All About Highest Quality Professional-Data-Engineer Preparation Labs

NEW QUESTION 1

You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

• A. Cloud Scheduler
• B. Cloud Dataflow
• C. Cloud Functions
• D. Cloud Composer

Answer: A

NEW QUESTION 2

You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old. What should you do?

• A. Disable caching by editing the report settings.
• B. Disable caching in BigQuery by editing table details.
• C. Refresh your browser tab showing the visualizations.
• D. Clear your browser history for the past hour then reload the tab showing the virtualizations.

Answer: A

Explanation:
Reference https://support.google.com/datastudio/answer/7020039?hl=en

NEW QUESTION 3

Your company handles data processing for a number of different clients. Each client prefers to use their own suite of analytics tools, with some allowing direct query access via Google BigQuery. You need to secure the data so that clients cannot see each other’s data. You want to ensure appropriate access to the data. Which three steps should you take? (Choose three.)

• A. Load data into different partitions.
• B. Load data into a different dataset for each client.
• C. Put each client’s BigQuery dataset into a different table.
• D. Restrict a client’s dataset to approved users.
• E. Only allow a service account to access the datasets.
• F. Use the appropriate identity and access management (IAM) roles for each client’s users.

Answer: BDF

NEW QUESTION 4

You currently have a single on-premises Kafka cluster in a data center in the us-east region that is responsible for ingesting messages from IoT devices globally. Because large parts of globe have poor internet connectivity, messages sometimes batch at the edge, come in all at once, and cause a spike in load on your Kafka cluster. This is becoming difficult to manage and prohibitively expensive. What is the
Google-recommended cloud native architecture for this scenario?

• A. Edge TPUs as sensor devices for storing and transmitting the messages.
• B. Cloud Dataflow connected to the Kafka cluster to scale the processing of incoming messages.
• C. An IoT gateway connected to Cloud Pub/Sub, with Cloud Dataflow to read and process the messages from Cloud Pub/Sub.
• D. A Kafka cluster virtualized on Compute Engine in us-east with Cloud Load Balancing to connect to the devices around the world.

Answer: C

NEW QUESTION 5

You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

• A. Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP typ
• B. Reload the data.
• C. Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numericvalues from the column TS for each ro
• D. Reference the column TS instead of the column DT from now on.
• E. Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP value
• F. Reference the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.
• G. Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN typ
• H. Reload all data in append mod
• I. For each appended row, set the value of IS_NEW to tru
• J. For future queries, reference the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.
• K. Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP value
• L. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP typ
• M. Reference the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now o
• N. In the future, new data is loaded into the table NEW_CLICK_STREAM.

Answer: D

NEW QUESTION 6

For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

• A. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
• B. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
• C. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
• D. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.

Answer: C

Explanation:
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance,
If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from
your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance

NEW QUESTION 7

You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

• A. Create an API using App Engine to receive and send messages to the applications
• B. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
• C. Create a table on Cloud SQL, and insert and delete rows with the job information
• D. Create a table on Cloud Spanner, and insert and delete rows with the job information

Answer: A

NEW QUESTION 8

Your company is streaming real-time sensor data from their factory floor into Bigtable and they have noticed extremely poor performance. How should the row key be redesigned to improve Bigtable performance on queries that populate real-time dashboards?

• A. Use a row key of the form <timestamp>.
• B. Use a row key of the form <sensorid>.
• C. Use a row key of the form <timestamp>#<sensorid>.
• D. Use a row key of the form >#<sensorid>#<timestamp>.

Answer: A

NEW QUESTION 9

You want to build a managed Hadoop system as your data lake. The data transformation process is composed of a series of Hadoop jobs executed in sequence. To accomplish the design of separating storage from compute, you decided to use the Cloud Storage connector to store all input data, output data, and intermediary data. However, you noticed that one Hadoop job runs very slowly with Cloud Dataproc, when compared with the on-premises bare-metal Hadoop environment (8-core nodes with 100-GB RAM). Analysis shows that this particular Hadoop job is disk I/O intensive. You want to resolve the issue. What should you do?

• A. Allocate sufficient memory to the Hadoop cluster, so that the intermediary data of that particular Hadoop job can be held in memory
• B. Allocate sufficient persistent disk space to the Hadoop cluster, and store the intermediate data of that particular Hadoop job on native HDFS
• C. Allocate more CPU cores of the virtual machine instances of the Hadoop cluster so that the networking bandwidth for each instance can scale up
• D. Allocate additional network interface card (NIC), and configure link aggregation in the operating system to use the combined throughput when working with Cloud Storage

Answer: A

NEW QUESTION 10

You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)

• A. There are very few occurrences of mutations relative to normal samples.
• B. There are roughly equal occurrences of both normal and mutated samples in the database.
• C. You expect future mutations to have different features from the mutated samples in the database.
• D. You expect future mutations to have similar features to the mutated samples in the database.
• E. You already have labels for which samples are mutated and which are normal in the database.

Answer: BC

NEW QUESTION 11

You work on a regression problem in a natural language processing domain, and you have 100M labeled exmaples in your dataset. You have randomly shuffled your data and split your dataset into train and test samples (in a 90/10 ratio). After you trained the neural network and evaluated your model on a test set, you discover that the root-mean-squared error (RMSE) of your model is twice as high on the train set as on the test set. How should you improve the performance of your model?

