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NEW QUESTION # 37
You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?
- A. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
- B. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model
- C. Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery
- D. Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler
Answer: A
NEW QUESTION # 38
A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among
200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.
What type of machine learning model should be used?
- A. Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.
- B. Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.
- C. Classification month-to-month using supervised learning of the 200 categories based on claim contents.
- D. Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.
Answer: B
Explanation:
Explanation
NEW QUESTION # 39
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 Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
- B. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
- C. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.
- D. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
Answer: D
NEW QUESTION # 40
You have built a model that is trained on data stored in Parquet files. You access the data through a Hive table hosted on Google Cloud. You preprocessed these data with PySpark and exported it as a CSV file into Cloud Storage. After preprocessing, you execute additional steps to train and evaluate your model. You want to parametrize this model training in Kubeflow Pipelines. What should you do?
- A. Add a ContainerOp to your pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed data in Cloud Storage.
- B. Remove the data transformation step from your pipeline.
- C. Containerize the PySpark transformation step, and add it to your pipeline.
- D. Deploy Apache Spark at a separate node pool in a Google Kubernetes Engine cluster. Add a ContainerOp to your pipeline that invokes a corresponding transformation job for this Spark instance.
Answer: D
NEW QUESTION # 41
You work on a data science team at a bank and are creating an ML model to predict loan default risk. You have collected and cleaned hundreds of millions of records worth of training data in a BigQuery table, and you now want to develop and compare multiple models on this data using TensorFlow and Vertex AI. You want to minimize any bottlenecks during the data ingestion state while considering scalability. What should you do?
- A. Use TensorFlow I/O's BigQuery Reader to directly read the data.
- B. Convert the data into TFRecords, and use tf.data.TFRecordDataset() to read them.
- C. Use the BigQuery client library to load data into a dataframe, and use tf.data.Dataset.from_tensor_slices() to read it.
- D. Export data to CSV files in Cloud Storage, and use tf.data.TextLineDataset() to read them.
Answer: D
NEW QUESTION # 42
A gaming company has launched an online game where people can start playing for free, but they need to pay if they choose to use certain features. The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year. The company has gathered a labeled dataset from 1 million users.
The training dataset consists of 1,000 positive samples (from users who ended up paying within 1 year) and
999,000 negative samples (from users who did not use any paid features). Each data sample consists of 200 features including user age, device, location, and play patterns.
Using this dataset for training, the Data Science team trained a random forest model that converged with over
99% accuracy on the training set. However, the prediction results on a test dataset were not satisfactory Which of the following approaches should the Data Science team take to mitigate this issue? (Choose two.)
- A. Change the cost function so that false positives have a higher impact on the cost value than false negatives.
- B. Include a copy of the samples in the test dataset in the training dataset.
- C. Change the cost function so that false negatives have a higher impact on the cost value than false positives.
- D. Add more deep trees to the random forest to enable the model to learn more features.
- E. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
Answer: C,E
NEW QUESTION # 43
A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day, the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL.
Which storage scheme is MOST adapted to this scenario?
- A. Store datasets as global tables in Amazon DynamoDB.
- B. Store datasets as files in Amazon S3.
- C. Store datasets as tables in a multi-node Amazon Redshift cluster.
- D. Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.
Answer: B
NEW QUESTION # 44
You are responsible for building a unified analytics environment across a variety of on-premises data marts. Your company is experiencing data quality and security challenges when integrating data across the servers, caused by the use of a wide range of disconnected tools and temporary solutions. You need a fully managed, cloud-native data integration service that will lower the total cost of work and reduce repetitive work. Some members on your team prefer a codeless interface for building Extract, Transform, Load (ETL) process. Which service should you use?
- A. Dataflow
- B. Cloud Data Fusion
- C. Apache Flink
- D. Dataprep
Answer: B
Explanation:
https://cloud.google.com/data-fusion/docs/concepts/overview#using_the_code-free_web_ui
NEW QUESTION # 45
You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?
- A. Create a cluster on Dataproc for training
- B. Use Al Platform for distributed training
- C. Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.
- D. Create a Managed Instance Group with autoscaling
Answer: B
Explanation:
AI platform also contains kubeflow pipelines. you don't need to set up infrastructure to use it. For D you need to set up a kubernetes cluster engine. The question asks us to minimize infrastructure overheard.
