2024年最新のに更新されたのはProfessional-Machine-Learning-EngineerテストエンジンとPDFで完全版無料問題集保証！ [Q19-Q36]

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2024年最新のに更新されたのはProfessional-Machine-Learning-EngineerテストエンジンとPDFで完全版無料問題集保証！

最新のGoogle Cloud Certified Professional-Machine-Learning-Engineer実際の無料試験問題

この試験では、Google Cloudの機械学習ツールとサービスを使用して機械学習ソリューションを設計および実装する候補者の能力を評価する、複数選択の質問、パフォーマンスベースのタスク、およびケーススタディで構成されています。この試験は、監督や監督なしの学習、深い学習、自然言語処理、コンピュータービジョンなど、主要な機械学習の概念に関する候補者の知識をテストするように設計されています。この試験では、大規模なデータセットを処理できるスケーラブルで信頼性の高い機械学習モデルを構築する方法についての候補者の理解も評価します。

質問 19
You have deployed a scikit-learn model to a Vertex Al endpoint using a custom model server. You enabled auto scaling; however, the deployed model fails to scale beyond one replica, which led to dropped requests.
You notice that CPU utilization remains low even during periods of high load. What should you do?

Attach a GPU to the prediction nodes.

Increase the number of workers in your model server.

Schedule scaling of the nodes to match expected demand.

Increase the minReplicaCount in your DeployedModel configuration.

Auto scaling is a feature that allows you to automatically adjust the number of prediction nodes based on the traffic and load of your deployed model1. However, auto scaling depends on the CPU utilization of your prediction nodes, which is the percentage of CPU resources used by your model server1. If your CPU utilization is low, even during periods of high load, it means that your model server is not fully utilizing the available CPU resources, and thus auto scaling will not trigger more replicas2.
One possible reason for low CPU utilization is that your model server is using a single worker process to handle prediction requests3. A worker process is a subprocess that runs your model code and handles prediction requests3. If you have only one worker process, it can only handle one request at a time, which can lead to dropped requests when the traffic is high3. To increase the CPU utilization and the throughput of your model server, you can increase the number of worker processes, which will allow your model server to handle multiple requests in parallel3.
To increase the number of workers in your model server, you need to modify your custom model server code and use the –workers flag to specify the number of worker processes you want to use3. For example, if you are using a Gunicorn server, you can use the following command to start your model server with four worker processes:
gunicorn –bind :$PORT –workers 4 –threads 1 –timeout 60 main:app
By increasing the number of workers in your model server, you can increase the CPU utilization of your prediction nodes, and thus enable auto scaling to scale beyond one replica.
The other options are not suitable for your scenario, because they either do not address the root cause of low CPU utilization, such as attaching a GPU or scheduling scaling, or they do not enableauto scaling, such as increasing the minReplicaCount, which is a fixed number of nodes that will always run regardless of the traffic1.
References:
* Scaling prediction nodes | Vertex AI | Google Cloud
* Troubleshooting | Vertex AI | Google Cloud
* Using a custom prediction routine with online prediction | Vertex AI | Google Cloud

質問 20
You are training a deep learning model for semantic image segmentation with reduced training time. While using a Deep Learning VM Image, you receive the following error: The resource
‘projects/deeplearning-platforn/zones/europe-west4-c/acceleratorTypes/nvidia-tesla-k80’ was not found. What should you do?

Ensure that you have GPU quota in the selected region.

Ensure that the required GPU is available in the selected region.

Ensure that you have preemptible GPU quota in the selected region.

Ensure that the selected GPU has enough GPU memory for the workload.

質問 21
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?

Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet

Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.

Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.

Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.

質問 22
You are developing an image recognition model using PyTorch based on ResNet50 architecture Your code is working fine on your local laptop on a small subsample. Your full dataset has 200k labeled images You want to quickly scale your training workload while minimizing cost. You plan to use 4 V100 GPUs What should you do?

Create a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs Prepare and submit a TFJob operator to this node pool.

Configure a Compute Engine VM with all the dependencies that launches the training Tram your model with Vertex Al using a custom tier that contains the required GPUs.

Create a Vertex Al Workbench user-managed notebooks instance with 4 V100 GPUs, and use it to tram your model.

Package your code with Setuptools and use a pre-built container. Train your model with Vertex Al using a custom tier that contains the required GPUs.

質問 23
A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.
Which approach will provide the MAXIMUM performance boost?

Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.

Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.

Reduce the learning rate and run the training process until the training loss stops decreasing.

Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.

質問 24
Your organization’s call center has asked you to develop a model that analyzes customer sentiments in each call. The call center receives over one million calls daily, and data is stored in Cloud Storage. The data collected must not leave the region in which the call originated, and no Personally Identifiable Information (Pll) can be stored or analyzed. The data science team has a third-party tool for visualization and access which requires a SQL ANSI-2011 compliant interface. You need to select components for data processing and for analytics. How should the data pipeline be designed?

