PassTest Professional-Data-Engineerリアル試験問題Professional-Data-Engineer練習問題集
厳密検証されたProfessional-Data-Engineer試験問題集と解答で無料提供のProfessional-Data-Engineer問題と正解付き
質問 # 157
Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?
- A. An hourly watermark
- B. The with Allowed Lateness method
- C. An event time trigger
- D. A processing time trigger
正解：D
解説：
When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.
Processing time triggers. These triggers operate on the processing time - the time when the data element is processed at any given stage in the pipeline.
Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based.
質問 # 158
You need to migrate a Redis database from an on-premises data center to a Memorystore for Redis instance.
You want to follow Google-recommended practices and perform the migration for minimal cost. time, and effort. What should you do?
- A. Write a shell script to migrate the Redis data, and create a new Memorystore for Redis instance.
- B. Make a secondary instance of the Redis database on a Compute Engine instance, and then perform a live cutover.
- C. Create a Dataflow job to road the Redis database from the on-premises data center. and write the data to a Memorystore for Redis instance
- D. Make an RDB backup of the Redis database, use the gsutil utility to copy the RDB file into a Cloud Storage bucket, and then import the RDB tile into the Memorystore for Redis instance.
正解：D
解説：
The import and export feature uses the native RDB snapshot feature of Redis to import data into or export data out of a Memorystore for Redis instance. The use of the native RDB format prevents lock-in and makes it very easy to move data within Google Cloud or outside of Google Cloud. Import and export uses Cloud Storage buckets to store RDB files. Reference:
https://cloud.google.com/memorystore/docs/redis/import-export-overview
質問 # 159
You need to migrate a 2TB relational database to Google Cloud Platform. You do not have the resources to significantly refactor the application that uses this database and cost to operate is of primary concern.
Which service do you select for storing and serving your data?
- A. Cloud Bigtable
- B. Cloud SQL
- C. Cloud Firestore
- D. Cloud Spanner
正解：B
質問 # 160
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. Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.
- B. 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 values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.
- C. Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.
- D. Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. the column TS instead of the column DT from now on.
- E. Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.
正解：B
解説：
It's better to create a new table and delete old one when we are changing the datatype is permanent. View is not suitable because every time the query will run and additional charges will be applied.
質問 # 161
You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?
- A. Consume the stream of data in Cloud Dataflow using Kafka IO. Set a sliding time window of 1 hour every 5 minutes. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
- B. Consume the stream of data in Cloud Dataflow using Kafka IO. Set a fixed time window of 1 hour. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
- C. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtable. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour. If that number falls below 4000, send an alert.
- D. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuery. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour. If that number falls below
4000, send an alert.
正解：C
質問 # 162
Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl. being able to reprocess all failing data).
What should you do?
- A. Add a filtering step to skip these types of errors in the future, extract erroneous rows from logs.
- B. Add a try... catch block to your DoFn that transforms the data, extract erroneous rows from logs.
- C. Add a try... catch block to your DoFn that transforms the data, use a sideOutput to create a PCollection that can be stored to PubSub later.
- D. Add a try... catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.
正解：D
質問 # 163
You need to choose a database for a new project that has the following requirements:
* Fully managed
* Able to automatically scale up
* Transactionally consistent
* Able to scale up to 6 TB
* Able to be queried using SQL
Which database do you choose?
- A. Cloud SQL
- B. Cloud Bigtable
- C. Cloud Spanner
- D. Cloud Datastore
正解：C
解説：
https://cloud.google.com/products/databases
質問 # 164
You need (o give new website users a globally unique identifier (GUID) using a service that takes in data points and returns a GUID This data is sourced from both internal and external systems via HTTP calls that you will make via microservices within your pipeline There will be tens of thousands of messages per second and that can be multithreaded, and you worry about the backpressure on the system How should you design your pipeline to minimize that backpressure?
- A. Create the pipeline statically in the class definition
- B. Create a new object in the startBundle method of DoFn
- C. Batch the job into ten-second increments
- D. Call out to the service via HTTP
正解：D
質問 # 165
When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a ____ proxy.
