.
- D. Embed the client on the website, and then deploy the model on AI Platform Prediction.
Answer: C
NEW QUESTION 51
A company wants to predict the sale prices of houses based on available historical sales data. The target variable in the company's dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable linear regression to predict house sale prices.
Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model's complexity?
- A. Plot a histogram of the features and compute their standard deviation. Remove features with low variance.
- B. Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.
- C. Plot a histogram of the features and compute their standard deviation. Remove features with high variance.
- D. Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.
Answer: B
NEW QUESTION 52
You are building a linear regression model on BigQuery ML to predict a customer's likelihood of purchasing your company's products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?
- A. Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5r and then use that number to represent the city in the model.
- B. Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.
- C. Use TensorFlow to create a categorical variable with a vocabulary list Create the vocabulary file, and upload it as part of your model to BigQuery ML.
- D. Create a new view with BigQuery that does not include a column with city information
Answer: A
NEW QUESTION 53
A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data.
The ingestion process must buffer and convert incoming records from JSON to a query-optimized, columnar format without data loss. The output datastore must be highly available, and Analysts must be able to run SQL queries against the data and connect to existing business intelligence dashboards.
Which solution should the Data Scientist build to satisfy the requirements?
- A. Use Amazon Kinesis Data Analytics to ingest the streaming data and perform real-time SQL queries to convert the records to Apache Parquet before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
- B. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and writes the data to a processed data location in Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
- C. Create a schema in the AWS Glue Data Catalog of the incoming data format. Use an Amazon Kinesis Data Firehose delivery stream to stream the data and transform the data to Apache Parquet or ORC format using the AWS Glue Data Catalog before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
- D. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and inserts it into an Amazon RDS PostgreSQL database. Have the Analysts query and run dashboards from the RDS database.
Answer: C
Explanation:
Explanation/Reference:
NEW QUESTION 54
You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf .data dataset?
Choose 2 answers
- A. Use the interleave option for reading data
- B. Increase the buffer size for the shuffle option.
- C. Decrease the batch size argument in your transformation
- D. Reduce the value of the repeat parameter
- E. Set the prefetch option equal to the training batch size
Answer: A,C
NEW QUESTION 55
You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?
- A. Redaction, reproducibility, and explainability
- B. Traceability, reproducibility, and explainability
- C. Differential privacy federated learning, and explainability
- D. Federated learning, reproducibility, and explainability
Answer: A
NEW QUESTION 56
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