1 = Dataflow, 2 = BigQuery

1 = Pub/Sub, 2 = Datastore

1 = Dataflow, 2 = Cloud SQL

1 = Cloud Function, 2 = Cloud SQL

質問 25
While running a model training pipeline on Vertex Al, you discover that the evaluation step is failing because of an out-of-memory error. You are currently using TensorFlow Model Analysis (TFMA) with a standard Evaluator TensorFlow Extended (TFX) pipeline component for the evaluation step. You want to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead. What should you do?

Add tfma.MetricsSpec () to limit the number of metrics in the evaluation step.

Migrate your pipeline to Kubeflow hosted on Google Kubernetes Engine, and specify the appropriate node parameters for the evaluation step.

Include the flag -runner=DataflowRunner in beam_pipeline_args to run the evaluation step on Dataflow.

Move the evaluation step out of your pipeline and run it on custom Compute Engine VMs with sufficient memory.

The best option to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead is to use Dataflow as the runner for the evaluation step. Dataflow is a fully managed service for executing Apache Beam pipelines that can scale up and down according to the workload. Dataflow can handle large-scale, distributed data processing tasks such as model evaluation, and it can also integrate with Vertex AI Pipelines and TensorFlow Extended (TFX). By using the flag -runner=DataflowRunner in beam_pipeline_args, you can instruct the Evaluator component to run the evaluation step on Dataflow, instead of using the default DirectRunner, which runs locally and may cause out-of-memory errors. Option A is incorrect because adding tfma.MetricsSpec() to limit the number of metrics in the evaluation step may downgrade the evaluation quality, as some important metrics may be omitted.
Moreover, reducing the number of metrics may not solve the out-of-memory error, as the evaluation step may still consume a lot of memory depending on the size and complexity of the data and the model. Option B is incorrect because migrating the pipeline to Kubeflow hosted on Google Kubernetes Engine (GKE) may increase the infrastructure overhead, as you need to provision, manage, and monitor the GKE cluster yourself.
Moreover, you need to specify the appropriate node parameters for the evaluation step, which may require trial and error to find the optimal configuration. Option D is incorrect because moving the evaluation step out of the pipeline and running it on custom Compute Engine VMs may also increase the infrastructure overhead, as you need to create, configure, and delete the VMs yourself. Moreover, you need to ensure that the VMs have sufficientmemory for the evaluation step, which may require trial and error to find the optimal machine type. References:
* Dataflow documentation
* Using DataflowRunner
* Evaluator component documentation
* Configuring the Evaluator component

質問 26
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?

Increase the size of the training batch

Decrease the size of the training batch

Increase the learning rate hyperparameter

Decrease the learning rate hyperparameter

質問 27
You are building an ML model to detect anomalies in real-time sensor data. 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?

1 = Dataflow, 2 – Al Platform, 3 = BigQuery

1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable

1 = BigQuery, 2 = AutoML, 3 = Cloud Functions

1 = BigQuery, 2 = Al Platform, 3 = Cloud Storage

質問 28
You are building a TensorFlow text-to-image generative model by using a dataset that contains billions of images with their respective captions. You want to create a low maintenance, automated workflow that reads the data from a Cloud Storage bucket collects statistics, splits the dataset into training/validation/test datasets performs data transformations, trains the model using the training/validation datasets. and validates the model by using the test dataset. What should you do?

Use the Apache Airflow SDK to create multiple operators that use Dataflow and Vertex Al services Deploy the workflow on Cloud Composer.

Use the MLFlow SDK and deploy it on a Google Kubernetes Engine Cluster Create multiple components that use Dataflow and Vertex Al services.

Use the Kubeflow Pipelines (KFP) SDK to create multiple components that use Dataflow and Vertex Al services Deploy the workflow on Vertex Al Pipelines.

Use the TensorFlow Extended (TFX) SDK to create multiple components that use Dataflow and Vertex Al services Deploy the workflow on Vertex Al Pipelines.

質問 29
You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

Three individual features binned latitude, binned longitude, and one-hot encoded car type

One feature obtained as an element-wise product between latitude, longitude, and car type

One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type

Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type

質問 30
You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?

* Validate the accuracy of the model that you trained on preprocessed data
* Create a new model that uses the raw data and is available in real time
* Deploy the new model onto Al Platform for online prediction

* Send incoming prediction requests to a Pub/Sub topic
* Transform the incoming data using a Dataflow job
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue

* Stream incoming prediction request data into Cloud Spanner
* Create a view to abstract your preprocessing logic.
* Query the view every second for new records
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue.

* Send incoming prediction requests to a Pub/Sub topic
* Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
* Implement your preprocessing logic in the Cloud Function
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue

質問 31
A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

Create a NAT gateway within the corporate VPC.

Route Amazon SageMaker traffic through an on-premises network.

Create Amazon SageMaker VPC interface endpoints within the corporate VPC.

Create VPC peering with Amazon VPC hosting Amazon SageMaker.

