Professional-Machine-Learning-Engineer Dumps for Pass Guaranteed - Pass Professional-Machine-Learning-Engineer Exam 2021
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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 19
You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?
- A. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
- B. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
- C. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
- D. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.
Answer: A
NEW QUESTION 20
A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.
Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population How should the Data Scientist correct this issue?
- A. Drop all records from the dataset where age has been set to 0.
- B. Use k-means clustering to handle missing features
- C. Replace the age field value for records with a value of 0 with the mean or median value from the dataset
- D. Drop the age feature from the dataset and train the model using the rest of the features.
Answer: A
Explanation:
Explanation
NEW QUESTION 21
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 22
You are training a TensorFlow model on a structured data set with 100 billion records stored in several CSV files. You need to improve the input/output execution performance. What should you do?
- A. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage
- B. Load the data into BigQuery and read the data from BigQuery.
- C. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)
- D. Load the data into Cloud Bigtable, and read the data from Bigtable
Answer: D
NEW QUESTION 23
Machine Learning Specialist is training a model to identify the make and model of vehicles in images. The Specialist wants to use transfer learning and an existing model trained on images of general objects. The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.
What should the Specialist do to initialize the model to re-train it with the custom data?
- A. Initialize the model with pre-trained weights in all layers including the last fully connected layer.
- B. Initialize the model with random weights in all layers and replace the last fully connected layer.
- C. Initialize the model with random weights in all layers including the last fully connected layer.
- D. Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.
Answer: D
Explanation:
Explanation/Reference:
NEW QUESTION 24
A Data Scientist needs to migrate an existing on-premises ETL process to the cloud. The current process runs at regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated output for downstream processing.
The Data Scientist has been given the following requirements to the cloud solution:
* Combine multiple data sources.
* Reuse existing PySpark logic.
* Run the solution on the existing schedule.
* Minimize the number of servers that will need to be managed.
Which architecture should the Data Scientist use to build this solution?
- A. Write the raw data to Amazon S3. Create an AWS Glue ETL job to perform the ETL processing against the input data. Write the ETL job in PySpark to leverage the existing logic. Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule. Configure the output target of the ETL job to write to a
"processed" location in Amazon S3 that is accessible for downstream use. - B. Write the raw data to Amazon S3. Schedule an AWS Lambda function to submit a Spark step to a persistent Amazon EMR cluster based on the existing schedule. Use the existing PySpark logic to run the ETL job on the EMR cluster. Output the results to a "processed" location in Amazon S3 that is accessible for downstream use.
- C. Write the raw data to Amazon S3. Schedule an AWS Lambda function to run on the existing schedule and process the input data from Amazon S3. Write the Lambda logic in Python and implement the existing PySpark logic to perform the ETL process. Have the Lambda function output the results to a "processed" location in Amazon S3 that is accessible for downstream use.
- D. Use Amazon Kinesis Data Analytics to stream the input data and perform real-time SQL queries against the stream to carry out the required transformations within the stream. Deliver the output results to a
"processed" location in Amazon S3 that is accessible for downstream use.
Answer: D
Explanation:
Explanation
NEW QUESTION 25
You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?
- A. Compare the mean average precision across the models using the Continuous Evaluation feature
- B. Compare the loss performance for each model on the validation data
- C. Compare the loss performance for each model on a held-out dataset.
- D. Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool
Answer: B
NEW QUESTION 26
A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep tracking application on AWS. The application collects device usage information from device users. The company's Data Science team is building a machine learning model to predict if and when a user will stop utilizing the company's devices. Predictions from this model are used by a downstream application that determines the best approach for contacting users.
The Data Science team is building multiple versions of the machine learning model to evaluate each version against the company's business goals. To measure long-term effectiveness, the team wants to run multiple versions of the model in parallel for long periods of time, with the ability to control the portion of inferences served by the models.
Which solution satisfies these requirements with MINIMAL effort?
- A. Build and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker endpoints, one for each model. Programmatically control invoking different models for inference at the application layer.
- B. Build and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint configuration with multiple production variants. Programmatically control the portion of the inferences served by the multiple models by updating the endpoint configuration.
- C. Build and host multiple models in Amazon SageMaker. Create a single endpoint that accesses multiple models. Use Amazon SageMaker batch transform to control invoking the different models through the single endpoint.
- D. Build and host multiple models in Amazon SageMaker Neo to take into account different types of medical devices. Programmatically control which model is invoked for inference based on the medical device type.
Answer: C
NEW QUESTION 27
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 28
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?