- A. HTTP
- B. HTTPS
- C. VPN
- D. SOCKS
正解：D
解説：
When using Cloud Dataproc clusters, configure your browser to use the SOCKS proxy. The SOCKS proxy routes data intended for the Cloud Dataproc cluster through an SSH tunnel.
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web- interfaces#interfaces
質問 # 166
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?
- A. Cloud Speech-to-Text API
- B. Cloud Natural Language API
- C. Cloud AutoML Natural Language
- D. Dialogflow Enterprise Edition
正解：D
質問 # 167
You are building new real-time data warehouse for your company and will use Google BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying dat
a. Which query type should you use?
- A. Include ORDER BY DESK on timestamp column and LIMIT to 1.
- B. Use the ROW_NUMBER window function with PARTITION by unique ID along with WHERE row equals 1.
- C. Use the LAG window function with PARTITION by unique ID along with WHERE LAG IS NOT NULL.
- D. Use GROUP BY on the unique ID column and timestamp column and SUM on the values.
正解：B
解説：
https://cloud.google.com/bigquery/docs/reference/standard-sql/analytic-function-concepts
質問 # 168
Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?
- A. Dimensionality Reduction
- B. Dropout Methods
- C. Threading
- D. Serialization
正解：B
解説：
Explanation/Reference: https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using- tensorflow-30505541d877
質問 # 169
How can you get a neural network to learn about relationships between categories in a categorical feature?
- A. Create a one-hot column
- B. Create an embedding column
- C. Create a hash bucket
- D. Create a multi-hot column
正解：B
解説：
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding column. The idea is that each category has a smaller vector with, let's say, 5 values in it.
But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too.
Reference: https://cloudacademy.com/google/introduction-to-google-cloud-machine-learning-engine-course/a-wide-and-deep-model.html
質問 # 170
Which of these statements about BigQuery caching is true?
- A. There is no charge for a query that retrieves its results from cache.
- B. BigQuery caches query results for 48 hours.
- C. By default, a query's results are not cached.
- D. Query results are cached even if you specify a destination table.
正解：A
解説：
Explanation
When query results are retrieved from a cached results table, you are not charged for the query.
BigQuery caches query results for 24 hours, not 48 hours.
Query results are not cached if you specify a destination table.
A query's results are always cached except under certain conditions, such as if you specify a destination table.
Reference: https://cloud.google.com/bigquery/querying-data#query-caching
質問 # 171
Given the record streams MJTelco is interested in ingesting per day, they are concerned about the cost of Google BigQuery increasing. MJTelco asks you to provide a design solution. They require a single large data table called tracking_table. Additionally, they want to minimize the cost of daily queries while performing fine-grained analysis of each day's events. They also want to use streaming ingestion. What should you do?
- A. Create a table called tracking_table and include a DATE column.
- B. Create a partitioned table called tracking_table and include a TIMESTAMP column.
- C. Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.
- D. Create a table called tracking_table with a TIMESTAMP column to represent the day.
正解：B
質問 # 172
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?
- A. Enable query caching so you can cache data from previous months
- B. Create separate views to cover each month, and query from these views
- C. Convert the sharded tables into a single partitioned table
- D. Convert all daily log tables into date-partitioned tables
正解：C
解説：
https://cloud.google.com/bigquery/docs/creating-partitioned-tables#converting_date- sharded_tables_into_ingestion-time_partitioned_tables
質問 # 173
You are designing a pipeline that publishes application events to a Pub/Sub topic. Although message ordering is not important, you need to be able to aggregate events across disjoint hourly intervals before loading the results to BigQuery for analysis. What technology should you use to process and load this data to BigQuery while ensuring that it will scale with large volumes of events?
- A. Schedule a Cloud Function to run hourly, pulling all available messages from the Pub/Sub topic and performing the necessary aggregations.
- B. Create a streaming Dataflow job that reads continually from the Pub/Sub topic and performs aggregations using tumbling windows.
- C. Schedule a batch Dataflow job to run hourly, pulling all available messages from the Pub/Sub topic and performing the necessary aggregations.