質問 32
While monitoring your model training’s GPU utilization, you discover that you have a native synchronous implementation. The training data is split into multiple files. You want to reduce the execution time of your input pipeline. What should you do?

Increase the CPU load

Add caching to the pipeline

Increase the network bandwidth

Add parallel interleave to the pipeline

質問 33
You are training and deploying updated versions of a regression model with tabular data by using Vertex Al Pipelines. Vertex Al Training Vertex Al Experiments and Vertex Al Endpoints. The model is deployed in a Vertex Al endpoint and your users call the model by using the Vertex Al endpoint. You want to receive an email when the feature data distribution changes significantly, so you can retrigger the training pipeline and deploy an updated version of your model What should you do?

Use Vertex Al Model Monitoring Enable prediction drift monitoring on the endpoint. and specify a notification email.

In Cloud Logging, create a logs-based alert using the logs in the Vertex Al endpoint. Configure Cloud Logging to send an email when the alert is triggered.

In Cloud Monitoring create a logs-based metric and a threshold alert for the metric. Configure Cloud Monitoring to send an email when the alert is triggered.

Export the container logs of the endpoint to BigQuery Create a Cloud Function to run a SQL query over the exported logs and send an email. Use Cloud Scheduler to trigger the Cloud Function.

質問 34
While performing exploratory data analysis on a dataset, you find that an important categorical feature has 5% null values. You want to minimize the bias that could result from the missing values. How should you handle the missing values?

Remove the rows with missing values, and upsample your dataset by 5%.

Replace the missing values with the feature’s mean.

Replace the missing values with a placeholder category indicating a missing value.

Move the rows with missing values to your validation dataset.

The best option for handling missing values in a categorical feature is to replace them with a placeholder category indicating a missing value. This is a type of imputation, which is a method of estimating the missing values based on the observed data. Imputing the missing values with a placeholder category preserves the information that the data is missing, and avoids introducing bias or distortion in the feature distribution. It also allows the machine learning model to learn from the missingness pattern, and potentially use it as a predictor for the target variable. The other options are not suitable for handling missing values in a categorical feature, because:
* Removing the rows with missing values and upsampling the dataset by 5% would reduce the size of the dataset and potentially lose important information. It would also introduce sampling bias and overfitting, as the upsampling process would create duplicate or synthetic observations that do not reflect the true population.
* Replacing the missing values with the feature’s mean would not make sense for a categorical feature, as the mean is a numerical measure that does not capture the mode or frequency of the categories. It would
* also create a new category that does not exist in the original data, and might confuse the machine learning model.
* Moving the rows with missing values to the validation dataset would compromise the validity and reliability of the model evaluation, as the validation dataset would not be representative of the test or production data. It would also reduce the amount of data available for training the model, and might introduce leakage or inconsistency between the training and validation datasets. References:
* Imputation of missing values
* Effective Strategies to Handle Missing Values in Data Analysis
* How to Handle Missing Values of Categorical Variables?
* Google Cloud launches machine learning engineer certification
* Google Professional Machine Learning Engineer Certification
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

質問 35
You work for an international manufacturing organization that ships scientific products all over the world Instruction manuals for these products need to be translated to 15 different languages Your organization’s leadership team wants to start using machine learning to reduce the cost of manual human translations and increase translation speed. You need to implement a scalable solution that maximizes accuracy and minimizes operational overhead. You also want to include a process to evaluate and fix incorrect translations. What should you do?

Create a workflow using Cloud Function Triggers Configure a Cloud Function that is triggered when documents are uploaded to an input Cloud Storage bucket Configure another Cloud Function that translates the documents using the Cloud Translation API and saves the translations to an output Cloud Storage bucket Use human reviewers to evaluate the incorrect translations.

Create a Vertex Al pipeline that processes the documents1 launches an AutoML Translation training job evaluates the translations, and deploys the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between training and live data re-trigger the pipeline with the latest data.

Use AutoML Translation to tram a model Configure a Translation Hub project and use the trained model to translate the documents Use human reviewers to evaluate the incorrect translations

Use Vertex Al custom training jobs to fine-tune a state-of-the-art open source pretrained model with your data Deploy the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between the training and live data, configure a trigger to run another training job with the latest data.

質問 36
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?

Classification month-to-month using supervised learning of the 200 categories based on claim contents.

Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.

Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.

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.

試験はデータ前処理、モデル選択、特徴量エンジニアリング、ハイパーパラメーターチューニング、モデル展開など、幅広いトピックをカバーしています。試験では、線形回帰、ロジスティック回帰、決定木、ランダムフォレスト、ニューラルネットワーク、ディープラーニングなどの機械学習アルゴリズムに関する候補者の知識もテストされます。試験に合格するためには、これらのトピックについて深い理解を持ち、実世界のシナリオでの実践的な応用を示すことが必要です。

Professional-Machine-Learning-Engineer問題集には更新された練習テストと267独特な問題：https://www.passtest.jp/Google/Professional-Machine-Learning-Engineer-shiken.html