• A. Increase the share of the test sample in the train-test split.
• B. Try to collect more data and increase the size of your dataset.
• C. Try out regularization techniques (e.g., dropout of batch normalization) to avoid overfitting.
• D. Increase the complexity of your model by, e.g., introducing an additional layer or increase sizing the size of vocabularies or n-grams used.

Answer: D

NEW QUESTION 12

What are all of the BigQuery operations that Google charges for?

• A. Storage, queries, and streaming inserts
• B. Storage, queries, and loading data from a file
• C. Storage, queries, and exporting data
• D. Queries and streaming inserts

Answer: A

Explanation:
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference: https://cloud.google.com/bigquery/pricing

NEW QUESTION 13

You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing data. Which Stackdriver alerts should you create?

• A. An alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination
• B. An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination
• C. An alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination
• D. An alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination

Answer: B

NEW QUESTION 14

Your company’s customer and order databases are often under heavy load. This makes performing analytics against them difficult without harming operations. The databases are in a MySQL cluster, with nightly backups taken using mysqldump. You want to perform analytics with minimal impact on operations. What should you do?

• A. Add a node to the MySQL cluster and build an OLAP cube there.
• B. Use an ETL tool to load the data from MySQL into Google BigQuery.
• C. Connect an on-premises Apache Hadoop cluster to MySQL and perform ETL.
• D. Mount the backups to Google Cloud SQL, and then process the data using Google Cloud Dataproc.

Answer: C

NEW QUESTION 15

Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the data. Which three machine learning applications can you use? (Choose three.)

• A. Supervised learning to determine which transactions are most likely to be fraudulent.
• B. Unsupervised learning to determine which transactions are most likely to be fraudulent.
• C. Clustering to divide the transactions into N categories based on feature similarity.
• D. Supervised learning to predict the location of a transaction.
• E. Reinforcement learning to predict the location of a transaction.
• F. Unsupervised learning to predict the location of a transaction.

Answer: BCE

NEW QUESTION 16

Your company has a hybrid cloud initiative. You have a complex data pipeline that moves data between cloud provider services and leverages services from each of the cloud providers. Which cloud-native service should you use to orchestrate the entire pipeline?

• A. Cloud Dataflow
• B. Cloud Composer
• C. Cloud Dataprep
• D. Cloud Dataproc

Answer: D

NEW QUESTION 17

You are training a spam classifier. You notice that you are overfitting the training data. Which three actions can you take to resolve this problem? (Choose three.)

• A. Get more training examples
• B. Reduce the number of training examples
• C. Use a smaller set of features
• D. Use a larger set of features
• E. Increase the regularization parameters
• F. Decrease the regularization parameters

Answer: ADF

NEW QUESTION 18

Which role must be assigned to a service account used by the virtual machines in a Dataproc cluster so they can execute jobs?

• A. Dataproc Worker
• B. Dataproc Viewer
• C. Dataproc Runner
• D. Dataproc Editor

Answer: A

Explanation:
Service accounts used with Cloud Dataproc must have Dataproc/Dataproc Worker role (or have all the permissions granted by Dataproc Worker role).
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes

NEW QUESTION 19

You need to choose a database to store time series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?

• A. Create a table in BigQuery, and append the new samples for CPU and memory to the table
• B. Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second
• C. Create a narrow table in Cloud Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second
• D. Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.

Answer: D

NEW QUESTION 20

Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

• A. The CSV data loaded in BigQuery is not flagged as CSV.
• B. The CSV data has invalid rows that were skipped on import.
• C. The CSV data loaded in BigQuery is not using BigQuery’s default encoding.
• D. The CSV data has not gone through an ETL phase before loading into BigQuery.

Answer: B

NEW QUESTION 21

What are two of the characteristics of using online prediction rather than batch prediction?

• A. It is optimized to handle a high volume of data instances in a job and to run more complex models.
• B. Predictions are returned in the response message.
• C. Predictions are written to output files in a Cloud Storage location that you specify.
• D. It is optimized to minimize the latency of serving predictions.

Answer: BD

Explanation:
Online prediction
Optimized to minimize the latency of serving predictions. Predictions returned in the response message.
Batch prediction
Optimized to handle a high volume of instances in a job and to run more complex models. Predictions written to output files in a Cloud Storage location that you specify.
Reference:
https://cloud.google.com/ml-engine/docs/prediction-overview#online_prediction_versus_batch_prediction

NEW QUESTION 22

You’re training a model to predict housing prices based on an available dataset with real estate properties. Your plan is to train a fully connected neural net, and you’ve discovered that the dataset contains latitude and longtitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you’d like to engineer a feature that incorporates this physical dependency.
What should you do?

• A. Provide latitude and longtitude as input vectors to your neural net.
• B. Create a numeric column from a feature cross of latitude and longtitude.
• C. Create a feature cross of latitude and longtitude, bucketize at the minute level and use L1 regularization during optimization.
• D. Create a feature cross of latitude and longtitude, bucketize it at the minute level and use L2 regularization during optimization.

Answer: B

Explanation:
Reference https://cloud.google.com/bigquery/docs/gis-data

NEW QUESTION 23

You work for a manufacturing plant that batches application log files together into a single log file once a day at 2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make sure the log file in processed once per day as inexpensively as possible. What should you do?

• A. Change the processing job to use Google Cloud Dataproc instead.
• B. Manually start the Cloud Dataflow job each morning when you get into the office.
• C. Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.
• D. Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.

Answer: C

NEW QUESTION 24
......