- D. Create a Cloud Function to perform the necessary data processing that executes using the Pub/Sub trigger every time a new message is published to the topic.
正解：D
解説：
Explanation/Reference:
質問 # 174
You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity 'Movie' the property 'actors' and the property 'tags' have multiple values but the property 'date released' does not. A typical query would ask for all movies with actor=<actorname> ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?
- A. Option A
- B. Option D
- C. Option C
- D. Option B.
正解：A
質問 # 175
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. Use a smaller set of features
- B. Decrease the regularization parameters
- C. Use a larger set of features
- D. Get more training examples
- E. Increase the regularization parameters
- F. Reduce the number of training examples
正解：B、C、D
質問 # 176
You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update. What should you do?
- A. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to the existing job name
- B. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to a new unique job name
- C. Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code
- D. Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code
正解：C
解説：
https://cloud.google.com/dataflow/docs/guides/updating-a-pipeline#changing_windowing
質問 # 177
You have some data, which is shown in the graphic below. The two dimensions are X and Y, and the shade of each dot represents what class it is. You want to classify this data accurately using a linear algorithm. To do this you need to add a synthetic feature. What should the value of that feature be?
- A. cos(X)
- B. X^2+Y^2
- C. Y^2
- D. X^2
正解：A
質問 # 178
You've migrated a Hadoop job from an on-premises cluster to Dataproc and Good Storage. Your Spark job is a complex analytical workload fiat consists of many shuffling operations, and initial data are parquet toes (on average 200-400 MB size each) You see some degradation in performance after the migration to Dataproc so you'd like to optimize for it. Your organization is very cost-sensitive so you'd Idee to continue using Dataproc on preemptibles (with 2 non-preemptibles workers only) for this workload. What should you do?
- A. Switch from HODs to SSDs override the preemptible VMs configuration to increase the boot disk size
- B. Switch from HDDs to SSDs. copy initial data from Cloud Storage to Hadoop Distributed File System (HDFS) run the Spark job and copy results back to Cloud Storage
- C. Increase the see of your parquet files to ensure them to be 1 GB minimum
- D. Switch to TFRecords format (appr 200 MB per We) instead of parquet files
正解：A
質問 # 179
You plan to deploy Cloud SQL using MySQL. You need to ensure high availability in the event of a zone failure. What should you do?
- A. Create a Cloud SQL instance in one zone, and configure an external read replica in a zone in a different region.
- B. Create a Cloud SQL instance in one zone, and create a read replica in another zone within the same region.
- C. Create a Cloud SQL instance in a region, and configure automatic backup to a Cloud Storage bucket in the same region.
- D. Create a Cloud SQL instance in one zone, and create a failover replica in another zone within the same region.
正解：D
解説：
https://cloud.google.com/sql/docs/mysql/high-availability
質問 # 180
You want to archive data in Cloud Storage. Because some data is very sensitive, you want to use the "Trust No One" (TNO) approach to encrypt your data to prevent the cloud provider staff from decrypting your data.
What should you do?
- A. Specify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in a different project that only the security team can access.
- B. Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key. Use gsutil cp to upload each encrypted file to the Cloud Storage bucket.
Manually destroy the key previously used for encryption, and rotate the key once. - C. Specify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in Cloud Memorystore as permanent storage of the secret.
- D. Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key and unique additional authenticated data (AAD). Use gsutil cp to upload each encrypted file to the Cloud Storage bucket, and keep the AAD outside of Google Cloud.
正解：B
質問 # 181
Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-to-date YouTube channels log data. How should you set up the log data transfer into Google Cloud?
- A. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi- Regional storage bucket as a final destination.
- B. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as a final destination.
- C. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.
- D. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional storage bucket as a final destination.
正解：A
質問 # 182
......
無料でゲット！高評価Google Professional-Data-Engineer試験問題集を今すぐダウンロード！：https://www.passtest.jp/Google/Professional-Data-Engineer-shiken.html
あなたを合格させるProfessional-Data-Engineer無料最新問題集でGoogle練習テスト：https://drive.google.com/open?id=1u5R4GxfkPXqb53oQvD6452YqPCteINhJ