- A. Categorical cross-entropy
- B. Categorical hinge
- C. Binary cross-entropy
- D. Sparse categorical cross-entropy
Answer: C
NEW QUESTION 29
You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine. You use the following parameters:
* Optimizer: SGD
* Image shape = 224x224
* Batch size = 64
* Epochs = 10
* Verbose = 2
During training you encounter the following error: ResourceExhaustedError: out of Memory (oom) when allocating tensor. What should you do?
- A. Reduce the image shape
- B. Change the learning rate
- C. Change the optimizer
- D. Reduce the batch size
Answer: D
NEW QUESTION 30
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.
What does the Specialist need to do?
- A. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body.
- B. Build the Docker container to be NVIDIA-Docker compatible.
- C. Bundle the NVIDIA drivers with the Docker image.
- D. Organize the Docker container's file structure to execute on GPU instances.
Answer: C
NEW QUESTION 31
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. Decrease the batch size argument in your transformation
- C. Increase the buffer size for the shuffle option.
- D. Reduce the value of the repeat parameter
- E. Set the prefetch option equal to the training batch size
Answer: A,E
NEW QUESTION 32
You built and manage a production system that is responsible for predicting sales numbers. Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn't changed; however the accuracy of the model has steadily deteriorated. What issue is most likely causing the steady decline in model accuracy?
- A. Incorrect data split ratio during model training, evaluation, validation, and test
- B. Lack of model retraining
- C. Too few layers in the model for capturing information
- D. Poor data quality
Answer: A
NEW QUESTION 33
A city wants to monitor its air quality to address the consequences of air pollution. A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city. As this is a prototype, only daily data from the last year is available.
Which model is MOST likely to provide the best results in Amazon SageMaker?
- A. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_typeof classifier.
- B. Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.
- C. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_typeof regressor.
- D. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_typeof regressor.
Answer: D
Explanation:
Explanation/Reference: https://aws.amazon.com/blogs/machine-learning/build-a-model-to-predict-the-impact-of-weather- on-urban-air-quality-using-amazon-sagemaker/?ref=Welcome.AI
NEW QUESTION 34
A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.
How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?
- A. Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.
- B. Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.
- C. Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.
- D. Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.
Answer: C
Explanation:
Explanation
Explanation/Reference: https://towardsdatascience.com/automating-aws-sagemaker-notebooks-2dec62bc2c84
NEW QUESTION 35
You work for an advertising company and want to understand the effectiveness of your company's latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an Al Platform notebook. What should you do?
- A. Export your table as a CSV file from BigQuery to Google Drive, and use the Google Drive API to ingest the file into your notebook instance
- B. Use Al Platform Notebooks' BigQuery cell magic to query the data, and ingest the results as a pandas dataframe
- C. From a bash cell in your Al Platform notebook, use the bq extract command to export the table as a CSV file to Cloud Storage, and then use gsutii cp to copy the data into the notebook Use pandas. read_csv to ingest the file as a pandas dataframe
- D. Download your table from BigQuery as a local CSV file, and upload it to your Al Platform notebook instance Use pandas. read_csv to ingest the file as a pandas dataframe
Answer: D
NEW QUESTION 36
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. Configure AutoML Tables to perform the classification task
- B. Use Al Platform to run the classification model job configured for hyperparameter tuning
- C. Run a BigQuery ML task to perform logistic regression for the classification
- D. Use Al Platform Notebooks to run the classification model with pandas library
Answer: D
NEW QUESTION 37
A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers. Currently, the company has the following data in Amazon Aurora:
* Profiles for all past and existing customers
* Profiles for all past and existing insured pets
* Policy-level information
* Premiums received
* Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?
- A. Use regression on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
- B. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.
- C. Use clustering on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
- D. Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.
Answer: D
NEW QUESTION 38
A Mobile Network Operator is building an analytics platform to analyze and optimize a company's operations using Amazon Athena and Amazon S3.
The source systems send data in .CSV format in real time. The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3.
Which solution takes the LEAST effort to implement?
- A. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.
- B. Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet
- C. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.
- D. Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.
Answer: A
Explanation:
Explanation/Reference:
NEW QUESTION 39
You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible. What should you do?
- A. Use the BigQuery console to execute your query and then save the query results Into a new BigQuery table.
- B. Use the Kubeflow Pipelines domain-specific language to create a custom component that uses the Python BigQuery client library to execute queries
- C. Locate the Kubeflow Pipelines repository on GitHub Find the BigQuery Query Component, copy that component's URL, and use it to load the component into your pipeline. Use the component to execute queries against BigQuery
- D. Write a Python script that uses the BigQuery API to execute queries against BigQuery Execute this script as the first step in your Kubeflow pipeline
Answer: A
NEW QUESTION 40
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 41
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