NEW QUESTION # 46
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 batch size
- B. Reduce the image shape
- C. Change the learning rate
- D. Change the optimizer
Answer: D
NEW QUESTION # 47
You work for an online publisher that delivers news articles to over 50 million readers. You have built an AI model that recommends content for the company's weekly newsletter. A recommendation is considered successful if the article is opened within two days of the newsletter's published date and the user remains on the page for at least one minute.
All the information needed to compute the success metric is available in BigQuery and is updated hourly. The model is trained on eight weeks of data, on average its performance degrades below the acceptable baseline after five weeks, and training time is 12 hours. You want to ensure that the model's performance is above the acceptable baseline while minimizing cost. How should you monitor the model to determine when retraining is necessary?
- A. Use Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100% and a monitoring frequency of two days.
- B. Schedule a daily Dataflow job in Cloud Composer to compute the success metric.
- C. Schedule a weekly query in BigQuery to compute the success metric.
- D. Schedule a cron job in Cloud Tasks to retrain the model every week before the newsletter is created.
Answer: B
NEW QUESTION # 48
You are building an ML model to predict trends in the stock market based on a wide range of factors. While exploring the data, you notice that some features have a large range. You want to ensure that the features with the largest magnitude don't overfit the model. What should you do?
- A. Apply a principal component analysis (PCA) to minimize the effect of any particular feature.
- B. Use a binning strategy to replace the magnitude of each feature with the appropriate bin number.
- C. Normalize the data by scaling it to have values between 0 and 1.
- D. Standardize the data by transforming it with a logarithmic function.
Answer: C
NEW QUESTION # 49
You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company's manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?
- A. Develop a custom PyTorch regression model, and optimize it using Vertex Al Training
- B. Develop a custom TensorFlow regression model, and optimize it using Vertex Al Training.
- C. Develop a custom scikit-learn regression model, and optimize it using Vertex Al Training
- D. Develop a regression model using BigQuery ML.
Answer: D
Explanation:
BigQuery ML is a powerful tool that allows you to build and deploy machine learning models directly within BigQuery, Google's fully-managed, serverless data warehouse. It allows you to create regression models using SQL, which is a familiar and easy-to-use language for many data scientists. It also allows you to scale smoothly and require minimal development work since you don't have to worry about cluster management and it's fully-managed by Google.
BigQuery ML also allows you to run your training on the same data where it's stored, this will minimize data movement, and thus minimize cost and time.
Reference:
BigQuery ML
BigQuery ML for regression
BigQuery ML for scalability
NEW QUESTION # 50
A retail company intends to use machine learning to categorize new products. A labeled dataset of current products was provided to the Data Science team. The dataset includes 1,200 products. The labeled dataset has 15 features for each product such as title dimensions, weight, and price. Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.
Which model should be used for categorizing new products using the provided dataset for training?
- A. A DeepAR forecasting model based on a recurrent neural network (RNN)
- B. A deep convolutional neural network (CNN) with a softmax activation function for the last layer
- C. AnXGBoost model where the objective parameter is set to multi:softmax
- D. A regression forest where the number of trees is set equal to the number of product categories
Answer: B
NEW QUESTION # 51
Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?
- A. Use Al Platform Notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API
- B. Use Al Platform Training to execute the experiments Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
- C. Use Al Platform Training to execute the experiments Write the accuracy metrics to BigQuery, and query the results using the BigQueryAPI.
- D. Use Kubeflow Pipelines to execute the experiments Export the metrics file, and query the results using the Kubeflow Pipelines API.
Answer: D
Explanation:
https://codelabs.developers.google.com/codelabs/cloud-kubeflow-pipelines-gis Kubeflow Pipelines (KFP) helps solve these issues by providing a way to deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility. Cloud AI Pipelines makes it easy to set up a KFP installation.
https://www.kubeflow.org/docs/components/pipelines/introduction/#what-is-kubeflow-pipelines
"Kubeflow Pipelines supports the export of scalar metrics. You can write a list of metrics to a local file to describe the performance of the model. The pipeline agent uploads the local file as your run-time metrics. You can view the uploaded metrics as a visualization in the Runs page for a particular experiment in the Kubeflow Pipelines UI." https://www.kubeflow.org/docs/components/pipelines/sdk/pipelines-metrics/
NEW QUESTION # 52
During batch training of a neural network, you notice that there is an oscillation in the loss. How should you adjust your model to ensure that it converges?
- A. Increase the learning rate hyperparameter
- B. Increase the size of the training batch
- C. Decrease the size of the training batch
- D. Decrease the learning rate hyperparameter
Answer: A
NEW QUESTION # 53
You need to analyze user activity data from your company's mobile applications. Your team will use BigQuery for data analysis, transformation, and experimentation with ML algorithms. You need to ensure real-time ingestion of the user activity data into BigQuery. What should you do?
- A. Configure Pub/Sub and a Dataflow streaming job to ingest the data into BigQuery,
- B. Run a Dataflow streaming job to ingest the data into BigQuery.
- C. Configure Pub/Sub to stream the data into BigQuery.
- D. Run an Apache Spark streaming job on Dataproc to ingest the data into BigQuery.
Answer: C
Explanation:
Pub/Sub is a messaging service that can be used to stream data into BigQuery in real-time. Configuring Pub/Sub to stream the user activity data into BigQuery would ensure real-time ingestion of the data. Source: Google Cloud
NEW QUESTION # 54
A Machine Learning Specialist is developing a custom video recommendation model for an application. The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket.
The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.
Which approach allows the Specialist to use all the data to train the model?
- A. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.
- B. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
- C. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and train using the full dataset
- D. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
Answer: D
NEW QUESTION # 55
You work on the data science team at a manufacturing company. You are reviewing the company's historical sales data, which has hundreds of millions of records. For your exploratory data analysis, you need to calculate descriptive statistics such as mean, median, and mode; conduct complex statistical tests for hypothesis testing; and plot variations of the features over time You want to use as much of the sales data as possible in your analyses while minimizing computational resources. What should you do?
- A. Visualize the time plots in Google Data Studio. Import the dataset into Vertex Al Workbench user-managed notebooks Use this data to calculate the descriptive statistics and run the statistical analyses
- B. Spin up a Vertex Al Workbench user-managed notebooks instance and import the dataset Use this data to create statistical and visual analyses
- C. Use BigQuery to calculate the descriptive statistics. Use Vertex Al Workbench user-managed notebooks to visualize the time plots and run the statistical analyses.
Answer: C
Explanation:
D Use BigQuery to calculate the descriptive statistics, and use Google Data Studio to visualize the time plots. Use Vertex Al Workbench user-managed notebooks to run the statistical analyses.
Explanation:
BigQuery is a powerful tool for analyzing large datasets and can be used to quickly calculate descriptive statistics, such as mean, median, and mode, on large amounts of data. By using BigQuery, you can analyze the entire dataset and minimize the computational resources required for your analyses.
Once you have calculated the descriptive statistics, you can use Vertex Al Workbench user-managed notebooks to visualize the time plots and run the statistical analyses. Vertex Al Workbench allows you to interactively explore the data, create visualizations, and perform advanced statistical analysis. It's also possible to run these notebooks on a powerful GPU which will help to increase the speed of the analysis.
NEW QUESTION # 56
You are building an ML model to detect anomalies in real-time sensor dat a. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization. How should you configure the pipeline?
- A. 1 = BigQuery, 2 = AutoML, 3 = Cloud Functions
- B. 1 = Dataflow, 2 - Al Platform, 3 = BigQuery
- C. 1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable
- D. 1 = BigQuery, 2 = Al Platform, 3 = Cloud Storage
Answer: A
NEW QUESTION # 57
You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?
- A. Use Data Catalog to search the BigQuery datasets by using keywords in the table description.
- B. Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.
- C. Execute a query in BigQuery to retrieve all the existing table names in your project using the INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result o find the table that you need.
- D. Maintain a lookup table in BigQuery that maps the table descriptions to the table ID. Query the lookup table to find the correct table ID for the data that you need.
Answer: B
NEW QUESTION # 58
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Professional-Machine-Learning-Engineer Exam Preparation Material with New Professional-Machine-Learning-Engineer Dumps Questions.: https://drive.google.com/open?id=17Jw5OJj0X5EwY7gmNVrElUXAml3R8jJ4
